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Communications",{"metadata":12161,"sys":12164,"fields":12174},{"tags":12162,"concepts":12163},[],[],{"space":12165,"id":12167,"type":14,"createdAt":12168,"updatedAt":12168,"environment":12169,"publishedVersion":901,"revision":1136,"contentType":12171,"locale":673},{"sys":12166},{"type":657,"linkType":658,"id":659},"3XLtL4MSYnYv2y9tAvOGnJ","2026-05-18T21:11:51.823Z",{"sys":12170},{"id":665,"type":657,"linkType":666},{"sys":12172},{"type":657,"linkType":671,"id":12173},"media",{"entryTitle":12175,"image":12176},"Feature: The End of “Bad I\u002FO”",{"metadata":12177,"sys":12180,"fields":12187},{"tags":12178,"concepts":12179},[],[],{"space":12181,"id":12183,"type":934,"createdAt":12184,"updatedAt":12184,"environment":12185,"publishedVersion":847,"revision":1136,"locale":673},{"sys":12182},{"type":657,"linkType":658,"id":659},"3QTHJdV2vgzwi2dx4xKV2b","2026-05-18T20:52:15.950Z",{"sys":12186},{"id":665,"type":657,"linkType":666},{"title":12156,"description":12156,"file":12188},{"url":12189,"details":12190,"fileName":12193,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3QTHJdV2vgzwi2dx4xKV2b\u002F089a4a09d595a4a9c4898d5a66778ccf\u002FCR-10228_BFI_End-of-Bad-IO_VAST-Data.jpg",{"size":12191,"image":12192},1948495,{"width":1144,"height":1145},"CR-10228_BFI_End-of-Bad-IO_VAST-Data.jpg",{"nodeType":1922,"data":12195,"content":12196},{},[12197,12204,12223,12230,12237,12266,12274,12281,12288,12312,12334,12346,12353,12360,12367,12386,12393,12400,12407,12414,12421,12438,12446,12540],{"nodeType":1921,"data":12198,"content":12199},{},[12200],{"nodeType":1907,"value":12201,"marks":12202,"data":12203},"For decades the HPC community has operated under a stern, almost puritanical doctrine: if your application exhibits \"bad I\u002FO,\" it is a failure of the researcher. The data infrastructure was a rigid temple. Users were expected to optimize their code - aligning blocks, smoothing out randomness, and buffering small writes - before they were allowed to touch the storage fabric. But as we move into the era of massive-scale AI, this paradigm is collapsing.",[],{},{"nodeType":1921,"data":12205,"content":12206},{},[12207,12211,12219],{"nodeType":1907,"value":12208,"marks":12209,"data":12210},"Users loading millions of small images and handling unpredictable metadata requests is now the standard. As VAST CTO Alon Horev and Field CTO Sven Breuner discussed during a ",[],{},{"nodeType":1916,"data":12212,"content":12214},{"uri":12213},"..\u002F..\u002Fresources\u002Fvast-forward-sessions\u002Fbest-practices-for-high-performance-io",[12215],{"nodeType":1907,"value":12216,"marks":12217,"data":12218},"recent conference session",[],{},{"nodeType":1907,"value":12220,"marks":12221,"data":12222},", the industry is shifting from a world where we optimize the application for the storage to a world where we must architect the storage to gracefully handle the chaos of the application.",[],{},{"nodeType":1921,"data":12224,"content":12225},{},[12226],{"nodeType":1907,"value":12227,"marks":12228,"data":12229},"According to Breuner (who also is the creator of BeeGFS and knows a thing or two about architecting high-performance file systems), it’s about accommodating the reality of modern research. \"We cannot assume that the users are super eager to spend a lot of time on optimizing their I\u002FOs. It’s unavoidable that there are bad I\u002FO patterns,” he said.",[],{},{"nodeType":1921,"data":12231,"content":12232},{},[12233],{"nodeType":1907,"value":12234,"marks":12235,"data":12236},"This shift has profound architectural implications. When \"bad\" patterns are the norm, the infrastructure must stop relying on the user’s ability to create sequential, large-block transfers. It requires a system that treats a four-kilobyte metadata request with the same urgency and efficiency as a multi-gigabyte stream.",[],{},{"nodeType":1921,"data":12238,"content":12239},{},[12240,12244,12252,12256,12262],{"nodeType":1907,"value":12241,"marks":12242,"data":12243},"VAST’s ",[],{},{"nodeType":1916,"data":12245,"content":12247},{"uri":12246},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fthe-future-of-hpc-storage-is-dase",[12248],{"nodeType":1907,"value":12249,"marks":12250,"data":12251},"Disaggregated and Shared Everything (DASE)",[],{},{"nodeType":1907,"value":12253,"marks":12254,"data":12255}," architecture, as Horev and Breuner explained, enables such systems by allowing any protocol server to handle any request ",[],{},{"nodeType":1907,"value":12257,"marks":12258,"data":12261},"without",[12259],{"type":12260},"italic",{},{"nodeType":1907,"value":12263,"marks":12264,"data":12265}," the cross-talk or metadata locking that traditionally turned small I\u002FO into a system-wide crawl.",[],{},{"nodeType":12267,"data":12268,"content":12269},"heading-4",{},[12270],{"nodeType":1907,"value":12271,"marks":12272,"data":12273},"\nThe Linux Page Cache: The Invisible Performance Engine",[],{},{"nodeType":1921,"data":12275,"content":12276},{},[12277],{"nodeType":1907,"value":12278,"marks":12279,"data":12280},"The modern data platform engineer might not be familiar with the Linux Page Cache, but as Horev explained, it functions as a high-speed application buffer that abstracts away the physics of the network. Every time an application opens a file for a standard read or write, it is performing buffered I\u002FO, which means it is interacting directly with local RAM, not the storage media.",[],{},{"nodeType":1921,"data":12282,"content":12283},{},[12284],{"nodeType":1907,"value":12285,"marks":12286,"data":12287},"Understanding these Page Cache mechanics is critical because it creates a massive performance gap between \"synthetic benchmarks\" and \"application reality” that transforms the user experience. While a benchmark using direct I\u002FO might be capped at 12,000 IOPS due to the round-trip latency of the network, an application using buffered I\u002FO can achieve over 600,000 IOPS because its data is already waiting in local memory.",[],{},{"nodeType":12289,"data":12290,"content":12311},"embedded-asset-block",{"target":12291},{"metadata":12292,"sys":12295,"fields":12302},{"tags":12293,"concepts":12294},[],[],{"space":12296,"id":12298,"type":934,"createdAt":12299,"updatedAt":12299,"environment":12300,"publishedVersion":692,"revision":1136,"locale":673},{"sys":12297},{"type":657,"linkType":658,"id":659},"4E2aBqvy7CbQ503wtrcmwD","2026-05-15T16:16:48.354Z",{"sys":12301},{"id":665,"type":657,"linkType":666},{"title":12303,"description":12156,"file":12304},"The End of “Bad I\u002FO” - Inline 1",{"url":12305,"details":12306,"fileName":12310,"contentType":4743},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4E2aBqvy7CbQ503wtrcmwD\u002F3d8798beb4f5159733f83444ef3822ff\u002FCR-10172_IG1_The_End_of_Bad_I-O_VAST-Data.png",{"size":12307,"image":12308},369463,{"width":1144,"height":12309},466,"CR-10172_IG1_The End of Bad I-O_VAST-Data.png",[],{"nodeType":12289,"data":12313,"content":12333},{"target":12314},{"metadata":12315,"sys":12318,"fields":12325},{"tags":12316,"concepts":12317},[],[],{"space":12319,"id":12321,"type":934,"createdAt":12322,"updatedAt":12322,"environment":12323,"publishedVersion":692,"revision":1136,"locale":673},{"sys":12320},{"type":657,"linkType":658,"id":659},"3oz5s00MTxltBqHJ34ueLv","2026-05-15T16:17:18.189Z",{"sys":12324},{"id":665,"type":657,"linkType":666},{"title":12326,"description":12156,"file":12327},"The End of “Bad I\u002FO” - Inline 2",{"url":12328,"details":12329,"fileName":12332,"contentType":4743},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3oz5s00MTxltBqHJ34ueLv\u002Fd37eb79286542e4c3a11c721bcc9e4a8\u002FCR-10172_IG2_The_End_of_Bad_I-O_VAST-Data.png",{"size":12330,"image":12331},396228,{"width":1144,"height":12309},"CR-10172_IG2_The End of Bad I-O_VAST-Data.png",[],{"nodeType":1921,"data":12335,"content":12336},{},[12337,12342],{"nodeType":1907,"value":12338,"marks":12339,"data":12341},"Direct I\u002FO is network-limited; buffered I\u002FO serves data at memory speed.",[12340],{"type":12260},{},{"nodeType":1907,"value":12343,"marks":12344,"data":12345}," ",[],{},{"nodeType":1921,"data":12347,"content":12348},{},[12349],{"nodeType":1907,"value":12350,"marks":12351,"data":12352},"While the Page Cache is \"always on,\" it isn't always optimized for high-performance scale-out architectures. Modern distributions often ship with a default read-ahead value of 128KB, a setting better suited for a Raspberry Pi than an AI training node, Horev suggested. VAST and the broader Linux community recommend cranking this value up to several megabytes (e.g., 16MB).",[],{},{"nodeType":1921,"data":12354,"content":12355},{},[12356],{"nodeType":1907,"value":12357,"marks":12358,"data":12359},"\"We have customers who saw over a 10x improvement just by increasing the read-ahead value,\" Breuner said. \"It allows the kernel to be much more aggressive in saturating the high-bandwidth links we have today.\"",[],{},{"nodeType":12267,"data":12361,"content":12362},{},[12363],{"nodeType":1907,"value":12364,"marks":12365,"data":12366},"\nToo Much GPU, Not Enough Data Path",[],{},{"nodeType":1921,"data":12368,"content":12369},{},[12370,12374,12382],{"nodeType":1907,"value":12371,"marks":12372,"data":12373},"In the hierarchy of performance, ",[],{},{"nodeType":1916,"data":12375,"content":12377},{"uri":12376},"..\u002F..\u002Fblog\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story",[12378],{"nodeType":1907,"value":12379,"marks":12380,"data":12381},"GPU Direct Storage (GDS)",[],{},{"nodeType":1907,"value":12383,"marks":12384,"data":12385}," represents an architectural \"shortcut\" on the client to move data from the NIC directly into GPU memory, based on RDMA. While standard buffered I\u002FO is the workhorse for most applications, GDS is the high-performance bypass used when the traditional CPU and system memory (SYSMEM) path becomes a hard bottleneck.",[],{},{"nodeType":1921,"data":12387,"content":12388},{},[12389],{"nodeType":1907,"value":12390,"marks":12391,"data":12392},"In other words, when your GPU wants to feed faster than the CPU can deliver it, that's where GPUDirect becomes highly useful. And perhaps the most compelling reason is the recovery of computational overhead. Without GDS, the GPU’s Streaming Multiprocessors - the core engines of AI compute - can stall simply managing the data transfer handshake.",[],{},{"nodeType":1921,"data":12394,"content":12395},{},[12396],{"nodeType":1907,"value":12397,"marks":12398,"data":12399},"\"If we're using the classic staging approach,” says Breuner, “then the streaming multiprocessors are basically stalled 18 percent of the time. With GDS, they are blocked 0 percent of the time because the network card is doing all of the work.\"",[],{},{"nodeType":1921,"data":12401,"content":12402},{},[12403],{"nodeType":1907,"value":12404,"marks":12405,"data":12406},"By offloading the data movement to the NIC, GDS ensures the GPU remains a pure compute engine rather than a data-moving clerk, effectively \"buying back\" nearly a fifth of your cluster's processing power.\n",[],{},{"nodeType":12267,"data":12408,"content":12409},{},[12410],{"nodeType":1907,"value":12411,"marks":12412,"data":12413},"A New Architectural Mandate",[],{},{"nodeType":1921,"data":12415,"content":12416},{},[12417],{"nodeType":1907,"value":12418,"marks":12419,"data":12420},"For senior leaders the question is no longer \"how fast is the storage?\" but rather \"how much compute can we unblock?\" Horev and Breuner believe organizations have the tools to build more efficient engines of discovery.",[],{},{"nodeType":1921,"data":12422,"content":12423},{},[12424,12427,12435],{"nodeType":1907,"value":2031,"marks":12425,"data":12426},[],{},{"nodeType":1916,"data":12428,"content":12430},{"uri":12429},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YImaRQMUPrc ",[12431],{"nodeType":1907,"value":12432,"marks":12433,"data":12434},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YImaRQMUPrc",[],{},{"nodeType":1907,"value":2031,"marks":12436,"data":12437},[],{},{"nodeType":1921,"data":12439,"content":12440},{},[12441],{"nodeType":1907,"value":12442,"marks":12443,"data":12445},"Learn more about VAST's philosophy and technical approach to ensuring sustained, real-world performance for the most demanding data-intensive 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again! The Network File System protocol is the grizzled remote data access veteran harking back to 1984 and has been a tried and tested way to access data from another server, while preserving the hierarchical file and directory semantics native to Unix and Linux. Being a Layer 7 protocol in the OSI stack, modern implementations of NFS use TCP (and RDMA) as the underlying transport across the network.",{"nodeType":1922,"data":12817,"content":12818},{},[12819,12826,12833,12840,12847,12865,12884,12891,12898,12906,12914,12933,12940,12948,12955,12977,12984,13005,13012,13019,13062,13080,13101,13109,13116,13133,13151,13158,13179,13187,13194,13206,13213,13229,13245,13263,13270,13277,13284,13305,13312,13343,13359,13366,13414,13442,13514,13548,13555,13562,13569,13578,13676],{"nodeType":12267,"data":12820,"content":12821},{},[12822],{"nodeType":1907,"value":12823,"marks":12824,"data":12825},"Achieve high bandwidth performance with NFS enhancements from VAST Data",[],{},{"nodeType":1921,"data":12827,"content":12828},{},[12829],{"nodeType":1907,"value":12830,"marks":12831,"data":12832},"What was old is new again! And again! I wrote this blog in Oct 2021 setting the stage for VAST to explode into the market. Fast forward to 2026, VAST has grown 300-400% a year to become a behemoth in the arena of AI and HPC Platforms, capturing the most NeoClouds, model-trainers, National Labs, higher-ed and other high-performance computing centers across the world.",[],{},{"nodeType":1921,"data":12834,"content":12835},{},[12836],{"nodeType":1907,"value":12837,"marks":12838,"data":12839},"VAST grew >100x in the past 4 years!! So it’s time for an update to understand why. Strap in for a fresh look at NFS - the changes we have made in leveraging the venerated NFS protocol are astounding.",[],{},{"nodeType":1921,"data":12841,"content":12842},{},[12843],{"nodeType":1907,"value":12844,"marks":12845,"data":12846},"The Network File System protocol is the grizzled remote data access veteran harking back to 1984 and has been a tried and tested way to access data from another server, while preserving the hierarchical file and directory semantics native to Unix and Linux. Being a Layer 7 protocol in the OSI stack, modern implementations of NFS can use TCP or RDMA as the underlying transport across the network.",[],{},{"nodeType":1921,"data":12848,"content":12849},{},[12850,12854,12862],{"nodeType":1907,"value":12851,"marks":12852,"data":12853},"In this article, we’ll examine some of the innovative work VAST Data has delivered to our customers for NFS - I will not distinguish much between NFS3 and NFS4 here, although massive additions have been made to NFS4 as well. A more detailed discussion of our implementation of NFS4 features can be found in ",[],{},{"nodeType":1916,"data":12855,"content":12857},{"uri":12856},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fimplementing-nfs4-with-vast-data",[12858],{"nodeType":1907,"value":12859,"marks":12860,"data":12861},"this blog post",[],{},{"nodeType":1907,"value":1920,"marks":12863,"data":12864},[],{},{"nodeType":1921,"data":12866,"content":12867},{},[12868,12872,12880],{"nodeType":1907,"value":12869,"marks":12870,"data":12871},"NFS is well defined in the standards community in ",[],{},{"nodeType":1916,"data":12873,"content":12875},{"uri":12874},"https:\u002F\u002Ftools.ietf.org\u002Fhtml\u002Frfc1813",[12876],{"nodeType":1907,"value":12877,"marks":12878,"data":12879},"RFC 1813",[],{},{"nodeType":1907,"value":12881,"marks":12882,"data":12883}," and while its availability is almost universal across operating systems and networks, it has never enjoyed a solid reputation for high bandwidth performance. I often hear customers say: “Isn’t NFS a very slow and unreliable file system”? My cheeky answer is always “NFS is not a file system, it’s a protocol - what matters is what you do at the endpoints.”. VAST implements NFS that is blazing fast, and rock-solid in reliability with crazy good linear scaling.",[],{},{"nodeType":12267,"data":12885,"content":12886},{},[12887],{"nodeType":1907,"value":12888,"marks":12889,"data":12890},"Enter VAST Data",[],{},{"nodeType":1921,"data":12892,"content":12893},{},[12894],{"nodeType":1907,"value":12895,"marks":12896,"data":12897},"We figured throwing out the baby with the bath water made no sense. Why not leverage the simplicity, solid install base, and standards-based implementation of NFS and improve the performance and availability to get the best of both worlds. The complexities of parallel file systems simply aren’t worth the hassle when NFS can be equally performant.",[],{},{"nodeType":1921,"data":12899,"content":12900},{},[12901],{"nodeType":1907,"value":12902,"marks":12903,"data":12905},"[Editorial rant: VAST Data is a software company. The VAST Data core architecture (DASE) maps a set of docker containers (with our code) to a set of NVMe devices. The form factor for what holds the containers or devices is flexible, and we have OEM relationships with a variety of providers. The front end containers can run on a server (CNode) , or in a Bluefield NIC, or in the same server with the NVMe devices (Ebox). We operate the same.",[12904],{"type":12260},{},{"nodeType":1921,"data":12907,"content":12908},{},[12909],{"nodeType":1907,"value":12910,"marks":12911,"data":12913},"So I groan when someone asks “how fast our box is” - clearly showing me that we have failed to accurately communicate how VAST Data works. VAST Data is not a box company. The correct answer is “as fast as you need” - we can build systems to match any requirements. We have production systems with 10’s of thousands of nodes, up to an exabyte in a single namespace, operating in excess of 10 TB\u002Fs (yep - big B=Byte). What we discuss below is independent of the form factor and will keep improving as HW becomes more capable.]",[12912],{"type":12260},{},{"nodeType":1921,"data":12915,"content":12916},{},[12917,12921,12929],{"nodeType":1907,"value":12918,"marks":12919,"data":12920},"This article examines the different methods of accessing data from a ",[],{},{"nodeType":1916,"data":12922,"content":12924},{"uri":12923},"https:\u002F\u002Fwww.vastdata.com\u002Fglossary\u002Fvast-cluster\u002F",[12925],{"nodeType":1907,"value":12926,"marks":12927,"data":12928},"VAST Cluster",[],{},{"nodeType":1907,"value":12930,"marks":12931,"data":12932}," using NFS that help you deliver high bandwidth performance, which are game-changing especially in your GPU accelerated environments.",[],{},{"nodeType":1921,"data":12934,"content":12935},{},[12936],{"nodeType":1907,"value":12937,"marks":12938,"data":12939},"VAST Data supports 4 different modes of NFS, and the same client can use any combination of these at the same time for different mount points. They differ in the underlying transport protocol (TCP vs RDMA) and the introduction of new features in the upstream Linux kernel for NFS around multiple TCP connections between the client and storage system.",[],{},{"nodeType":12267,"data":12941,"content":12942},{},[12943],{"nodeType":1907,"value":12944,"marks":12945,"data":12947},"NFS\u002FTCP",[12946],{"type":12260},{},{"nodeType":1921,"data":12949,"content":12950},{},[12951],{"nodeType":1907,"value":12952,"marks":12953,"data":12954},"This is the existing standards based implementation present in all Linux kernels. This sets up one TCP connection between the client port and one storage port, and uses TCP as the transport for both metadata and data operations.",[],{},{"nodeType":12956,"data":12957,"content":12976},"embedded-entry-block",{"target":12958},{"metadata":12959,"sys":12962,"fields":12972},{"tags":12960,"concepts":12961},[],[],{"space":12963,"id":12965,"type":14,"createdAt":12966,"updatedAt":12966,"environment":12967,"publishedVersion":847,"revision":1136,"contentType":12969,"locale":673},{"sys":12964},{"type":657,"linkType":658,"id":659},"5qms2YLiLIMUA1KeNq9cKL","2026-02-12T23:29:37.404Z",{"sys":12968},{"id":665,"type":657,"linkType":666},{"sys":12970},{"type":657,"linkType":671,"id":12971},"codeBlock",{"entryTitle":12944,"code":12973,"language":12974,"theme":12975},"# This is an example mount commands (1 local ports to remote ports):\n\nsudo mount -o proto=tcp,vers=3 172.25.1.1:\u002F \u002Fmnt\u002Ftcp\n\n# This is standards based syntax for NFS\u002FTCP - the proto=tcp is default.","bash","vast-data-dark",[],{"nodeType":1921,"data":12978,"content":12979},{},[12980],{"nodeType":1907,"value":12981,"marks":12982,"data":12983},"While the easiest to use and requiring no installation on non-standard components, this is also the least performant option. Typically we see about 2- 3 GB\u002Fs per mount point for this with large block IOs. 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Here the connection topology is still the same - single connection between one client port and one storage port - however the data transfer occurs using RDMA thus increasing the throughput. The use of RDMA bypasses the TCP socket limitations. This implementation requires:",[],{},{"nodeType":12447,"data":13020,"content":13021},{},[13022,13032,13042,13052],{"nodeType":12451,"data":13023,"content":13024},{},[13025],{"nodeType":1921,"data":13026,"content":13027},{},[13028],{"nodeType":1907,"value":13029,"marks":13030,"data":13031},"an RDMA-capable NIC e.g. NVIDIA ConnectX or Bluefield",[],{},{"nodeType":12451,"data":13033,"content":13034},{},[13035],{"nodeType":1921,"data":13036,"content":13037},{},[13038],{"nodeType":1907,"value":13039,"marks":13040,"data":13041},"Jumbo frame support in the RDMA network",[],{},{"nodeType":12451,"data":13043,"content":13044},{},[13045],{"nodeType":1921,"data":13046,"content":13047},{},[13048],{"nodeType":1907,"value":13049,"marks":13050,"data":13051},"an OFED from NVIDIA (Mellanox OFED or the DOCA OFED for Bluefield NICs).",[],{},{"nodeType":12451,"data":13053,"content":13054},{},[13055],{"nodeType":1921,"data":13056,"content":13057},{},[13058],{"nodeType":1907,"value":13059,"marks":13060,"data":13061},"Modern linux kernels have “in-box” drivers for RDMA, but VAST can also provide an enhanced version for RDMA capabilities that enables other capabilities as well. VAST server side support for this is available on both x86 and ARM CPUs, for IB and Ethernet (RoCEv2) and multiple linux distributions (Fedora & Debian forks), {D,M}OFEDs, and linux kernels spanning over 10+ years.",[],{},{"nodeType":12956,"data":13063,"content":13079},{"target":13064},{"metadata":13065,"sys":13068,"fields":13077},{"tags":13066,"concepts":13067},[],[],{"space":13069,"id":13071,"type":14,"createdAt":13072,"updatedAt":13072,"environment":13073,"publishedVersion":712,"revision":1136,"contentType":13075,"locale":673},{"sys":13070},{"type":657,"linkType":658,"id":659},"513EWK4HKkUtkZOiGypQBg","2026-02-12T23:32:45.569Z",{"sys":13074},{"id":665,"type":657,"linkType":666},{"sys":13076},{"type":657,"linkType":671,"id":12971},{"entryTitle":13009,"code":13078,"language":12974,"theme":12975},"# This is an example mount for NFS\u002FRDMA command (1 local ports to fremote ports):\n\nsudo mount -o proto=rdma,port=20049, vers=3 172.25.1.1:\u002F \u002Fmnt\u002Frdma\n\n# This is standards based syntax for NFS\u002FRDMA. Port 20049 is also standard for NFS\u002FRDMA and is implemented in VAST\n\nWith NFS\u002FRDMA, we are able to achieve 8-8.5 GB\u002Fs per mount point with large IOs (1 MB), while IOPS remains unchanged relative to the standard NFS over TCP option.",[],{"nodeType":12289,"data":13081,"content":13100},{"target":13082},{"metadata":13083,"sys":13086,"fields":13093},{"tags":13084,"concepts":13085},[],[],{"space":13087,"id":13089,"type":934,"createdAt":13090,"updatedAt":13090,"environment":13091,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13088},{"type":657,"linkType":658,"id":659},"5aFNbZV77PMss0mYS4TlDr","2023-09-06T18:08:00.929Z",{"sys":13092},{"id":665,"type":657,"linkType":666},{"title":12786,"description":12786,"file":13094},{"url":13095,"details":13096,"fileName":13099,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5aFNbZV77PMss0mYS4TlDr\u002Fdb28c08170c6629d4687d1160c83facc\u002Fmeet-your-need-for-speed-with-nfs-2.webp",{"size":13097,"image":13098},143902,{"width":1144,"height":1145},"meet-your-need-for-speed-with-nfs-2.webp",[],{"nodeType":12267,"data":13102,"content":13103},{},[13104],{"nodeType":1907,"value":13105,"marks":13106,"data":13108},"NFS\u002FTCP (or RDMA) with nconnect:",[13107],{"type":12260},{},{"nodeType":1921,"data":13110,"content":13111},{},[13112],{"nodeType":1907,"value":13113,"marks":13114,"data":13115},"This is an upstream kernel feature but exclusive to Linux kernels after 5.3. This requires no specialized hardware (no RDMA, NICs,...but can work with RDMA NICs as well) and works “out-of-the-box”. Here the NFS driver allows for multiple connections between client and one storage port - controlled by the nconnect mount parameter. Up to 16 connections can be created between the client and storage port with nconnect in the upstream kernel (VAST provides a client that supports up to 64).",[],{},{"nodeType":1921,"data":13117,"content":13118},{},[13119,13123,13129],{"nodeType":1907,"value":13120,"marks":13121,"data":13122},"The transport protocol is TCP or RDMA under NFS, and using nconnect allows for multiple connections across multiple cores in the client CPUs. I should emphasize that ",[],{},{"nodeType":1907,"value":13124,"marks":13125,"data":13128},"these are NOT dedicated cores",[13126],{"type":13127},"underline",{},{"nodeType":1907,"value":13130,"marks":13131,"data":13132}," (which some vendors require for good performance with their native DMA clients) and the overall CPU overhead is extremely low (low single digit percentage) even at full throttle.",[],{},{"nodeType":12956,"data":13134,"content":13150},{"target":13135},{"metadata":13136,"sys":13139,"fields":13148},{"tags":13137,"concepts":13138},[],[],{"space":13140,"id":13142,"type":14,"createdAt":13143,"updatedAt":13143,"environment":13144,"publishedVersion":847,"revision":1136,"contentType":13146,"locale":673},{"sys":13141},{"type":657,"linkType":658,"id":659},"5qhJojN1wspoMHW0WGzYgF","2026-02-12T23:35:00.510Z",{"sys":13145},{"id":665,"type":657,"linkType":666},{"sys":13147},{"type":657,"linkType":671,"id":12971},{"entryTitle":13105,"code":13149,"language":12974,"theme":12975},"# This is an example mount command for kernel 5.3+ nconnect NFS\u002FTCP (1 local ports to 1 remote ports):\n\nsudo mount -o proto=tcp, vers=3, nconnect=8 172.25.1.1:\u002F \u002Fmnt\u002Fnconnect\n\n# This is standards based syntax for NFS\u002FTCP. Note that nconnect is limited to 16.\n\n# Once again, the proto=tcp is default - the command will simply not work for the wrong kernels.\n\n# The default port is specified as 20048 for nconnect and is implicit - use the \"-v\" flag for details if curious",[],{"nodeType":1921,"data":13152,"content":13153},{},[13154],{"nodeType":1907,"value":13155,"marks":13156,"data":13157},"The upstream kernel nconnect feature can provide close to line bandwidth for a single 100 Gb NIC. For example, we can achieve 11 GB\u002Fs on a single mount point with this on a Mellanox ConnectX-5 100 Gb NIC. These days, we have NVIDIA CX-7 NICs capable of 200 Gbps\u002Fport on the front-end Cnodes, so even higher speeds are possible with just nconnect using higher speed client NICs.",[],{},{"nodeType":12289,"data":13159,"content":13178},{"target":13160},{"metadata":13161,"sys":13164,"fields":13171},{"tags":13162,"concepts":13163},[],[],{"space":13165,"id":13167,"type":934,"createdAt":13168,"updatedAt":13168,"environment":13169,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13166},{"type":657,"linkType":658,"id":659},"KXtUQBeQDnvgvAyMEZkyU","2023-09-06T18:08:36.620Z",{"sys":13170},{"id":665,"type":657,"linkType":666},{"title":12786,"description":12786,"file":13172},{"url":13173,"details":13174,"fileName":13177,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FKXtUQBeQDnvgvAyMEZkyU\u002F541c8fa5e5453d6898cd6d2106611e99\u002Fmeet-your-need-for-speed-with-nfs-3.webp",{"size":13175,"image":13176},148442,{"width":1144,"height":1145},"meet-your-need-for-speed-with-nfs-3.webp",[],{"nodeType":12267,"data":13180,"content":13181},{},[13182],{"nodeType":1907,"value":13183,"marks":13184,"data":13186},"Multipath NFS\u002FRDMA or NFS\u002FTCP:",[13185],{"type":12260},{},{"nodeType":1921,"data":13188,"content":13189},{},[13190],{"nodeType":1907,"value":13191,"marks":13192,"data":13193},"By now, the astute reader would have figured out that all the three options we have discussed have a common topological constraint - each client NIC port can only talk to one server NIC port, thus limiting overall performance to a mount point to what a server NIC can deliver.",[],{},{"nodeType":1921,"data":13195,"content":13196},{},[13197,13202],{"nodeType":1907,"value":13198,"marks":13199,"data":13201},"What if ALL the client NIC ports on a node could talk to ALL the server NIC ports?",[13200],{"type":13127},{},{"nodeType":1907,"value":13203,"marks":13204,"data":13205}," Magic! Every front-end node in a VAST Cluster can access the same namespace, after all, so IOs can be issued to any node. Theoretically (and in practice) we can line saturate every set of client NICs and therefore deliver full line speed to a single mount point on the client, long as the VAST cluster is sized to deliver this.",[],{},{"nodeType":1921,"data":13207,"content":13208},{},[13209],{"nodeType":1907,"value":13210,"marks":13211,"data":13212},"This core ability is what we refer to as “multipath”. And people thought (and many will still insist) that NFS was “slow” and that a parallel FS was needed to do this, with proprietary DMA clients! VAST NFS has irreversibly smashed that misconception. This was the breakthrough (5 years ago) that made VAST the first NFS server to pass SuperPOD certification. More on this later.",[],{},{"nodeType":1921,"data":13214,"content":13215},{},[13216,13220,13225],{"nodeType":1907,"value":13217,"marks":13218,"data":13219},"First, ",[],{},{"nodeType":1907,"value":13221,"marks":13222,"data":13224},"nconnect",[13223],{"type":12260},{},{"nodeType":1907,"value":13226,"marks":13227,"data":13228}," here provides the ability to have multiple connections between the client and the storage – however, the connections are no longer limited to a single storage port, but can connect with any number of storage ports that can serve that NFS filesystem. Load balancing and HA capabilities are built-in into this feature as well, and for complex systems with multiple client NICs and PCIe switches, NIC affinity is implemented to ensure optimal connectivity inside the server.",[],{},{"nodeType":1921,"data":13230,"content":13231},{},[13232,13236,13241],{"nodeType":1907,"value":13233,"marks":13234,"data":13235},"A key differentiator with Multipath is that the",[],{},{"nodeType":1907,"value":13237,"marks":13238,"data":13240}," nconnect",[13239],{"type":12260},{},{"nodeType":1907,"value":13242,"marks":13243,"data":13244}," feature is no longer restricted to the 5.3+ kernels, but has been backported to lower kernels (3.x and 4.x) as well, making these powerful features available to a broad ecosystem of deployments. Typical mount semantics differ from normal NFS mounts in a few ways. See example below.",[],{},{"nodeType":12956,"data":13246,"content":13262},{"target":13247},{"metadata":13248,"sys":13251,"fields":13260},{"tags":13249,"concepts":13250},[],[],{"space":13252,"id":13254,"type":14,"createdAt":13255,"updatedAt":13255,"environment":13256,"publishedVersion":847,"revision":1136,"contentType":13258,"locale":673},{"sys":13253},{"type":657,"linkType":658,"id":659},"5LTxnpHSQ7f4UJdhjDdGGg","2026-02-12T23:38:04.769Z",{"sys":13257},{"id":665,"type":657,"linkType":666},{"sys":13259},{"type":657,"linkType":671,"id":12971},{"entryTitle":13183,"code":13261,"language":12974,"theme":12975},"# This is an example mount commands (4 local ports to 8 remote ports): sudo mount -o proto=rdma,port=20049, vers=3, nconnect=8, localports=172.25.1.101- 172.25.1.104, remoteports=172.25.1.1-172.25.1.8 172.25.1.1:\u002F \u002Fmnt\u002Fmultipath\n\n# The code changes in this repository add the following parameters localports remoteports A list of IPv4 addresses for the local ports to bind A list of IPv4 addresses for the remote ports to bind\n\nIP addresses can be given as an inclusive range, with '-' as a delimiter, e.g. 'FIRST-LAST'. Multiple ranges or IP addresses can be separated by '~'.",[],{"nodeType":1921,"data":13264,"content":13265},{},[13266],{"nodeType":1907,"value":13267,"marks":13268,"data":13269},"The performance we were able to achieve for a single-client single-mount, 5 years ago, far exceeded any other approach, allowing us to pass NVIDIA BasePOD and SuperPOD reference architecture certifications easily. We saw up to 162 GiB\u002Fs (174 GB\u002Fs) on systems with 8x200 Gbps NICs with GPU Direct Storage, with a single client DGX-A100 System.",[],{},{"nodeType":1921,"data":13271,"content":13272},{},[13273],{"nodeType":1907,"value":13274,"marks":13275,"data":13276},"Today with much more powerful servers and client NICs, this is a moving target. However, in every case we have tested we have been able to line saturate the connectivity to the client on a single mount point.",[],{},{"nodeType":1921,"data":13278,"content":13279},{},[13280],{"nodeType":1907,"value":13281,"marks":13282,"data":13283},"Additionally, as all the C-nodes participate to deliver IOPS, an entry level 4 C-node system (with the latest AMD Turin CNodes) is able to deliver over 1.1 Million 4K IOPS to a single client\u002Fsingle mount point\u002Fsingle client - again a moving target but as new processors advance, so do we. We are designed to scale this performance linearly as more C-nodes participate.",[],{},{"nodeType":12289,"data":13285,"content":13304},{"target":13286},{"metadata":13287,"sys":13290,"fields":13297},{"tags":13288,"concepts":13289},[],[],{"space":13291,"id":13293,"type":934,"createdAt":13294,"updatedAt":13294,"environment":13295,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13292},{"type":657,"linkType":658,"id":659},"4iWAhcixocrdpU9K5VVS2g","2023-09-06T18:09:10.753Z",{"sys":13296},{"id":665,"type":657,"linkType":666},{"title":12786,"description":12786,"file":13298},{"url":13299,"details":13300,"fileName":13303,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4iWAhcixocrdpU9K5VVS2g\u002F4c562e4a300af6d4bf6dafbd48048125\u002Fmeet-your-need-for-speed-with-nfs-4.webp",{"size":13301,"image":13302},166338,{"width":1144,"height":1145},"meet-your-need-for-speed-with-nfs-4.webp",[],{"nodeType":12267,"data":13306,"content":13307},{},[13308],{"nodeType":1907,"value":13309,"marks":13310,"data":13311},"The VAST NFS Client",[],{},{"nodeType":1921,"data":13313,"content":13314},{},[13315,13319,13327,13331,13339],{"nodeType":1907,"value":13316,"marks":13317,"data":13318},"All the above just highlights the performance gains for IOPS and Bandwidth - this was just the first step in our journey to enhance NFS in an open fashion for the world. The implementation of the VAST NFS Client that makes multipath NFS and all its advanced features is worth examining now.\n\nThe reader is urged to take a look at the documentation for the ",[],{},{"nodeType":1916,"data":13320,"content":13322},{"uri":13321},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002Findex.html",[13323],{"nodeType":1907,"value":13324,"marks":13325,"data":13326},"VAST NFS Client",[],{},{"nodeType":1907,"value":13328,"marks":13329,"data":13330}," to learn more details, but lets discuss some of the very cool things the VAST R&D teams have developed.\n\nSome crucial facts about teh VAST NFS Client: The code is 100% open-source, free and is not tied to the VAST AI OS at all, unlike a native DMA client. So much so that ",[],{},{"nodeType":1916,"data":13332,"content":13334},{"uri":13333},"http:\u002F\u002Fwww.unstructureddatatips.com\u002Fpowerscale-multipath-client-driver-pre-built-package-installation\u002F",[13335],{"nodeType":1907,"value":13336,"marks":13337,"data":13338},"other vendors",[],{},{"nodeType":1907,"value":13340,"marks":13341,"data":13342}," have now started to use it to get around limitations of their NFS implementations to achieve their own SuperPOD certifications.",[],{},{"nodeType":1921,"data":13344,"content":13345},{},[13346,13350,13355],{"nodeType":1907,"value":13347,"marks":13348,"data":13349},"They say ",[],{},{"nodeType":1907,"value":13351,"marks":13352,"data":13354},"imitation is the best form of flattery, ",[13353],{"type":13127},{},{"nodeType":1907,"value":13356,"marks":13357,"data":13358},"and we welcome it! This was the intent behind open sourcing the code, so we could make NFS better for everyone, not just our customers. We have contributed several sections to the upstream Linux community as well.",[],{},{"nodeType":1921,"data":13360,"content":13361},{},[13362],{"nodeType":1907,"value":13363,"marks":13364,"data":13365},"Some salient features:",[],{},{"nodeType":12447,"data":13367,"content":13368},{},[13369,13379,13389,13399],{"nodeType":12451,"data":13370,"content":13371},{},[13372],{"nodeType":1921,"data":13373,"content":13374},{},[13375],{"nodeType":1907,"value":13376,"marks":13377,"data":13378},"Client support for Intel (x86-64), AArch64 (ARM), RISC-V, POWER, S390x and more",[],{},{"nodeType":12451,"data":13380,"content":13381},{},[13382],{"nodeType":1921,"data":13383,"content":13384},{},[13385],{"nodeType":1907,"value":13386,"marks":13387,"data":13388},"Support for both NFS3 and NFS4 (later kernels only)",[],{},{"nodeType":12451,"data":13390,"content":13391},{},[13392],{"nodeType":1921,"data":13393,"content":13394},{},[13395],{"nodeType":1907,"value":13396,"marks":13397,"data":13398},"Support for NFS with TLS for RHEL 9\u002FRocky 9 and Ubuntu 24.04",[],{},{"nodeType":12451,"data":13400,"content":13401},{},[13402],{"nodeType":1921,"data":13403,"content":13404},{},[13405,13410],{"nodeType":1907,"value":13406,"marks":13407,"data":13409},"vastnfs-ctl ",[13408],{"type":12260},{},{"nodeType":1907,"value":13411,"marks":13412,"data":13413},"utility for providing a simple control plane for the client installation, monitoring and configuration.",[],{},{"nodeType":1921,"data":13415,"content":13416},{},[13417,13421,13426,13430,13438],{"nodeType":1907,"value":13418,"marks":13419,"data":13420},"Many new mount options have been developed beyond the ",[],{},{"nodeType":1907,"value":13422,"marks":13423,"data":13425},"localports\u002Fremoteports",[13424],{"type":12260},{},{"nodeType":1907,"value":13427,"marks":13428,"data":13429}," I have written about. Some of the more important ones (see ",[],{},{"nodeType":1916,"data":13431,"content":13433},{"uri":13432},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002Fusage\u002Fmount-params.html",[13434],{"nodeType":1907,"value":13435,"marks":13436,"data":13437},"details",[],{},{"nodeType":1907,"value":13439,"marks":13440,"data":13441},"):",[],{},{"nodeType":12447,"data":13443,"content":13444},{},[13445,13460,13475,13499],{"nodeType":12451,"data":13446,"content":13447},{},[13448],{"nodeType":1921,"data":13449,"content":13450},{},[13451,13456],{"nodeType":1907,"value":13452,"marks":13453,"data":13455},"forcerdirplus",[13454],{"type":12260},{},{"nodeType":1907,"value":13457,"marks":13458,"data":13459},": This option issues READDIRPLUS metadata calls which are advantageous over READDIR in directory listing use cases",[],{},{"nodeType":12451,"data":13461,"content":13462},{},[13463],{"nodeType":1921,"data":13464,"content":13465},{},[13466,13471],{"nodeType":1907,"value":13467,"marks":13468,"data":13470},"noextend:",[13469],{"type":12260},{},{"nodeType":1907,"value":13472,"marks":13473,"data":13474}," Turns off the default extend-to-page optimization in writes, which allows for more efficient write IOs - particularly useful for HPC MPI-IO codes.",[],{},{"nodeType":12451,"data":13476,"content":13477},{},[13478],{"nodeType":1921,"data":13479,"content":13480},{},[13481,13486,13490,13495],{"nodeType":1907,"value":13482,"marks":13483,"data":13485},"spread_reads",[13484],{"type":12260},{},{"nodeType":1907,"value":13487,"marks":13488,"data":13489}," and ",[],{},{"nodeType":1907,"value":13491,"marks":13492,"data":13494},"spread_writes",[13493],{"type":12260},{},{"nodeType":1907,"value":13496,"marks":13497,"data":13498},": Controls whether a single file IO should use a single or multiple connections. Using this is often very useful to balance all the front-end IO from multiple clients evenly across all the cnode ports on the cluster.",[],{},{"nodeType":12451,"data":13500,"content":13501},{},[13502],{"nodeType":1921,"data":13503,"content":13504},{},[13505,13510],{"nodeType":1907,"value":13506,"marks":13507,"data":13509},"relmtime",[13508],{"type":12260},{},{"nodeType":1907,"value":13511,"marks":13512,"data":13513},": Ensures that stat() calls are not blocked wanting for pending writes.",[],{},{"nodeType":1921,"data":13515,"content":13516},{},[13517,13521,13526,13530,13535,13539,13544],{"nodeType":1907,"value":13518,"marks":13519,"data":13520},"There are many more options to study and experiment with. These improve not just IO performance but metadata performance as well, extending the performance gains that many applications need. Many HA features (such as ",[],{},{"nodeType":1907,"value":13522,"marks":13523,"data":13525},"noidlexprt",[13524],{"type":12260},{},{"nodeType":1907,"value":13527,"marks":13528,"data":13529},", ",[],{},{"nodeType":1907,"value":13531,"marks":13532,"data":13534},"localports_failover",[13533],{"type":12260},{},{"nodeType":1907,"value":13536,"marks":13537,"data":13538}," etc) as well as usability features (such as being able to specify l",[],{},{"nodeType":1907,"value":13540,"marks":13541,"data":13543},"ocalports ",[13542],{"type":12260},{},{"nodeType":1907,"value":13545,"marks":13546,"data":13547},"using interface names instead of IP addresses - super useful in large GPU cluster automation) have been added as well to enhance stability and ease-of-use.",[],{},{"nodeType":12267,"data":13549,"content":13550},{},[13551],{"nodeType":1907,"value":13552,"marks":13553,"data":13554},"Conclusion",[],{},{"nodeType":1921,"data":13556,"content":13557},{},[13558],{"nodeType":1907,"value":13559,"marks":13560,"data":13561},"These connectivity options for NFS are powerful methods to access data over a standards based protocol, NFS. The modern variants, and the innovation that VAST Data has brought to the forefront, has changed the landscape of what NFS is capable of.",[],{},{"nodeType":1921,"data":13563,"content":13564},{},[13565],{"nodeType":1907,"value":13566,"marks":13567,"data":13568},"All these approaches are available with the VAST Data AI OS. Some are standard, some have kernel limitations, and some need RDMA support with some (free) software from VAST Data. VAST Data is constantly working with NFS upstream maintainers to contribute our code to the Linux kernel.",[],{},{"nodeType":1921,"data":13570,"content":13571},{},[13572],{"nodeType":1907,"value":13573,"marks":13574,"data":13577},"General References",[13575],{"type":13576},"bold",{},{"nodeType":12447,"data":13579,"content":13580},{},[13581,13600,13619,13638,13657],{"nodeType":12451,"data":13582,"content":13583},{},[13584],{"nodeType":1921,"data":13585,"content":13586},{},[13587,13590,13597],{"nodeType":1907,"value":2031,"marks":13588,"data":13589},[],{},{"nodeType":1916,"data":13591,"content":13593},{"uri":13592},"https:\u002F\u002Fwww.kernel.org\u002Fdoc\u002FDocumentation\u002Ffilesystems\u002Fnfs\u002Fnfs-rdma.txt",[13594],{"nodeType":1907,"value":13592,"marks":13595,"data":13596},[],{},{"nodeType":1907,"value":2031,"marks":13598,"data":13599},[],{},{"nodeType":12451,"data":13601,"content":13602},{},[13603],{"nodeType":1921,"data":13604,"content":13605},{},[13606,13609,13616],{"nodeType":1907,"value":2031,"marks":13607,"data":13608},[],{},{"nodeType":1916,"data":13610,"content":13612},{"uri":13611},"http:\u002F\u002Fnfs.sourceforge.net\u002Fnfs-howto\u002Far01s05.html",[13613],{"nodeType":1907,"value":13611,"marks":13614,"data":13615},[],{},{"nodeType":1907,"value":2031,"marks":13617,"data":13618},[],{},{"nodeType":12451,"data":13620,"content":13621},{},[13622],{"nodeType":1921,"data":13623,"content":13624},{},[13625,13628,13635],{"nodeType":1907,"value":2031,"marks":13626,"data":13627},[],{},{"nodeType":1916,"data":13629,"content":13631},{"uri":13630},"http:\u002F\u002Fwww.slashroot.in\u002Fhow-do-linux-nfs-performance-tuning-and-optimization",[13632],{"nodeType":1907,"value":13630,"marks":13633,"data":13634},[],{},{"nodeType":1907,"value":2031,"marks":13636,"data":13637},[],{},{"nodeType":12451,"data":13639,"content":13640},{},[13641],{"nodeType":1921,"data":13642,"content":13643},{},[13644,13647,13654],{"nodeType":1907,"value":2031,"marks":13645,"data":13646},[],{},{"nodeType":1916,"data":13648,"content":13650},{"uri":13649},"https:\u002F\u002Faccess.redhat.com\u002Fdocumentation\u002Fen-US\u002FRed_Hat_Enterprise_Linux\u002F6\u002Fhtml\u002FStorage_Administration_Guide\u002Fnfs-rdma.html",[13651],{"nodeType":1907,"value":13649,"marks":13652,"data":13653},[],{},{"nodeType":1907,"value":2031,"marks":13655,"data":13656},[],{},{"nodeType":12451,"data":13658,"content":13659},{},[13660],{"nodeType":1921,"data":13661,"content":13662},{},[13663,13666,13673],{"nodeType":1907,"value":2031,"marks":13664,"data":13665},[],{},{"nodeType":1916,"data":13667,"content":13669},{"uri":13668},"http:\u002F\u002Fwiki.linux-nfs.org\u002Fwiki\u002Findex.php\u002FMain_Page",[13670],{"nodeType":1907,"value":13668,"marks":13671,"data":13672},[],{},{"nodeType":1907,"value":2031,"marks":13674,"data":13675},[],{},{"nodeType":1921,"data":13677,"content":13678},{},[13679],{"nodeType":1907,"value":2031,"marks":13680,"data":13681},[],{},[13683,14366,14938],{"metadata":13684,"sys":13716,"fields":13727},{"tags":13685,"concepts":13715},[13686,13689,13692,13694,13697,13700,13702,13705,13707,13709,13712],{"sys":13687},{"type":657,"linkType":1502,"id":13688},"ai",{"sys":13690},{"type":657,"linkType":1502,"id":13691},"customer",{"sys":13693},{"type":657,"linkType":1502,"id":12740},{"sys":13695},{"type":657,"linkType":1502,"id":13696},"industryHigherEducationResearch",{"sys":13698},{"type":657,"linkType":1502,"id":13699},"locationAmer",{"sys":13701},{"type":657,"linkType":1502,"id":12743},{"sys":13703},{"type":657,"linkType":1502,"id":13704},"personaChampion",{"sys":13706},{"type":657,"linkType":1502,"id":12749},{"sys":13708},{"type":657,"linkType":1502,"id":12752},{"sys":13710},{"type":657,"linkType":1502,"id":13711},"productDataStore",{"sys":13713},{"type":657,"linkType":1502,"id":13714},"useCaseHpc",[],{"space":13717,"id":13719,"type":14,"createdAt":13720,"updatedAt":13721,"environment":13722,"publishedVersion":13724,"revision":1117,"contentType":13725,"locale":673},{"sys":13718},{"type":657,"linkType":658,"id":659},"6eNcchf7hIAacdtFkzAfwR","2023-07-21T19:15:21.343Z","2026-03-26T19:38:01.363Z",{"sys":13723},{"id":665,"type":657,"linkType":666},168,{"sys":13726},{"type":657,"linkType":671,"id":12765},{"title":13728,"slug":13729,"publishedDate":13730,"author":13731,"thumbnailImage":13732,"middleContent":13765,"socialIcons":14152,"hidden":19,"companyName":14278,"companyLogo":14279,"companyMedia":14297,"seo":14331},"The Launchpad to Exascale: The Story of VAST @ TACC","the-launchpad-to-exascale-the-story-of-vast-at-tacc","2023-07-25","Jeff Denworth",{"metadata":13733,"sys":13736,"fields":13745},{"tags":13734,"concepts":13735},[],[],{"space":13737,"id":13739,"type":14,"createdAt":13740,"updatedAt":13740,"environment":13741,"publishedVersion":901,"revision":1136,"contentType":13743,"locale":673},{"sys":13738},{"type":657,"linkType":658,"id":659},"27SphWS84okW1MLAGv3cNq","2023-07-21T19:11:19.923Z",{"sys":13742},{"id":665,"type":657,"linkType":666},{"sys":13744},{"type":657,"linkType":671,"id":12173},{"entryTitle":13728,"image":13746},{"metadata":13747,"sys":13750,"fields":13758},{"tags":13748,"concepts":13749},[],[],{"space":13751,"id":13753,"type":934,"createdAt":13754,"updatedAt":13755,"environment":13756,"publishedVersion":865,"revision":901,"locale":673},{"sys":13752},{"type":657,"linkType":658,"id":659},"1rhq3pdpSpQ0kzUk1ugf3Z","2023-07-21T18:56:21.135Z","2023-07-21T22:59:02.319Z",{"sys":13757},{"id":665,"type":657,"linkType":666},{"title":13728,"description":13728,"file":13759},{"url":13760,"details":13761,"fileName":13764,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1rhq3pdpSpQ0kzUk1ugf3Z\u002Ff4d0f1201f71d8abab1d3cf4a0c61283\u002Fthe-launchpad-to-exascale-the-story-of-vast-at-tacc.webp",{"size":13762,"image":13763},128906,{"width":1144,"height":1145},"the-launchpad-to-exascale-the-story-of-vast-at-tacc.webp",{"nodeType":1922,"data":13766,"content":13767},{},[13768,13786,13807,13826,13833,13852,13875,13882,13889,13905,13912,13919,13926,13933,13941,13948,13956,13963,13975,13982,13994,14001,14013,14020,14027,14034,14041,14048,14067,14074,14096,14104,14111,14118,14126,14133],{"nodeType":1921,"data":13769,"content":13770},{},[13771,13775,13783],{"nodeType":1907,"value":13772,"marks":13773,"data":13774},"In the HPC realm, the Texas Advanced Computing Center (TACC) is known for deploying some of the biggest and baddest systems in the world. Systems like Frontera, which was formerly #5 on the TOP500 list of the world’s fastest computers. Or Ranger, which had the famous Sun Microsystems Magnum InfiniBand switch that had longhorns mounted to the top… a switch that can now be found in the ",[],{},{"nodeType":1916,"data":13776,"content":13778},{"uri":13777},"https:\u002F\u002Fcomputerhistory.org\u002F",[13779],{"nodeType":1907,"value":13780,"marks":13781,"data":13782},"Computer History Museum",[],{},{"nodeType":1907,"value":1920,"marks":13784,"data":13785},[],{},{"nodeType":12289,"data":13787,"content":13806},{"target":13788},{"metadata":13789,"sys":13792,"fields":13799},{"tags":13790,"concepts":13791},[],[],{"space":13793,"id":13795,"type":934,"createdAt":13796,"updatedAt":13796,"environment":13797,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13794},{"type":657,"linkType":658,"id":659},"4tFXMKfKEtKVTvDssptn81","2023-07-22T21:26:15.905Z",{"sys":13798},{"id":665,"type":657,"linkType":666},{"title":13728,"description":13728,"file":13800},{"url":13801,"details":13802,"fileName":13805,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4tFXMKfKEtKVTvDssptn81\u002F9a0cff71d1a81be8548594023f115756\u002Fthe-launchpad-to-exascale-the-story-of-vast-at-tacc-alt.webp",{"size":13803,"image":13804},136010,{"width":1144,"height":1145},"the-launchpad-to-exascale-the-story-of-vast-at-tacc-alt.webp",[],{"nodeType":1921,"data":13808,"content":13809},{},[13810,13814,13822],{"nodeType":1907,"value":13811,"marks":13812,"data":13813},"As an aside, you can mount longhorns on virtually any piece of IT equipment, car, bedpost or household pet, just visit ",[],{},{"nodeType":1916,"data":13815,"content":13817},{"uri":13816},"https:\u002F\u002Fskullbliss.com\u002F",[13818],{"nodeType":1907,"value":13819,"marks":13820,"data":13821},"skullbliss.com",[],{},{"nodeType":1907,"value":13823,"marks":13824,"data":13825},"! 🤘",[],{},{"nodeType":1921,"data":13827,"content":13828},{},[13829],{"nodeType":1907,"value":13830,"marks":13831,"data":13832},"OK, back to being serious and talking about data. For almost two decades, TACC has been powering these machines with the Lustre File System. While Lustre is a popular option for HPC centers that work to balance scale and affordability, its complexity and periodic fragility also has reinforced the “no pain, no gain” mentality that is commonly found in scalable HPC and AI computing.",[],{},{"nodeType":1921,"data":13834,"content":13835},{},[13836,13840,13848],{"nodeType":1907,"value":13837,"marks":13838,"data":13839},"In 2022, Dan Stanzione (Executive Director, TACC) gave a talk at the Rice Energy HPC conference TACC that was reported on ",[],{},{"nodeType":1916,"data":13841,"content":13843},{"uri":13842},"https:\u002F\u002Fwww.hpcwire.com\u002F2022\u002F04\u002F14\u002Ftacc-looks-to-horizon-system-for-its-leadership-class-computing-facility\u002F",[13844],{"nodeType":1907,"value":13845,"marks":13846,"data":13847},"by HPCWire",[],{},{"nodeType":1907,"value":13849,"marks":13850,"data":13851}," to discuss their scaling plans for their first ~exascale class system, Horizon. While we were having some conversations with TACC at the time, I was surprised to see us called out as a potential option for such a large system.\n\nIn 2023, the TACC team returned to deliver two follow-on talks at Rice:",[],{},{"nodeType":12447,"data":13853,"content":13854},{},[13855,13865],{"nodeType":12451,"data":13856,"content":13857},{},[13858],{"nodeType":1921,"data":13859,"content":13860},{},[13861],{"nodeType":1907,"value":13862,"marks":13863,"data":13864},"In talk 1, Dan outlines how the price of flash infrastructure (with a side of revolutionary data reduction) has dropped to the point where TACC can now consider unifying their \u002Fscratch tier and \u002Fnearline tier into one bigger and faster tier",[],{},{"nodeType":12451,"data":13866,"content":13867},{},[13868],{"nodeType":1921,"data":13869,"content":13870},{},[13871],{"nodeType":1907,"value":13872,"marks":13873,"data":13874},"In talk 2, Tommy Minyard (TACC Director of Advanced Computing Systems) then discussed their recent storage explorations and some of the things that “pleasantly surprised” them about VAST",[],{},{"nodeType":1921,"data":13876,"content":13877},{},[13878],{"nodeType":1907,"value":13879,"marks":13880,"data":13881},"Today, we’re excited to announce that TACC has selected VAST as the data platform for Stampede3, their next generation large-scale research computer that will power applications for 10,000+ of the United States’ scientists and engineers. The Stampede purchase precedes selection for their next really big system, which will likely be announced later this year.",[],{},{"nodeType":1921,"data":13883,"content":13884},{},[13885],{"nodeType":1907,"value":13886,"marks":13887,"data":13888},"For the rest of this blog, I’ll outline the three critical factors that led TACC to choose VAST. Before that, here’s a quick video that dives into Tommy’s thoughts on I\u002FO for these systems.",[],{},{"nodeType":1921,"data":13890,"content":13891},{},[13892,13895,13902],{"nodeType":1907,"value":2031,"marks":13893,"data":13894},[],{},{"nodeType":1916,"data":13896,"content":13898},{"uri":13897},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1q4rhkab2lo",[13899],{"nodeType":1907,"value":13897,"marks":13900,"data":13901},[],{},{"nodeType":1907,"value":12343,"marks":13903,"data":13904},[],{},{"nodeType":1921,"data":13906,"content":13907},{},[13908],{"nodeType":1907,"value":13909,"marks":13910,"data":13911},"OK, now to the back story.",[],{},{"nodeType":1921,"data":13913,"content":13914},{},[13915],{"nodeType":1907,"value":13916,"marks":13917,"data":13918},"First, it’s important to point out that pain has a real price. In the video, Tommy starts his I\u002FO section by pointing out that file systems have been the most significant contributor to system downtime, which led them to look beyond Lustre for new options. Today’s supercomputers are not cheap… customers can spend over $100M on systems that are expected to be fully operational in order to realize their value. And downtime costs… not just in admin and user frustration, but it also has an explicit cost in the form of diminished supercomputer system value and lost opportunity cost as people wait to run their codes.",[],{},{"nodeType":1921,"data":13920,"content":13921},{},[13922],{"nodeType":1907,"value":13923,"marks":13924,"data":13925},"If you synthesized all of the links, you’ve got DAOS, BeeGFS, Weka and VAST in the mix as TACC considered their options beyond Lustre. VAST was the only solution without a bespoke parallel file system client… instead providing a simple NFS-based solution which was perceived as the option that would perform the least (just reflecting the classic anti-NFS bias in HPC).",[],{},{"nodeType":1921,"data":13927,"content":13928},{},[13929],{"nodeType":1907,"value":13930,"marks":13931,"data":13932},"So the TACC team got to testing.",[],{},{"nodeType":1921,"data":13934,"content":13935},{},[13936],{"nodeType":1907,"value":13937,"marks":13938,"data":13940},"Test #1: Data Reduction",[13939],{"type":13576},{},{"nodeType":1921,"data":13942,"content":13943},{},[13944],{"nodeType":1907,"value":13945,"marks":13946,"data":13947},"After loading heaps of scientific (and pre-compressed) data into the VAST system, TACC was pleasantly surprised to find an average of 2:1 data reduction across their data. This, coupled with VAST’s approach to leveraging low-cost QLC flash and our new low-overhead data protection led TACC to conclude that they could consolidate their high-performance flash plans with their lower-performance nearline storage plans and instead plan a single all-flash system that eliminates data migration between tiers and brings more acceleration by making the entire active data set flash-based. All-flash, of course, helps as TACC transitions to supporting more AI workloads since these applications are heavy with random read operations, and random reads hate HDD storage.",[],{},{"nodeType":1921,"data":13949,"content":13950},{},[13951],{"nodeType":1907,"value":13952,"marks":13953,"data":13955},"Test #2: Application Scaling and Performance",[13954],{"type":13576},{},{"nodeType":1921,"data":13957,"content":13958},{},[13959],{"nodeType":1907,"value":13960,"marks":13961,"data":13962},"Enter Colin. Colin is a researcher at CMU who studies nuclear physics and runs simulations on TACC’s systems. I don’t know all of the details about how his code reads and writes data, but I’ve come to understand that his ‘nuclear codes 🙂’ play hell with scalable file systems. So, TACC uses Colin’s code as a litmus test for how file systems can play under the most stressful and pathological conditions.",[],{},{"nodeType":1921,"data":13964,"content":13965},{},[13966,13970],{"nodeType":1907,"value":13967,"marks":13968,"data":13969},"Test #2.1 - ",[],{},{"nodeType":1907,"value":13971,"marks":13972,"data":13974},"does it work?",[13973],{"type":12260},{},{"nodeType":1921,"data":13976,"content":13977},{},[13978],{"nodeType":1907,"value":13979,"marks":13980,"data":13981},"Upon 1st pass, VAST is the only new system that can stand up to the load. Everything else fails.",[],{},{"nodeType":1921,"data":13983,"content":13984},{},[13985,13989],{"nodeType":1907,"value":13986,"marks":13987,"data":13988},"Test #2.2 - ",[],{},{"nodeType":1907,"value":13990,"marks":13991,"data":13993},"no really, does it work?",[13992],{"type":12260},{},{"nodeType":1921,"data":13995,"content":13996},{},[13997],{"nodeType":1907,"value":13998,"marks":13999,"data":14000},"After much experimentation and optimization with Lustre, TACC has managed to scale Colin’s code to 350 clients, before the Lustre metadata server falls over. So, at this point TACC decides to scale up to an equivalent level of client scalability on VAST. The result?\nA. VAST scales fine to 350 clients, now neck and neck with Lustre in terms of scaling\nB. At equivalent client scale, Colin’s code is running 30% faster on VAST than Lustre. Whoa.",[],{},{"nodeType":1921,"data":14002,"content":14003},{},[14004,14008],{"nodeType":1907,"value":14005,"marks":14006,"data":14007},"Test #2.3 - ",[],{},{"nodeType":1907,"value":14009,"marks":14010,"data":14012},"whoa. so, where does it break?",[14011],{"type":12260},{},{"nodeType":1921,"data":14014,"content":14015},{},[14016],{"nodeType":1907,"value":14017,"marks":14018,"data":14019},"Then TACC opened things up and started trying to break VAST with their Frontera supercomputer, which at the time was the 19th fastest supercomputer in the world.",[],{},{"nodeType":1921,"data":14021,"content":14022},{},[14023],{"nodeType":1907,"value":14024,"marks":14025,"data":14026},"… 500 nodes. Still running.",[],{},{"nodeType":1921,"data":14028,"content":14029},{},[14030],{"nodeType":1907,"value":14031,"marks":14032,"data":14033},"… 1,000 nodes. Still running...",[],{},{"nodeType":1921,"data":14035,"content":14036},{},[14037],{"nodeType":1907,"value":14038,"marks":14039,"data":14040},"… 2,000 nodes. All good!",[],{},{"nodeType":1921,"data":14042,"content":14043},{},[14044],{"nodeType":1907,"value":14045,"marks":14046,"data":14047},"… 4,000 nodes. OK, it still works. We give up… ",[],{},{"nodeType":1921,"data":14049,"content":14050},{},[14051,14055,14063],{"nodeType":1907,"value":14052,"marks":14053,"data":14054},"The VAST system had made its point, and the power of VAST’s ",[],{},{"nodeType":1916,"data":14056,"content":14058},{"uri":14057},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fhow-it-works",[14059],{"nodeType":1907,"value":14060,"marks":14061,"data":14062},"parallel DASE architecture",[],{},{"nodeType":1907,"value":14064,"marks":14065,"data":14066}," as well as great engineering and QA has given hope to TACC that there’s now a new standard of scalability when metadata and data are now embarrassingly parallel across all of the VAST servers.",[],{},{"nodeType":1921,"data":14068,"content":14069},{},[14070],{"nodeType":1907,"value":14071,"marks":14072,"data":14073},"Fortunately, we were able to grab some CCTV footage during the benchmark to confirm that 20U of HW running VAST SW could stand up to 50 racks of Dell servers:",[],{},{"nodeType":12289,"data":14075,"content":14095},{"target":14076},{"metadata":14077,"sys":14080,"fields":14088},{"tags":14078,"concepts":14079},[],[],{"space":14081,"id":14083,"type":934,"createdAt":14084,"updatedAt":14085,"environment":14086,"publishedVersion":712,"revision":901,"locale":673},{"sys":14082},{"type":657,"linkType":658,"id":659},"6s5OqPMXzWsNblI5O2nO9c","2023-07-21T19:05:24.652Z","2023-07-21T19:05:38.577Z",{"sys":14087},{"id":665,"type":657,"linkType":666},{"title":13728,"description":13728,"file":14089},{"url":14090,"details":14091,"fileName":14094,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6s5OqPMXzWsNblI5O2nO9c\u002F3bc7479c5b12950b916886b7ac632e23\u002Fthe-launchpad-to-exascale-the-story-of-vast-at-tacc-2.webp",{"size":14092,"image":14093},79504,{"width":1144,"height":1145},"the-launchpad-to-exascale-the-story-of-vast-at-tacc-2.webp",[],{"nodeType":1921,"data":14097,"content":14098},{},[14099],{"nodeType":1907,"value":14100,"marks":14101,"data":14103},"Test #3: Failure Handling",[14102],{"type":13576},{},{"nodeType":1921,"data":14105,"content":14106},{},[14107],{"nodeType":1907,"value":14108,"marks":14109,"data":14110},"As TACC went to upgrade to a newer version of the VAST AI OS, the upgrade process failed as the system discovered a failed storage server. During this time, the system was left in a state where it was concurrently running multiple versions of our software.",[],{},{"nodeType":1921,"data":14112,"content":14113},{},[14114],{"nodeType":1907,"value":14115,"marks":14116,"data":14117},"So - the VAST support team coordinates an RMA and the Sales team calls to apologize for the headache. 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world of Artificial Intelligence was rocked circa 2012 by the use of GPU’s in Neural Network processing by Geoffrey Hinton’s team at the University of Toronto – where Alex Krizhevsky’s Ph. D thesis work won the Imagenet object recognition challenge using them. The results, as everyone knows, were stunning – a 15% increase in accuracy overnight compared to other machine learning techniques, and this started an explosion of research and applications in the world of Neural Networks for a variety of purposes, especially in the arena of Convolutional Neural Networks (CNNs), now referred to as ",{"data":17467,"content":17469,"nodeType":1916},{"uri":17468},"https:\u002F\u002Fvastdata.com\u002Fscale-out-solutions\u002Fai-machine-learning-storage\u002F",[17470],{"data":17471,"marks":17472,"value":6468,"nodeType":1907},{},[],{"data":17474,"marks":17475,"value":1920,"nodeType":1907},{},[],{"nodeType":1922,"data":17477,"content":17478},{},[17479,17487,17494,17513,17520,17527,17557,17564,17571,17578,17599,17606,17612,17678,17685,17692,17713,17720,17741,17748,17755,17774,17795,17802,17809,17830,17837,17845,17862,17869,17888,17895,17986,18027,18049,18056],{"nodeType":17480,"data":17481,"content":17482},"heading-3",{},[17483],{"nodeType":1907,"value":17484,"marks":17485,"data":17486},"Introduction",[],{},{"nodeType":1921,"data":17488,"content":17489},{},[17490],{"nodeType":1907,"value":17491,"marks":17492,"data":17493},"The world of Artificial Intelligence was rocked circa 2012 by the use of GPUs in Neural Network processing by Geoffrey Hinton’s team at the University of Toronto – where Alex Krizhevsky’s Ph. D. thesis work won the ImageNet object recognition challenge using them. The results, as everyone knows, were stunning – a 15% increase in accuracy overnight compared to other machine learning techniques, and this started an explosion of research and applications in the world of Neural Networks for a variety of purposes, especially in the arena of Convolutional Neural Networks (CNNs), now referred to as Deep Learning.",[],{},{"nodeType":1921,"data":17495,"content":17496},{},[17497,17501,17509],{"nodeType":1907,"value":17498,"marks":17499,"data":17500},"While doing justice to this vast field is beyond the scope of this article, a good starting point for the reader is the ",[],{},{"nodeType":1916,"data":17502,"content":17504},{"uri":17503},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDeep_learning",[17505],{"nodeType":1907,"value":17506,"marks":17507,"data":17508},"Wikipedia Deep Learning",[],{},{"nodeType":1907,"value":17510,"marks":17511,"data":17512}," article, which chronicles the history and current state of this field. Yoshua Bengio, Geoffrey Hinton, and Yann LeCun were awarded the ACM Turing Award for their seminal contributions to this field, and subsequently, Geoffrey Hinton was awarded the Nobel Prize in Physics. NVIDIA has been at the forefront of this revolution since 2009 with its GPUs as the backbone technology for much of these works.",[],{},{"nodeType":1921,"data":17514,"content":17515},{},[17516],{"nodeType":1907,"value":17517,"marks":17518,"data":17519},"As GPUs became more powerful, far outstripping advances in CPU processing, new bottlenecks appeared in processing architectures, where it became apparent that moving data between main memory (SYSMEM) and GPU memory, as well as GPU-GPU data movement, was increasingly a limiting factor in keeping the GPUs cores busy.",[],{},{"nodeType":1921,"data":17521,"content":17522},{},[17523],{"nodeType":1907,"value":17524,"marks":17525,"data":17526},"This prompted the introduction of a set of technologies from NVIDIA using Remote Direct Memory Access (RDMA) to bypass the SYSMEM\u002FCPU complex to enable direct communication between GPUs, both within a server and with other servers, referred to as GPUDirect. This allowed multiple GPUs on a system, and across systems, to cooperate in training large models. But this still left a limitation in moving data from the IO subsystem to the GPU complex, which traditionally had to use SYSMEM as a “bounce buffer” to get data to  GPU memory.",[],{},{"nodeType":1921,"data":17528,"content":17529},{},[17530,17534,17542,17546,17554],{"nodeType":1907,"value":17531,"marks":17532,"data":17533},"NVIDIA then began to develop an extension - GPUDirect Storage (GDS) – to enable direct movement of data from the storage subsystem to GPU memory when appropriate, bypassing SYSMEM. A good start to become acquainted with these technologies is the ",[],{},{"nodeType":1916,"data":17535,"content":17537},{"uri":17536},"https:\u002F\u002Fstatic.rainfocus.com\u002Fnvidia\u002Fgtcfall20\u002Fsess\u002F1596756804120001kqY3\u002FSessionFile\u002FA21209%20Accelerating%20Storage%20with%20Magnum%20IO%20and%20GPUDirect%20Storage_1602092945212001mbEW.pdf",[17538],{"nodeType":1907,"value":17539,"marks":17540,"data":17541},"NVIDIA GTC 2020 Conference slides for MagnumIO and GPUDirect for Storage",[],{},{"nodeType":1907,"value":17543,"marks":17544,"data":17545},". For deeper details, please study the ",[],{},{"nodeType":1916,"data":17547,"content":17549},{"uri":17548},"https:\u002F\u002Fdocs.nvidia.com\u002Fgpudirect-storage\u002Fdesign-guide\u002Findex.html",[17550],{"nodeType":1907,"value":17551,"marks":17552,"data":17553},"GPUDirect for Storage Design Guide",[],{},{"nodeType":1907,"value":1920,"marks":17555,"data":17556},[],{},{"nodeType":12267,"data":17558,"content":17559},{},[17560],{"nodeType":1907,"value":17561,"marks":17562,"data":17563},"So what is GPUDirect Storage (GDS)?",[],{},{"nodeType":1921,"data":17565,"content":17566},{},[17567],{"nodeType":1907,"value":17568,"marks":17569,"data":17570},"Traditional data flow from Storage to the GPU typically flows through a NIC to the CPU (through a PCIe Switch complex in the larger, purpose-built NVIDIA DGX Systems) and to SYSMEM, after which it is moved to the GPU memory for further processing, as shown in Fig 1. Unless some pre-processing is needed in the CPU, this is an unnecessary limitation in several ways.",[],{},{"nodeType":1921,"data":17572,"content":17573},{},[17574],{"nodeType":1907,"value":17575,"marks":17576,"data":17577},"First, it is an unneeded hop for the data between SYSMEM and GPU. Next, the operation required the use of precious CPU resources that could have been used for some other processing. Lastly, the aggregate bandwidth the storage can provide to the system is limited by the internal SYSMEM-GPU bandwidth, which is typically significantly less than the bandwidth a well-architected storage system can provide through the multiple NICs on the system. For example, on an NVIDIA DGX-2 system, we have observed that we can get a maximum of around 50 GiB\u002Fs on internal bandwidth, while the 8x100 Gb IB NICs can deliver close to twice that bandwidth.",[],{},{"nodeType":12289,"data":17579,"content":17598},{"target":17580},{"metadata":17581,"sys":17584,"fields":17591},{"tags":17582,"concepts":17583},[],[],{"space":17585,"id":17587,"type":934,"createdAt":17588,"updatedAt":17588,"environment":17589,"publishedVersion":847,"revision":1136,"locale":673},{"sys":17586},{"type":657,"linkType":658,"id":659},"2kLuELC8CauxCQWndQi3rs","2023-09-07T07:06:16.228Z",{"sys":17590},{"id":665,"type":657,"linkType":666},{"title":17420,"description":17420,"file":17592},{"url":17593,"details":17594,"fileName":17597,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2kLuELC8CauxCQWndQi3rs\u002Fa73721b71c206c6cf6cae411a759a7fc\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-1.webp",{"size":17595,"image":17596},34166,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-1.webp",[],{"nodeType":1921,"data":17600,"content":17601},{},[17602],{"nodeType":1907,"value":17603,"marks":17604,"data":17605},"GDS solves that issue elegantly, again, by exploiting DMA or RDMA capabilities from the storage. As Figure 2 shows, if the storage subsystem supports (R)DMA, it can be instructed to move data directly to a GPU Memory address as opposed to a SYSMEM memory address, thus bypassing the CPU\u002FSYSMEM complex altogether. This permits data transfer to the system at near line speeds, as it is no longer limited by the internal memory bandwidth.",[],{},{"nodeType":12267,"data":17607,"content":17608},{},[17609],{"nodeType":1907,"value":12888,"marks":17610,"data":17611},[],{},{"nodeType":1921,"data":17613,"content":17614},{},[17615,17619,17627,17631,17639,17643,17651,17655,17662,17666,17674],{"nodeType":1907,"value":17616,"marks":17617,"data":17618},"VAST Data is a modern all-flash ",[],{},{"nodeType":1916,"data":17620,"content":17622},{"uri":17621},"..\u002F..\u002Fglossary\u002Fnvme\u002F",[17623],{"nodeType":1907,"value":17624,"marks":17625,"data":17626},"NVMe",[],{},{"nodeType":1907,"value":17628,"marks":17629,"data":17630}," architecture that breaks the trade-off between performance, scale, and cost. By using a combination of NVMeoF, ",[],{},{"nodeType":1916,"data":17632,"content":17634},{"uri":17633},"..\u002F..\u002Fglossary\u002Fstorage-class-memory\u002F",[17635],{"nodeType":1907,"value":17636,"marks":17637,"data":17638},"Storage Class Memory",[],{},{"nodeType":1907,"value":17640,"marks":17641,"data":17642},", and ",[],{},{"nodeType":1916,"data":17644,"content":17646},{"uri":17645},"..\u002F..\u002Fglossary\u002Fhyperscale-flash\u002F",[17647],{"nodeType":1907,"value":17648,"marks":17649,"data":17650},"QLC",[],{},{"nodeType":1907,"value":17652,"marks":17653,"data":17654}," NAND technology in a cache-less architecture, we have delivered record-breaking performance, hyper-linear scalability, and costs approaching the economics of hard-drive based systems. A ",[],{},{"nodeType":1916,"data":17656,"content":17658},{"uri":17657},"..\u002F..\u002Fglossary\u002Fvast-cluster\u002F",[17659],{"nodeType":1907,"value":12926,"marks":17660,"data":17661},[],{},{"nodeType":1907,"value":17663,"marks":17664,"data":17665}," presents itself to client systems through industry standard protocols – NFS, NFS over RDMA, ",[],{},{"nodeType":1916,"data":17667,"content":17669},{"uri":17668},"..\u002F..\u002Fglossary\u002Fsmb\u002F",[17670],{"nodeType":1907,"value":17671,"marks":17672,"data":17673},"SMB",[],{},{"nodeType":1907,"value":17675,"marks":17676,"data":17677},", and S3, with support for a Kubernetes CSI in a container-driven world.",[],{},{"nodeType":1921,"data":17679,"content":17680},{},[17681],{"nodeType":1907,"value":17682,"marks":17683,"data":17684},"When we started to work with NVIDIA in early 2020, we quickly realized, along with NVIDIA GDS Engineering, that we were exposing ourselves as an RDMA target to the DGX-2 systems we had been testing. NVIDIA GDS engineers had already experimented with GDS working on NFS systems that support RDMA, and had been on the lookout for a commercial storage system that supported it. We were eager to test this, and astonishingly, VAST NFS over RDMA worked with GDS literally out of the box – we had usable results in a few hours with no modifications to our system.",[],{},{"nodeType":1921,"data":17686,"content":17687},{},[17688],{"nodeType":1907,"value":17689,"marks":17690,"data":17691},"This started a highly productive collaboration between us, and with some larger systems, we quickly showed that we can achieve line rate bandwidth with a DGX-2 system. Shown below in Figure 3 is the VAST UI delivering data to a DGX-2 system with 8 x 100 Gb HDR100 IB NICs, delivering a sustained 94+ GB\u002Fs 1 MB IO Size Read throughput (we have recorded as high as 98 GB\u002Fs). One of the key points to note here is that this throughput was delivered via a single NFSoRDMA mount point to a single client DGX-2 system, which is unprecedented for NFS.",[],{},{"nodeType":12289,"data":17693,"content":17712},{"target":17694},{"metadata":17695,"sys":17698,"fields":17705},{"tags":17696,"concepts":17697},[],[],{"space":17699,"id":17701,"type":934,"createdAt":17702,"updatedAt":17702,"environment":17703,"publishedVersion":847,"revision":1136,"locale":673},{"sys":17700},{"type":657,"linkType":658,"id":659},"5igu7lrV49O47d5LQY9Ws7","2023-09-07T07:12:08.424Z",{"sys":17704},{"id":665,"type":657,"linkType":666},{"title":17420,"description":17420,"file":17706},{"url":17707,"details":17708,"fileName":17711,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5igu7lrV49O47d5LQY9Ws7\u002F7a703680c315981d365e8af9cdb9dce7\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-2.webp",{"size":17709,"image":17710},56030,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-2.webp",[],{"nodeType":1921,"data":17714,"content":17715},{},[17716],{"nodeType":1907,"value":17717,"marks":17718,"data":17719},"Probably even more interesting was the fact that the effect of bypassing CPU\u002FSYSMEM was dramatic for the CPU utilization of the system. Figure 4 shows data moving to the GPU using the traditional path (as shown in Figure 1) and achieving no more than 33 GB\u002Fs before saturating the CPU. In contrast, in Figure 5, we get near the rate line bandwidth, but with only 15% CPU utilization as seen in htop.",[],{},{"nodeType":12289,"data":17721,"content":17740},{"target":17722},{"metadata":17723,"sys":17726,"fields":17733},{"tags":17724,"concepts":17725},[],[],{"space":17727,"id":17729,"type":934,"createdAt":17730,"updatedAt":17730,"environment":17731,"publishedVersion":692,"revision":1136,"locale":673},{"sys":17728},{"type":657,"linkType":658,"id":659},"9C2uR3xuYEx3YlO5UqQFR","2023-09-07T07:12:38.537Z",{"sys":17732},{"id":665,"type":657,"linkType":666},{"title":17420,"description":17420,"file":17734},{"url":17735,"details":17736,"fileName":17739,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F9C2uR3xuYEx3YlO5UqQFR\u002Fa01fa6c08cfb790a4b3cb148a3d73856\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-3.webp",{"size":17737,"image":17738},148670,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-3.webp",[],{"nodeType":12267,"data":17742,"content":17743},{},[17744],{"nodeType":1907,"value":17745,"marks":17746,"data":17747},"The Next Chapter: GDS with the DGX A100",[],{},{"nodeType":1921,"data":17749,"content":17750},{},[17751],{"nodeType":1907,"value":17752,"marks":17753,"data":17754},"In the middle of 2020, NVIDIA announced a new DGX Platform based on the revolutionary A100 GPUs, an innovation that we eagerly awaited along with the rest of the industry. The system architecture used 8xA100 GPUs, AMD Epyc (Rome) processors, and 8 HDR 200 Gb NICs, with a theoretical line bandwidth of 200 GB\u002Fs on a single system – clearly a head-turner!",[],{},{"nodeType":1921,"data":17756,"content":17757},{},[17758,17762,17770],{"nodeType":1907,"value":17759,"marks":17760,"data":17761},"As GDS is not yet generally available (as of this writing), we also published a joint NVIDIA\u002FVAST ",[],{},{"nodeType":1916,"data":17763,"content":17765},{"uri":17764},"..\u002F..\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fgpu-reference-architecture",[17766],{"nodeType":1907,"value":17767,"marks":17768,"data":17769},"Reference Architecture",[],{},{"nodeType":1907,"value":17771,"marks":17772,"data":17773}," for the DGX A100 systems without GDS. All the measurements were conducted by NVIDIA in their labs. The curious reader should download and study the details in this RA. To highlight some parts of it, the results from running MLPerf with 1 to 4 DGX A100s are shown below.",[],{},{"nodeType":12289,"data":17775,"content":17794},{"target":17776},{"metadata":17777,"sys":17780,"fields":17787},{"tags":17778,"concepts":17779},[],[],{"space":17781,"id":17783,"type":934,"createdAt":17784,"updatedAt":17784,"environment":17785,"publishedVersion":847,"revision":1136,"locale":673},{"sys":17782},{"type":657,"linkType":658,"id":659},"74YXAFCMQs5K0RJAvhkOPs","2023-09-07T07:15:46.945Z",{"sys":17786},{"id":665,"type":657,"linkType":666},{"title":17420,"description":17420,"file":17788},{"url":17789,"details":17790,"fileName":17793,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F74YXAFCMQs5K0RJAvhkOPs\u002Fa3bc8482ecd48b9ee1a4f5c26cfa6dc1\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-4.webp",{"size":17791,"image":17792},38726,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-4.webp",[],{"nodeType":1921,"data":17796,"content":17797},{},[17798],{"nodeType":1907,"value":17799,"marks":17800,"data":17801},"The MLPerf Training (v0.7) benchmark tests the well-known Resnet-50 Residual CNN image classification benchmark on the Imagenet dataset (typically run under MXNet, Tensorflow, or Pytorch), and the results we obtained show impressive linearity in scaling as well as state-of-the-art images\u002Fsecond. However, even more impressive is that Epoch 0 (the first training pass over the data), which typically is data straight from the storage subsystem, with nothing in the file system buffer cache, has a near-zero difference with the overall average for all Epochs. This shows that the VAST cluster performance is as good as subsequent Epochs where the data can come from system memory! Back to GDS on the DGX A100’s…",[],{},{"nodeType":1921,"data":17803,"content":17804},{},[17805],{"nodeType":1907,"value":17806,"marks":17807,"data":17808},"We were not disappointed when we had the opportunity to test GDS on a VAST Data Cluster against a DGX A100 server. We achieved a maximum of 162 GiB\u002Fs Read Throughput – which appears to be the maximum the system can deliver, fully saturating the capabilities of the DGX A100 system. The results below were from benchmarking performed by NVIDIA (not by VAST) and were shown and published at the NVIDIA GTC 2020 Fall Conference.",[],{},{"nodeType":12289,"data":17810,"content":17829},{"target":17811},{"metadata":17812,"sys":17815,"fields":17822},{"tags":17813,"concepts":17814},[],[],{"space":17816,"id":17818,"type":934,"createdAt":17819,"updatedAt":17819,"environment":17820,"publishedVersion":847,"revision":1136,"locale":673},{"sys":17817},{"type":657,"linkType":658,"id":659},"68c6Ez6mNxoTIp8Bgm1lIy","2023-09-07T07:16:30.393Z",{"sys":17821},{"id":665,"type":657,"linkType":666},{"title":17420,"description":17420,"file":17823},{"url":17824,"details":17825,"fileName":17828,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F68c6Ez6mNxoTIp8Bgm1lIy\u002Fd456b4b0f17a8a10697ea6179aa1a24d\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-5.webp",{"size":17826,"image":17827},42398,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-5.webp",[],{"nodeType":1921,"data":17831,"content":17832},{},[17833],{"nodeType":1907,"value":17834,"marks":17835,"data":17836},"VAST Data was one of a few select vendors that supports GDS officially at launch by NVIDIA, which became generally available in 2021. More ecosystem work is expected to be completed in that time frame as well, with support for the NVIDIA cuFile APIs (necessary to exploit GDS capabilities) in major Deep Learning Frameworks such as Tensorflow, PyTorch, and MXNet, as well as exploration of new use cases for this work.",[],{},{"nodeType":12267,"data":17838,"content":17839},{},[17840],{"nodeType":1907,"value":17841,"marks":17842,"data":17844},"GPU Direct Use Cases in real life",[17843],{"type":13576},{},{"nodeType":1921,"data":17846,"content":17847},{},[17848,17852,17859],{"nodeType":1907,"value":17849,"marks":17850,"data":17851},"NVIDIA did some stellar work integrating the GDS cuFile APIs into various pieces of the NVIDIA software portfolio, starting with NVIDIA Dali, an image augmentation library, with Python bindings. They also introduced GDS support for image loading in numpy format to allow for fast loading into GPU memory for inference. \n\nVAST Data (yours truly) presented (at Tech Field Day in 2023) a variety of live benchmarks for GDS and other real-life applications, notable among which was using the GDS numpy integration to accelerate the MLPerf DeepCam Climate Segmentation benchmark, as well as using the DALI integration to show how MRI images can be ingested faster with GDS. These results, and others,  can be viewed on YouTube from that:\n\n",[],{},{"nodeType":1916,"data":17853,"content":17855},{"uri":17854},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bW_0_JKuTt0",[17856],{"nodeType":1907,"value":17854,"marks":17857,"data":17858},[],{},{"nodeType":1907,"value":2031,"marks":17860,"data":17861},[],{},{"nodeType":1921,"data":17863,"content":17864},{},[17865],{"nodeType":1907,"value":17866,"marks":17867,"data":17868},"Since then, the use of GDS has made rapid strides with NVIDIA incorporating GDS support in its RAPIDS framework, with direct support for GDS.",[],{},{"nodeType":1921,"data":17870,"content":17871},{},[17872,17876,17884],{"nodeType":1907,"value":17873,"marks":17874,"data":17875},"GPU Direct Storage functionality on VAST is invoked by using the",[],{},{"nodeType":1916,"data":17877,"content":17879},{"uri":17878},"https:\u002F\u002Fdocs.nvidia.com\u002Fgpudirect-storage\u002Fapi-reference-guide\u002Findex.html",[17880],{"nodeType":1907,"value":17881,"marks":17882,"data":17883}," cuFile APIs",[],{},{"nodeType":1907,"value":17885,"marks":17886,"data":17887}," from NVIDIA, which provide bindings in the C Language, and are part of CUDA from CUDA 11.4 onwards. One way to use GDS for applications is to directly write the appropriate cuFile function calls into custom CUDA code in the calling application. The implementation will depend on application details, but most CUDA developers will know how to proceed - the API specification is fairly straightforward.",[],{},{"nodeType":1921,"data":17889,"content":17890},{},[17891],{"nodeType":1907,"value":17892,"marks":17893,"data":17894},"However, in many cases, GDS is integrated with other frameworks from NVIDIA, allowing for more transparent access to its capabilities. Note that GDS is useful when the data are needed in the GPU VMEM with no requirements to do any CPU-based processing in SYSMEM. Typically, large datasets benefit from this significantly. There are a few broad areas where GDS works “out-of-the-box” without any need to write to the cuFILE APIs.",[],{},{"nodeType":1921,"data":17896,"content":17897},{},[17898,17902,17910,17914,17922,17926,17934,17938,17946,17950,17958,17962,17970,17974,17982],{"nodeType":1907,"value":17899,"marks":17900,"data":17901},"Using the NVIDIA",[],{},{"nodeType":1916,"data":17903,"content":17905},{"uri":17904},"https:\u002F\u002Frapids.ai\u002F",[17906],{"nodeType":1907,"value":17907,"marks":17908,"data":17909}," RAPIDS framework",[],{},{"nodeType":1907,"value":17911,"marks":17912,"data":17913},", many components of RAPIDS support GDS either automatically or with some tweaking of configurations. Much of this is by using the",[],{},{"nodeType":1916,"data":17915,"content":17917},{"uri":17916},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhigh-performance-remote-io-with-nvidia-kvikio\u002F",[17918],{"nodeType":1907,"value":17919,"marks":17920,"data":17921}," KvikIO libraries",[],{},{"nodeType":1907,"value":17923,"marks":17924,"data":17925}," and integrating this with various tooling for dataframes -",[],{},{"nodeType":1916,"data":17927,"content":17929},{"uri":17928},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fboosting-data-ingest-throughput-with-gpudirect-storage-and-rapids-cudf\u002F",[17930],{"nodeType":1907,"value":17931,"marks":17932,"data":17933}," cuDF",[],{},{"nodeType":1907,"value":17935,"marks":17936,"data":17937},", medical images -",[],{},{"nodeType":1916,"data":17939,"content":17941},{"uri":17940},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Faccelerating-digital-pathology-workflows-using-cucim-and-nvidia-gpudirect-storage\u002F#:~:text=Whole%20slide%20imaging%20(WSI)%20is,a%20tiled%20image%20processing%20workflow.",[17942],{"nodeType":1907,"value":17943,"marks":17944,"data":17945}," cuCIM",[],{},{"nodeType":1907,"value":17947,"marks":17948,"data":17949},", Machine Learning -",[],{},{"nodeType":1916,"data":17951,"content":17953},{"uri":17952},"https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcuml",[17954],{"nodeType":1907,"value":17955,"marks":17956,"data":17957}," cuML",[],{},{"nodeType":1907,"value":17959,"marks":17960,"data":17961},", Graphs -",[],{},{"nodeType":1916,"data":17963,"content":17965},{"uri":17964},"https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcugraph",[17966],{"nodeType":1907,"value":17967,"marks":17968,"data":17969}," cugraph",[],{},{"nodeType":1907,"value":17971,"marks":17972,"data":17973},", etc. Many file types, such as Parquet, CSV, ORC, ZARR, etc., and multi-tiled ",[],{},{"nodeType":1916,"data":17975,"content":17977},{"uri":17976},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fwhole-slide-image-analysis-in-real-time-with-monai-and-rapids\u002F",[17978],{"nodeType":1907,"value":17979,"marks":17980,"data":17981},"Whole Slide Images (WSI",[],{},{"nodeType":1907,"value":17983,"marks":17984,"data":17985},") can be accessed using cuDF and cuSIM with significant performance gains using the RAPIDS framework.",[],{},{"nodeType":1921,"data":17987,"content":17988},{},[17989,17993,18001,18005,18012,18016,18024],{"nodeType":1907,"value":17990,"marks":17991,"data":17992},"GDS can also be used to",[],{},{"nodeType":1916,"data":17994,"content":17996},{"uri":17995},"https:\u002F\u002Fdocs.lmcache.ai\u002Fkv_cache\u002Fgds.html",[17997],{"nodeType":1907,"value":17998,"marks":17999,"data":18000}," accelerate inference",[],{},{"nodeType":1907,"value":18002,"marks":18003,"data":18004}," in frameworks such as vLLM with LMCache by providing acceleration to offload KV Cache to external GDS capable storage. VAST is a ",[],{},{"nodeType":1916,"data":18006,"content":18007},{"uri":17995},[18008],{"nodeType":1907,"value":18009,"marks":18010,"data":18011},"contributor",[],{},{"nodeType":1907,"value":18013,"marks":18014,"data":18015}," to this effort in the LMCache project. This functionality can also be used to accelerate inference in the",[],{},{"nodeType":1916,"data":18017,"content":18019},{"uri":18018},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-to-reduce-kv-cache-bottlenecks-with-nvidia-dynamo\u002F",[18020],{"nodeType":1907,"value":18021,"marks":18022,"data":18023}," NVIDIA Dynamo LMCache integration framework",[],{},{"nodeType":1907,"value":1920,"marks":18025,"data":18026},[],{},{"nodeType":12289,"data":18028,"content":18048},{"target":18029},{"metadata":18030,"sys":18033,"fields":18040},{"tags":18031,"concepts":18032},[],[],{"space":18034,"id":18036,"type":934,"createdAt":18037,"updatedAt":18037,"environment":18038,"publishedVersion":752,"revision":1136,"locale":673},{"sys":18035},{"type":657,"linkType":658,"id":659},"5uXdve4N8A9uJNG9Yseq7f","2026-02-19T09:46:20.490Z",{"sys":18039},{"id":665,"type":657,"linkType":666},{"title":18041,"description":17420,"file":18042},"NVIDIA GPUDirect Storage (GDS): The VAST Data Story - Tested State",{"url":18043,"details":18044,"fileName":18047,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5uXdve4N8A9uJNG9Yseq7f\u002F8f7f3d4a1d8cd1a4c771e1934d9f0e6e\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story-6.webp",{"size":18045,"image":18046},34608,{"width":1144,"height":1145},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story-6.webp",[],{"nodeType":1921,"data":18050,"content":18051},{},[18052],{"nodeType":1907,"value":18053,"marks":18054,"data":18055},"The reader is encouraged to study the references provided in this section to determine where benefit could be derived.",[],{},{"nodeType":1921,"data":18057,"content":18058},{},[18059],{"nodeType":1907,"value":18060,"marks":18061,"data":18062},"The interest in this from our customer base is unprecedented. Many of our customers who have purchased DGX A100 systems are planning to use them with VAST systems, with or without GDS, as VAST Data offers a scalable, highly performant, and cost-effective platform to base their Deep Learning workloads on, without any compromises.",[],{},[18064,18536,18938],{"metadata":18065,"sys":18081,"fields":18091},{"tags":18066,"concepts":18080},[18067,18070,18072,18074,18076,18078],{"sys":18068},{"type":657,"linkType":1502,"id":18069},"enterprise_backup_and_recovery",{"sys":18071},{"type":657,"linkType":1502,"id":12740},{"sys":18073},{"type":657,"linkType":1502,"id":12743},{"sys":18075},{"type":657,"linkType":1502,"id":13704},{"sys":18077},{"type":657,"linkType":1502,"id":1081},{"sys":18079},{"type":657,"linkType":1502,"id":12752},[],{"space":18082,"id":18084,"type":14,"createdAt":18085,"updatedAt":18086,"environment":18087,"publishedVersion":1095,"revision":792,"contentType":18089,"locale":673},{"sys":18083},{"type":657,"linkType":658,"id":659},"6t3HjnRuTmQJDf6tpRFujo","2022-09-15T16:22:46.995Z","2025-03-17T19:23:27.815Z",{"sys":18088},{"id":665,"type":657,"linkType":666},{"sys":18090},{"type":657,"linkType":671,"id":12765},{"title":18092,"slug":18093,"publishedDate":18094,"author":18095,"hero":18096,"thumbnailImage":18112,"middleContent":18147,"socialIcons":18374,"hidden":19,"seo":18500},"How Affordable All-Flash Storage is Changing Backup & Restore","how-affordable-all-flash-storage-is-changing-backup-restore","2022-08-29","George Axberg",{"metadata":18097,"sys":18100,"fields":18110},{"tags":18098,"concepts":18099},[],[],{"space":18101,"id":18103,"type":14,"createdAt":18104,"updatedAt":18105,"environment":18106,"publishedVersion":668,"revision":847,"contentType":18108,"locale":673},{"sys":18102},{"type":657,"linkType":658,"id":659},"Mj1koOuU10R8qLtBlE2Xr","2022-09-15T16:21:44.126Z","2023-02-12T00:24:48.983Z",{"sys":18107},{"id":665,"type":657,"linkType":666},{"sys":18109},{"type":657,"linkType":671,"id":1186},{"entryTitle":18111,"title":18092,"textAlignment":5376,"alignImageRight":19,"showSubscriptionForm":19},"Blog - How Affordable All-Flash Storage is Changing Backup & Restore",{"metadata":18113,"sys":18116,"fields":18126},{"tags":18114,"concepts":18115},[],[],{"space":18117,"id":18119,"type":14,"createdAt":18120,"updatedAt":18121,"environment":18122,"publishedVersion":692,"revision":901,"contentType":18124,"locale":673},{"sys":18118},{"type":657,"linkType":658,"id":659},"4AJGmg9dR7fC55QmIlBa1a","2023-05-24T03:31:32.790Z","2023-09-05T15:47:41.497Z",{"sys":18123},{"id":665,"type":657,"linkType":666},{"sys":18125},{"type":657,"linkType":671,"id":12173},{"entryTitle":18092,"image":18127},{"metadata":18128,"sys":18131,"fields":18139},{"tags":18129,"concepts":18130},[],[],{"space":18132,"id":18134,"type":934,"createdAt":18135,"updatedAt":18136,"environment":18137,"publishedVersion":865,"revision":901,"locale":673},{"sys":18133},{"type":657,"linkType":658,"id":659},"41254VptV9jxzfKd7ruDBJ","2023-05-23T03:37:59.889Z","2023-05-23T03:42:31.193Z",{"sys":18138},{"id":665,"type":657,"linkType":666},{"title":18140,"description":2031,"file":18141},"how-affordable-all-flash-storage-is-changing-backup-and-restore",{"url":18142,"details":18143,"fileName":18146,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F41254VptV9jxzfKd7ruDBJ\u002F948ca68d742788be9144244a597a3d7c\u002FAll-Flash-Backup-Restore-cover.jpg",{"size":18144,"image":18145},962622,{"width":1144,"height":1145},"All-Flash-Backup-Restore-cover.jpg",{"data":18148,"content":18149,"nodeType":1922},{},[18150,18157,18164,18171,18178,18185,18192,18248,18255,18274,18302,18309,18316,18323,18342,18349,18356],{"data":18151,"content":18152,"nodeType":1921},{},[18153],{"data":18154,"marks":18155,"value":18156,"nodeType":1907},{},[],"It seems like every week another ransomware attack against a major company is in the news—and those are only the attacks announced publicly. Gartner expects at least 75% of IT organizations to face one or more ransomware attacks by 2025. And many of the IT leaders I meet operate with a “not if or when, but how many” mindset.",{"data":18158,"content":18159,"nodeType":1921},{},[18160],{"data":18161,"marks":18162,"value":18163,"nodeType":1907},{},[],"It used to be enough for organizations to develop a hardened network perimeter that would satisfy security concerns, but the interconnectedness of today’s various on-prem, hybrid cloud, and SaaS applications present a massive opportunity for bad actors to infiltrate your network.",{"data":18165,"content":18166,"nodeType":1921},{},[18167],{"data":18168,"marks":18169,"value":18170,"nodeType":1907},{},[],"That means the primary DR consideration for almost any organization is how fast you can recover. Having backup data is only part of the equation. You must ensure you can quickly restore those backups – and this is where legacy backup targets cannot help you.",{"data":18172,"content":18173,"nodeType":17480},{},[18174],{"data":18175,"marks":18176,"value":18177,"nodeType":1907},{},[],"The Evolving Nature of Restores",{"data":18179,"content":18180,"nodeType":1921},{},[18181],{"data":18182,"marks":18183,"value":18184,"nodeType":1907},{},[],"We used to think about RTO objectives in hourly timeframes, but today’s sophisticated attacks that lock up entire IT estates have shifted such intervals to weeks, if not months. And while the nature of ransomware attacks is well understood, the path to recovery is less clear. Some organizations will pay the attackers the ransom to (presumably) regain access to their data, while others may choose to immediately start restoring data to their last known non-infected state. If you can do the latter well, you can save your organization massive ransom payouts, and more importantly, protect yourself from future attacks.",{"data":18186,"content":18187,"nodeType":1921},{},[18188],{"data":18189,"marks":18190,"value":18191,"nodeType":1907},{},[],"The path to fast restores is typically dependent on a few variables:",{"data":18193,"content":18194,"nodeType":18247},{},[18195,18205,18215,18237],{"data":18196,"content":18197,"nodeType":12451},{},[18198],{"data":18199,"content":18200,"nodeType":1921},{},[18201],{"data":18202,"marks":18203,"value":18204,"nodeType":1907},{},[],"Your network topology, or the path your data takes to move from source to destination and the time it needs to get there. Organizations often optimize bandwidth through data reduction, load balancing, throttling, etc.",{"data":18206,"content":18207,"nodeType":12451},{},[18208],{"data":18209,"content":18210,"nodeType":1921},{},[18211],{"data":18212,"marks":18213,"value":18214,"nodeType":1907},{},[],"Your number of data movers (physical or virtual), i.e. the backup agents running on your servers. Increasing the number of data movers in a system enables multi-streamed restores.",{"data":18216,"content":18217,"nodeType":12451},{},[18218],{"data":18219,"content":18220,"nodeType":1921},{},[18221,18225,18233],{"data":18222,"marks":18223,"value":18224,"nodeType":1907},{},[],"Your landing zone, or a clean network destination for the recovery of your immutable data, or ",{"data":18226,"content":18228,"nodeType":1916},{"uri":18227},"..\u002F..\u002Fblog\u002Fmaking-data-indestructible\u002F",[18229],{"data":18230,"marks":18231,"value":18232,"nodeType":1907},{},[],"indestructible snapshots",{"data":18234,"marks":18235,"value":18236,"nodeType":1907},{},[],", in the event of an attack.",{"data":18238,"content":18239,"nodeType":12451},{},[18240],{"data":18241,"content":18242,"nodeType":1921},{},[18243],{"data":18244,"marks":18245,"value":18246,"nodeType":1907},{},[],"Your data protection target. In enterprise data centers this is most often the bottleneck, as traditional backup appliances read (restore) data about 20% as fast as they write (backup) data. Therefore, even if you do everything else right (steps 1-3 above), restores are only as fast as the read performance of the backup target.","ordered-list",{"data":18249,"content":18250,"nodeType":1921},{},[18251],{"data":18252,"marks":18253,"value":18254,"nodeType":1907},{},[],"Purpose-built backup appliances (PBBAs) simply cannot deliver strong read performance. While they can write data quickly to disk and deduplicate it to optimize for capacity, these same characteristics inherently limit them from restoring large-scale data sets quickly, which is the name of the game in the ransomware era. PBBAs are a bit like Hotel California in that way: easy to get data in for a backup, but hard to get it out for a restore.",{"data":18256,"content":18257,"nodeType":1921},{},[18258,18262,18270],{"data":18259,"marks":18260,"value":18261,"nodeType":1907},{},[],"In fact, the smaller the system block size (Dell EMC PowerProtect\u002FData Domain has variable block lengths between 4kb and 8kb, for example) the more individual blocks that must be rehydrated via random IO when restoring data. Disk doesn’t do random IO well, thanks to time-consuming disk head movement. (Read more about the “rehydration tax” in conventional storage systems in our ",{"data":18263,"content":18265,"nodeType":1916},{"uri":18264},"..\u002F..\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fall-data-reduction-is-not-created-equal",[18266],{"data":18267,"marks":18268,"value":18269,"nodeType":1907},{},[],"data reduction white paper",{"data":18271,"marks":18272,"value":18273,"nodeType":1907},{},[],".) And when you consider that a large dataset was 500GB when PBBAs came to market in the early 2000s, it’s no wonder that today’s backups weigh heavily upon the limitations of the architecture. The result is that large, full system recoveries via legacy backup appliances can last weeks.",{"data":18275,"content":18276,"nodeType":1921},{},[18277,18281,18286,18290,18298],{"data":18278,"marks":18279,"value":18280,"nodeType":1907},{},[],"In fact, let’s consider it a modern data protection truth: ",{"data":18282,"marks":18283,"value":18285,"nodeType":1907},{},[18284],{"type":13576},"deduplication on hard drives will never work for fast restores",{"data":18287,"marks":18288,"value":18289,"nodeType":1907},{},[],". It’s for this reason that Data Domain and other legacy PBBAs never discuss restore performance in their spec sheets, leaving customers to discover their recovery limitations at the worst possible times. 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and multi-turn AI conversations create significant amounts of intermediate key-value data that is used by inference applications. Because GPU memory is a precious resource, these results cannot be kept forever in GPU memory, thus creating a memory wall where not all intermediate data can be kept in GPU memory. ",{"data":20246,"content":20247,"nodeType":12451},{},[20248],{"data":20249,"content":20250,"nodeType":1921},{},[20251],{"data":20252,"marks":20253,"value":20254,"nodeType":1907},{},[],"The NVIDIA Dynamo inference framework is bringing novel capabilities to market such as key-value cache (KV$ in short) offload",{"data":20256,"content":20257,"nodeType":12451},{},[20258],{"data":20259,"content":20260,"nodeType":1921},{},[20261],{"data":20262,"marks":20263,"value":20264,"nodeType":1907},{},[],"KV$ offload enables the moving of GPU memory states from GPU memory to lower-tiers of memory or storage, enabling GPUs to pre-load past conversations or cache frequent prompts, avoiding the expensive recompute operation (aka: prefill) normally required to load large context into GPU memory",{"data":20266,"content":20267,"nodeType":12451},{},[20268],{"data":20269,"content":20270,"nodeType":1921},{},[20271],{"data":20272,"marks":20273,"value":20274,"nodeType":1907},{},[],"The KV$ offload upside is measured in terms of accelerated time-to-first-token (TTFT) and the savings that come from not needing GPU cycles for prefill, thanks to the data already being cached",{"data":20276,"content":20277,"nodeType":12451},{},[20278],{"data":20279,"content":20280,"nodeType":1921},{},[20281],{"data":20282,"marks":20283,"value":20284,"nodeType":1907},{},[],"Both NVIDIA Dynamo and the VAST AI Operating System are designed to move data at the speed of light (or the speed of the network in this case…)",{"data":20286,"content":20287,"nodeType":12451},{},[20288],{"data":20289,"content":20290,"nodeType":1921},{},[20291],{"data":20292,"marks":20293,"value":20294,"nodeType":1907},{},[],"This blog post covers sizing and architecture considerations when building for large-scale inference infrastructure that is enabled for KV$ offload",{"data":20296,"content":20297,"nodeType":12451},{},[20298],{"data":20299,"content":20300,"nodeType":1921},{},[20301,20305,20312,20316,20321,20324,20329],{"data":20302,"marks":20303,"value":20304,"nodeType":1907},{},[],"VAST collaborated with CoreWeave to evaluate ",{"data":20306,"content":20307,"nodeType":1916},{"uri":18018},[20308],{"data":20309,"marks":20310,"value":20311,"nodeType":1907},{},[],"NVIDIA’s Dynamo KV$",{"data":20313,"marks":20314,"value":20315,"nodeType":1907},{},[]," offload with the VAST AI OS and saw ",{"data":20317,"marks":20318,"value":20320,"nodeType":1907},{},[20319],{"type":13576},"GPU efficiency improve by 90%",{"data":20322,"marks":20323,"value":13487,"nodeType":1907},{},[],{"data":20325,"marks":20326,"value":20328,"nodeType":1907},{},[20327],{"type":13576},"TTFT improve by 20x",{"data":20330,"marks":20331,"value":20332,"nodeType":1907},{},[]," as compared to when GPUs need to prefill calculate every past conversation",{"data":20334,"content":20335,"nodeType":12451},{},[20336],{"data":20337,"content":20338,"nodeType":1921},{},[20339],{"data":20340,"marks":20341,"value":20342,"nodeType":1907},{},[],"Dynamo data persistence now relies on data batching for large asynchronous writes, and bulk synchronous reads - resulting in an IO profile focused on storage throughput, not IOPS and low latency",{"data":20344,"content":20345,"nodeType":12451},{},[20346],{"data":20347,"content":20348,"nodeType":1921},{},[20349,20353,20360],{"data":20350,"marks":20351,"value":20352,"nodeType":1907},{},[],"Ultimately, VAST’s testing identified that most KV$ offload operations are not storage-limited, but rather they are network-limited. For additional performance improvements, the NVIDIA Spectrum-X Ethernet fabric accelerates AI Storage fabrics by removing performance bottlenecks common to off the shelf (OTS) Ethernet solutions. VAST has integrated with NVIDIA Spectrum-X - more details ",{"data":20354,"content":20355,"nodeType":1916},{"uri":19897},[20356],{"data":20357,"marks":20358,"value":20359,"nodeType":1907},{},[],"here",{"data":20361,"marks":20362,"value":1920,"nodeType":1907},{},[],{"data":20364,"content":20365,"nodeType":20366},{},[],"hr",{"data":20368,"content":20369,"nodeType":1921},{},[20370],{"data":20371,"marks":20372,"value":20373,"nodeType":1907},{},[],"For the past few months, the VAST and NVIDIA engineering teams have been collaborating on improving NVIDIA NIXL and NVIDIA Dynamo. The following analysis reflects the software enhancements we've worked on that focus on aggregating and accessing KV data in large sequentially-accessed blocks - using new approaches to data storage and access such as KVBM block queuing, transfer list management and support for multi-file layouts. ",{"data":20375,"content":20376,"nodeType":1921},{},[20377,20381,20388],{"data":20378,"marks":20379,"value":20380,"nodeType":1907},{},[],"Previously, we wrote about ",{"data":20382,"content":20383,"nodeType":1916},{"uri":19449},[20384],{"data":20385,"marks":20386,"value":20387,"nodeType":1907},{},[],"accelerating inference and the AI data pipeline",{"data":20389,"marks":20390,"value":20391,"nodeType":1907},{},[]," where we introduced the critical challenge of KV cache (KV$) management for LLMs. We surveyed the landscape of offloading solutions, including LMCache, vLLM, and NVIDIA Dynamo KVBM, and shared compelling results from our own experiments. Those tests, using NIXL and NVIDIA GPUDirect Storage (GDS) and vLLM\u002FLMCache integration, demonstrated the massive potential of offloading to KV$ VAST, showing a 10× improvement in prefill time for large-context scenarios.",{"data":20393,"content":20394,"nodeType":1921},{},[20395,20399,20405,20408,20416,20420,20428],{"data":20396,"marks":20397,"value":20398,"nodeType":1907},{},[],"Today, we're excited to share the next major step in this journey: the collaboration and integration between the ",{"data":20400,"content":20401,"nodeType":1916},{"uri":11592},[20402],{"data":20403,"marks":20404,"value":1123,"nodeType":1907},{},[],{"data":20406,"marks":20407,"value":13487,"nodeType":1907},{},[],{"data":20409,"content":20411,"nodeType":1916},{"uri":20410},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fdynamo\u002F",[20412],{"data":20413,"marks":20414,"value":20415,"nodeType":1907},{},[],"NVIDIA Dynamo",{"data":20417,"marks":20418,"value":20419,"nodeType":1907},{},[],". This article will detail Dynamo's sophisticated ",{"data":20421,"content":20423,"nodeType":1916},{"uri":20422},"https:\u002F\u002Fdocs.nvidia.com\u002Fdynamo\u002Farchive\u002F0.2.0\u002Farchitecture\u002Fkv_cache_manager.html",[20424],{"data":20425,"marks":20426,"value":20427,"nodeType":1907},{},[],"architecture",{"data":20429,"marks":20430,"value":20431,"nodeType":1907},{},[]," and show how the VAST AI OS is now integrated as a foundational, high-performance capacity solution to solve the GPU memory wall.",{"data":20433,"content":20434,"nodeType":12267},{},[20435],{"data":20436,"marks":20437,"value":20438,"nodeType":1907},{},[],"What is NVIDIA Dynamo?",{"data":20440,"content":20441,"nodeType":1921},{},[20442,20446,20453],{"data":20443,"marks":20444,"value":20445,"nodeType":1907},{},[],"Before we dive into the problem, let's briefly introduce ",{"data":20447,"content":20449,"nodeType":1916},{"uri":20448},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fwhy-everyones-talking-about-nvidia-dynamo-and-why-it-actually-matters",[20450],{"data":20451,"marks":20452,"value":20415,"nodeType":1907},{},[],{"data":20454,"marks":20455,"value":20456,"nodeType":1907},{},[],". 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As a conversation or document gets longer, the cache size grows, consuming precious high-bandwidth memory (HBM) on the GPU. This creates a hard limit, forcing a difficult choice between supporting long context for a few users or short context for many. Expensive HBM becomes the bottleneck for scaling inference.",{"data":20511,"content":20512,"nodeType":12267},{},[20513],{"data":20514,"marks":20515,"value":20516,"nodeType":1907},{},[],"The Solution: A New Caching Architecture",{"data":20518,"content":20519,"nodeType":1921},{},[20520],{"data":20521,"marks":20522,"value":20523,"nodeType":1907},{},[],"NVIDIA Dynamo's KV Block Manager (KVBM) provides an elegant solution to this problem by creating a sophisticated, multi-tiered system for intelligent memory management. This architecture is designed to place data in the right tier at the right time, balancing speed, capacity, and cost. Understanding the complete vision is key to seeing how VAST fits in. ",{"data":20525,"content":20526,"nodeType":1921},{},[20527],{"data":20528,"marks":20529,"value":20530,"nodeType":1907},{},[],"Here is the four-tiered architecture as defined by NVIDIA:",{"data":20532,"content":20533,"nodeType":12447},{},[20534,20549,20564,20579],{"data":20535,"content":20536,"nodeType":12451},{},[20537],{"data":20538,"content":20539,"nodeType":1921},{},[20540,20545],{"data":20541,"marks":20542,"value":20544,"nodeType":1907},{},[20543],{"type":13576},"G1: Device Memory (GPU HBM):",{"data":20546,"marks":20547,"value":20548,"nodeType":1907},{},[]," The “hot” tier. This is the fastest and most expensive memory, residing directly on the GPU. 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It is designed for high-performance, massive-scale offloading when the upper tiers are full. VAST provides a shared, scale-out pool of capacity that many GPU nodes can access. In its current implementation, this G3 tier is ephemeral; it serves as a powerful temporary storage layer to support extremely long context windows for the duration of the service's uptime.",{"data":20580,"content":20581,"nodeType":12451},{},[20582],{"data":20583,"content":20584,"nodeType":1921},{},[20585,20590],{"data":20586,"marks":20587,"value":20589,"nodeType":1907},{},[20588],{"type":13576},"G4: Shared Network Storage:",{"data":20591,"marks":20592,"value":20593,"nodeType":1907},{},[]," This final tier is long-term persistence. This tier allows sessions to be saved permanently and then reloaded hours or days later, even after a full GPU system restart. 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By integrating into the G3 high-capacity cache, the VAST AI OS provides the critical, scalable capacity needed to make use of massive context windows a reality in production inference environments today.",{"data":20602,"content":20622,"nodeType":12289},{"target":20603},{"metadata":20604,"sys":20607,"fields":20614},{"tags":20605,"concepts":20606},[],[],{"space":20608,"id":20610,"type":934,"createdAt":20611,"updatedAt":20611,"environment":20612,"publishedVersion":847,"revision":1136,"locale":673},{"sys":20609},{"type":657,"linkType":658,"id":659},"3rd6P1QQmnIepmPq1YkZd1","2026-01-14T12:20:42.908Z",{"sys":20613},{"id":665,"type":657,"linkType":666},{"title":20186,"description":20186,"file":20615},{"url":20616,"details":20617,"fileName":20621,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3rd6P1QQmnIepmPq1YkZd1\u002F5d441ebfa62d8cefe45cd048c47ddeb1\u002Fnvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",{"size":20618,"image":20619},46764,{"width":1144,"height":20620},781,"nvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",[],{"data":20624,"content":20625,"nodeType":1921},{},[20626,20631,20638],{"data":20627,"marks":20628,"value":20630,"nodeType":1907},{},[20629],{"type":12260},"Dynamo architecture ",{"data":20632,"content":20633,"nodeType":1916},{"uri":20422},[20634],{"data":20635,"marks":20636,"value":20422,"nodeType":1907},{},[20637],{"type":12260},{"data":20639,"marks":20640,"value":2031,"nodeType":1907},{},[],{"data":20642,"content":20643,"nodeType":20366},{},[],{"data":20645,"content":20646,"nodeType":12267},{},[20647],{"data":20648,"marks":20649,"value":20650,"nodeType":1907},{},[],"Architecting For KV$ Offload",{"data":20652,"content":20653,"nodeType":19541},{},[20654],{"data":20655,"marks":20656,"value":20657,"nodeType":1907},{},[],"General Architecture Considerations",{"data":20659,"content":20660,"nodeType":18247},{},[20661,20671,20681,20691],{"data":20662,"content":20663,"nodeType":12451},{},[20664],{"data":20665,"content":20666,"nodeType":1921},{},[20667],{"data":20668,"marks":20669,"value":20670,"nodeType":1907},{},[],"KV$ is stored in large blocks, making KV$ offload a workload that demands high throughput",{"data":20672,"content":20673,"nodeType":12451},{},[20674],{"data":20675,"content":20676,"nodeType":1921},{},[20677],{"data":20678,"marks":20679,"value":20680,"nodeType":1907},{},[],"The network and supporting storage subsystems must scale with\u002Fto the count of GPUs",{"data":20682,"content":20683,"nodeType":12451},{},[20684],{"data":20685,"content":20686,"nodeType":1921},{},[20687],{"data":20688,"marks":20689,"value":20690,"nodeType":1907},{},[],"Each host’s network access to storage will determine its TTFT performance",{"data":20692,"content":20693,"nodeType":12451},{},[20694],{"data":20695,"content":20696,"nodeType":1921},{},[20697],{"data":20698,"marks":20699,"value":20700,"nodeType":1907},{},[],"KV$ carries conversation information and should be encrypted using an encryption key per-customer, following the best practices of securing user information",{"data":20702,"content":20703,"nodeType":19541},{},[20704],{"data":20705,"marks":20706,"value":20707,"nodeType":1907},{},[],"VAST Data Architecture Considerations",{"data":20709,"content":20710,"nodeType":18247},{},[20711,20732,20754],{"data":20712,"content":20713,"nodeType":12451},{},[20714],{"data":20715,"content":20716,"nodeType":1921},{},[20717,20721,20729],{"data":20718,"marks":20719,"value":20720,"nodeType":1907},{},[],"Any VAST cluster supports KV$ offload today via Dynamo’s transfer library (",{"data":20722,"content":20724,"nodeType":1916},{"uri":20723},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fnixl",[20725],{"data":20726,"marks":20727,"value":20728,"nodeType":1907},{},[],"NIXL",{"data":20730,"marks":20731,"value":20476,"nodeType":1907},{},[],{"data":20733,"content":20734,"nodeType":12451},{},[20735],{"data":20736,"content":20737,"nodeType":1921},{},[20738,20742,20750],{"data":20739,"marks":20740,"value":20741,"nodeType":1907},{},[],"Use the ",{"data":20743,"content":20745,"nodeType":1916},{"uri":20744},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002FIntro.html",[20746],{"data":20747,"marks":20748,"value":20749,"nodeType":1907},{},[],"VAST NFS",{"data":20751,"marks":20752,"value":20753,"nodeType":1907},{},[]," driver, to enable multipathing to saturate the IO needs of any GPU machine",{"data":20755,"content":20756,"nodeType":12451},{},[20757],{"data":20758,"content":20759,"nodeType":1921},{},[20760],{"data":20761,"marks":20762,"value":20763,"nodeType":1907},{},[],"While VAST systems can saturate any client’s I\u002FO needs, even on a lossy network, when using NFS-over-TCP, VAST recommends mounting VAST clusters over RoCE (RDMA over converged Ethernet) for optimizing latency, host CPU utilization, and unlocking GPUDirect Storage for direct KV$ retrieval from storage into GPU memory.",{"data":20765,"content":20766,"nodeType":20366},{},[],{"data":20768,"content":20769,"nodeType":12267},{},[20770],{"data":20771,"marks":20772,"value":20773,"nodeType":1907},{},[],"Calculating KV$ Footprint",{"data":20775,"content":20776,"nodeType":1921},{},[20777],{"data":20778,"marks":20779,"value":20780,"nodeType":1907},{},[],"KV$ creates gigabytes of data per prompt and is strongly dependent on model parameters:",{"data":20782,"content":20783,"nodeType":1921},{},[20784],{"data":20785,"marks":20786,"value":20787,"nodeType":1907},{},[],"Let’s take Llama 3.1-405B as an example for determining the size of the KV$ for a single token using standard parameters (126 layers, 8 KV heads, 128 head dimension, and FP16 storage):",{"data":20789,"content":20790,"nodeType":1921},{},[20791],{"data":20792,"marks":20793,"value":20794,"nodeType":1907},{},[],"Size per Token = 126 * 2 ( for Keys and Values)  * 8 * 128 * 2 (bytes) = 516,096 bytes",{"data":20796,"content":20797,"nodeType":1921},{},[20798,20802,20807],{"data":20799,"marks":20800,"value":20801,"nodeType":1907},{},[],"This results in approximately ",{"data":20803,"marks":20804,"value":20806,"nodeType":1907},{},[20805],{"type":13576},"504 KiB per token",{"data":20808,"marks":20809,"value":20810,"nodeType":1907},{},[],". For a single user utilizing a full 127,188-token context window, the cache becomes massive:",{"data":20812,"content":20813,"nodeType":1921},{},[20814,20818,20822],{"data":20815,"marks":20816,"value":20817,"nodeType":1907},{},[],"504KiB\u002Ftoken * 127,188 tokens ≈",{"data":20819,"marks":20820,"value":12343,"nodeType":1907},{},[20821],{"type":13576},{"data":20823,"marks":20824,"value":20825,"nodeType":1907},{},[]," 61 GB",{"data":20827,"content":20828,"nodeType":12267},{},[20829],{"data":20830,"marks":20831,"value":20833,"nodeType":1907},{},[20832],{"type":13576},"Modeling the \"Data Dump\" Scenarios",{"data":20835,"content":20836,"nodeType":1921},{},[20837],{"data":20838,"marks":20839,"value":20840,"nodeType":1907},{},[],"While it helps to have CPU memory and local SSDs to store data into, the large amounts of KV$ data created per each user session begs the need for scalable low cost storage infrastructure. The purpose of the G4 tier is to support massive contexts and serve them back with low latency to multi-turn inference sessions running on multiple GPU nodes. KV$ retrievals spawn low-TTFT, high-throughput read events rather than a gentle trickle of data. \n\nConsider these scenarios:",{"data":20842,"content":20843,"nodeType":12447},{},[20844,20859],{"data":20845,"content":20846,"nodeType":12451},{},[20847],{"data":20848,"content":20849,"nodeType":1921},{},[20850,20855],{"data":20851,"marks":20852,"value":20854,"nodeType":1907},{},[20853],{"type":13576},"The 'Power User' Returns:",{"data":20856,"marks":20857,"value":20858,"nodeType":1907},{},[]," A user accumulates 128,000 tokens worth of context (~61 GB) and pauses. Their session is evicted to VAST to free up memory space on a host to service other users. Upon the return of the original user, the system must rehydrate 61GB of KV$ back into a machine’s GPU memory. This is a 100% cache hit event.",{"data":20860,"content":20861,"nodeType":12451},{},[20862],{"data":20863,"content":20864,"nodeType":1921},{},[20865,20870],{"data":20866,"marks":20867,"value":20869,"nodeType":1907},{},[20868],{"type":13576},"The RAG Workflow:",{"data":20871,"marks":20872,"value":20873,"nodeType":1907},{},[]," An analyst uploads 64,000 tokens worth of documents (~30 GB). Every subsequent question triggers a reload of that 30GB base cache, resulting in >90% cache hits per prompt. Queries which reference the same document set but come from a different analyst will also enjoy a 90% cache hit per prompt.",{"data":20875,"content":20876,"nodeType":20366},{},[],{"data":20878,"content":20879,"nodeType":19541},{},[20880],{"data":20881,"marks":20882,"value":20883,"nodeType":1907},{},[],"How Much Capacity Do You Need?",{"data":20885,"content":20886,"nodeType":1921},{},[20887],{"data":20888,"marks":20889,"value":20890,"nodeType":1907},{},[],"The following is an example formula that can guide customers in sizing for KV$:",{"data":20892,"content":20893,"nodeType":1921},{},[20894,20899],{"data":20895,"marks":20896,"value":20898,"nodeType":1907},{},[20897],{"type":13576},"Total Capacity = ",{"data":20900,"marks":20901,"value":20902,"nodeType":1907},{},[],"Number of Users * Average KV$ per User * Retention Multiplier",{"data":20904,"content":20905,"nodeType":1921},{},[20906],{"data":20907,"marks":20908,"value":20909,"nodeType":1907},{},[],"The retention multiplier is the number of past conversations that are stored for a user.  Let's use our Llama 3.1 405B example, and plan for 100,000 concurrent users or agents:",{"data":20911,"content":20912,"nodeType":12447},{},[20913,20937,20952],{"data":20914,"content":20915,"nodeType":12451},{},[20916],{"data":20917,"content":20918,"nodeType":1921},{},[20919,20924,20928,20933],{"data":20920,"marks":20921,"value":20923,"nodeType":1907},{},[20922],{"type":13576},"Average KV$ per Prompt:",{"data":20925,"marks":20926,"value":20927,"nodeType":1907},{},[]," As large context windows (e.g., 128,000 tokens) are becoming standard for tasks like document analysis or long-running conversations, the cache size per user balloons. A 128k-token context is roughly 61 GB in size. Let's assume a more typical ",{"data":20929,"marks":20930,"value":20932,"nodeType":1907},{},[20931],{"type":12260},"average",{"data":20934,"marks":20935,"value":20936,"nodeType":1907},{},[]," for a prompt is 64K tokens, roughly 30 GB",{"data":20938,"content":20939,"nodeType":12451},{},[20940],{"data":20941,"content":20942,"nodeType":1921},{},[20943,20948],{"data":20944,"marks":20945,"value":20947,"nodeType":1907},{},[20946],{"type":13576},"Retention Multiplier:",{"data":20949,"marks":20950,"value":20951,"nodeType":1907},{},[]," We'll use 15 to provide a good user experience",{"data":20953,"content":20954,"nodeType":12451},{},[20955],{"data":20956,"content":20957,"nodeType":1921},{},[20958,20963,20967],{"data":20959,"marks":20960,"value":20962,"nodeType":1907},{},[20961],{"type":13576},"Calculation:",{"data":20964,"marks":20965,"value":20966,"nodeType":1907},{},[]," 100,000 users * 30 GB * 15 = 45,000,000 GB = ",{"data":20968,"marks":20969,"value":20971,"nodeType":1907},{},[20970],{"type":13576},"45 PB",{"data":20973,"content":20974,"nodeType":1921},{},[20975],{"data":20976,"marks":20977,"value":20978,"nodeType":1907},{},[],"To support 100,000 users\u002Fagents on a large-context model with a robust conversation history, you need 45 petabytes of storage. ",{"data":20980,"content":20981,"nodeType":20366},{},[],{"data":20983,"content":20984,"nodeType":12267},{},[20985],{"data":20986,"marks":20987,"value":20988,"nodeType":1907},{},[],"Understanding the KV$ I\u002FO Profile",{"data":20990,"content":20991,"nodeType":1921},{},[20992,20997],{"data":20993,"marks":20994,"value":20996,"nodeType":1907},{},[20995],{"type":13576},"Asynchronous Writes (Eviction):",{"data":20998,"marks":20999,"value":21000,"nodeType":1907},{},[]," When the GPU HBM (G1) and CPU Memory (G2) tiers fill up, the KVBM evicts less-active cache blocks to the storage tier. This operation is asynchronous; it does not happen in the immediate, real-time path of generating the next token. This means the system can intelligently batch these writes into larger, more efficient operations. There is no strict, real-time latency or high-IOPS requirement for this eviction process.",{"data":21002,"content":21003,"nodeType":1921},{},[21004],{"data":21005,"marks":21006,"value":21007,"nodeType":1907},{},[],"The offload block size granularity is controlled on the Dynamo framework level. Currently, it mirrors the inference engine block size, usually 16 or 32 tokens, but can be enhanced to increase to whatever IO size we want. Upon eviction, the KVBM writes the all of the model layers of the block size, which amounts to:",{"data":21009,"content":21010,"nodeType":1921},{},[21011,21016],{"data":21012,"marks":21013,"value":21015,"nodeType":1907},{},[21014],{"type":13576},"Total Cache per Token (all 126 layers):",{"data":21017,"marks":21018,"value":21019,"nodeType":1907},{},[]," 504 KiB",{"data":21021,"content":21022,"nodeType":1921},{},[21023,21028],{"data":21024,"marks":21025,"value":21027,"nodeType":1907},{},[21026],{"type":13576},"Total Cache per Block (16 tokens):",{"data":21029,"marks":21030,"value":21031,"nodeType":1907},{},[]," 504 KiB\u002Ftoken * 16 tokens ≈ 8 MB",{"data":21033,"content":21034,"nodeType":1921},{},[21035,21040],{"data":21036,"marks":21037,"value":21039,"nodeType":1907},{},[21038],{"type":13576},"Data Written per GPU per Evicted Block (8 ways):",{"data":21041,"marks":21042,"value":21043,"nodeType":1907},{},[]," 8064 KiB \u002F 8 GPUs ≈ 1 MB ",{"data":21045,"content":21046,"nodeType":1921},{},[21047,21052,21056,21061,21065,21070],{"data":21048,"marks":21049,"value":21051,"nodeType":1907},{},[21050],{"type":13576},"Latency-Critical Reads (Retrieval)",{"data":21053,"marks":21054,"value":21055,"nodeType":1907},{},[],": When a user session\u002Fconversation is reactivated, the system must fetch the offloaded KV$. While modern inference engines utilize pipeline parallelism and batching to keep the GPU compute units active with ",{"data":21057,"marks":21058,"value":21060,"nodeType":1907},{},[21059],{"type":12260},"other",{"data":21062,"marks":21063,"value":21064,"nodeType":1907},{},[]," concurrent requests, the TTFT for the ",{"data":21066,"marks":21067,"value":21069,"nodeType":1907},{},[21068],{"type":12260},"specific",{"data":21071,"marks":21072,"value":21073,"nodeType":1907},{},[]," re-activated user\u002Fconversation is entirely bound by how fast this data can be retrieved.",{"data":21075,"content":21076,"nodeType":1921},{},[21077],{"data":21078,"marks":21079,"value":21080,"nodeType":1907},{},[],"Therefore, the goal is not to maximize IOPS (processing many small requests) and overly focus on latency, but to maximize throughput (delivering a massive stream of data instantly). AI inference systems need to stream GBs of data linearly into HBM to unblock that specific sequence. This is a bandwidth-intensive workload where the storage tier must deliver the entire KV$ at line-rate to minimize the response time perceived by the user.",{"data":21082,"content":21083,"nodeType":1921},{},[21084],{"data":21085,"marks":21086,"value":21088,"nodeType":1907},{},[21087],{"type":12260},"NOTE: At larger batch sizes (inference on multiple prompts at once for improved token serving efficiency - less power per token), KV$ load becomes an asynchronous operation, as the next batch is prepared in memory while the existing batch is running.",{"data":21090,"content":21091,"nodeType":20366},{},[],{"data":21093,"content":21094,"nodeType":12267},{},[21095],{"data":21096,"marks":21097,"value":21098,"nodeType":1907},{},[],"Real-World Performance: VAST and Dynamo in Action",{"data":21100,"content":21101,"nodeType":1921},{},[21102],{"data":21103,"marks":21104,"value":21105,"nodeType":1907},{},[],"Hardware:",{"data":21107,"content":21108,"nodeType":12447},{},[21109,21124,21139,21154,21169,21184,21199,21214],{"data":21110,"content":21111,"nodeType":12451},{},[21112],{"data":21113,"content":21114,"nodeType":1921},{},[21115,21120],{"data":21116,"marks":21117,"value":21119,"nodeType":1907},{},[21118],{"type":13576},"Client system:",{"data":21121,"marks":21122,"value":21123,"nodeType":1907},{},[]," NVIDIA HGX™ H100",{"data":21125,"content":21126,"nodeType":12451},{},[21127],{"data":21128,"content":21129,"nodeType":1921},{},[21130,21135],{"data":21131,"marks":21132,"value":21134,"nodeType":1907},{},[21133],{"type":13576},"GPUs:",{"data":21136,"marks":21137,"value":21138,"nodeType":1907},{},[]," 8x NVIDIA HGX H100 80GB HBM3",{"data":21140,"content":21141,"nodeType":12451},{},[21142],{"data":21143,"content":21144,"nodeType":1921},{},[21145,21150],{"data":21146,"marks":21147,"value":21149,"nodeType":1907},{},[21148],{"type":13576},"Network link",{"data":21151,"marks":21152,"value":21153,"nodeType":1907},{},[],": 2x100Gbit\u002Fsec Ethernet, Bluefied-3 integrated connectX",{"data":21155,"content":21156,"nodeType":12451},{},[21157],{"data":21158,"content":21159,"nodeType":1921},{},[21160,21165],{"data":21161,"marks":21162,"value":21164,"nodeType":1907},{},[21163],{"type":13576},"Software:",{"data":21166,"marks":21167,"value":21168,"nodeType":1907},{},[]," NVIDIA Dynamo pre-release 0.7.0 (Nov 21, 2025, modified) + vLLM v0.11.0 (modified) + NIXL 0.7.1",{"data":21170,"content":21171,"nodeType":12451},{},[21172],{"data":21173,"content":21174,"nodeType":1921},{},[21175,21180],{"data":21176,"marks":21177,"value":21179,"nodeType":1907},{},[21178],{"type":13576},"Model:",{"data":21181,"marks":21182,"value":21183,"nodeType":1907},{},[]," clowman\u002FLlama-3.1-405B-Instruct-AWQ-Int4",{"data":21185,"content":21186,"nodeType":12451},{},[21187],{"data":21188,"content":21189,"nodeType":1921},{},[21190,21195],{"data":21191,"marks":21192,"value":21194,"nodeType":1907},{},[21193],{"type":13576},"Run mode:",{"data":21196,"marks":21197,"value":21198,"nodeType":1907},{},[]," Tensor parallelism across 8 GPUs",{"data":21200,"content":21201,"nodeType":12451},{},[21202],{"data":21203,"content":21204,"nodeType":1921},{},[21205,21210],{"data":21206,"marks":21207,"value":21209,"nodeType":1907},{},[21208],{"type":13576},"Storage:",{"data":21211,"marks":21212,"value":21213,"nodeType":1907},{},[]," NFS (TCP, VAST NFS multipath, VAST server)",{"data":21215,"content":21216,"nodeType":12451},{},[21217],{"data":21218,"content":21219,"nodeType":1921},{},[21220,21225],{"data":21221,"marks":21222,"value":21224,"nodeType":1907},{},[21223],{"type":13576},"Prompt:",{"data":21226,"marks":21227,"value":21228,"nodeType":1907},{},[]," 127,188 tokens",{"data":21230,"content":21231,"nodeType":19541},{},[21232],{"data":21233,"marks":21234,"value":21235,"nodeType":1907},{},[],"Results",{"data":21237,"content":21238,"nodeType":1921},{},[21239,21243,21250,21254,21259,21263,21268],{"data":21240,"marks":21241,"value":21242,"nodeType":1907},{},[],"Collaborating on this configuration with our partner ",{"data":21244,"content":21246,"nodeType":1916},{"uri":21245},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Fcoreweave",[21247],{"data":21248,"marks":21249,"value":8535,"nodeType":1907},{},[],{"data":21251,"marks":21252,"value":21253,"nodeType":1907},{},[],", VAST consistently saturates the network during KV$ retrieval, sustaining ",{"data":21255,"marks":21256,"value":21258,"nodeType":1907},{},[21257],{"type":13576},"over 90% of line rate (181 Gbps)",{"data":21260,"marks":21261,"value":21262,"nodeType":1907},{},[]," and peaking at ",{"data":21264,"marks":21265,"value":21267,"nodeType":1907},{},[21266],{"type":13576},"~99% (198 Gbps)",{"data":21269,"marks":21270,"value":21271,"nodeType":1907},{},[]," on a 200 Gbps link. ",{"data":21273,"content":21292,"nodeType":12289},{"target":21274},{"metadata":21275,"sys":21278,"fields":21285},{"tags":21276,"concepts":21277},[],[],{"space":21279,"id":21281,"type":934,"createdAt":21282,"updatedAt":21282,"environment":21283,"publishedVersion":847,"revision":1136,"locale":673},{"sys":21280},{"type":657,"linkType":658,"id":659},"6DWr3VdK0ZzHJfYC0Wh4a6","2025-12-16T20:19:33.551Z",{"sys":21284},{"id":665,"type":657,"linkType":666},{"title":20186,"description":20186,"file":21286},{"url":21287,"details":21288,"fileName":21291,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWr3VdK0ZzHJfYC0Wh4a6\u002Ff41d8b35caefaba50dcc1245b279fb39\u002Fnvidia-dynamo-vast-scalable-optimized-inference-2.webp",{"size":21289,"image":21290},120100,{"width":1144,"height":1145},"nvidia-dynamo-vast-scalable-optimized-inference-2.webp",[],{"data":21294,"content":21295,"nodeType":1921},{},[21296,21301],{"data":21297,"marks":21298,"value":21300,"nodeType":1907},{},[21299],{"type":13576},"Note:",{"data":21302,"marks":21303,"value":21304,"nodeType":1907},{},[]," VAST’s NFS multipath driver can bind an arbitrary number of client interfaces into a single mount point, enabling extremely high aggregate throughput across enterprise environments.",{"data":21306,"content":21307,"nodeType":1921},{},[21308],{"data":21309,"marks":21310,"value":21311,"nodeType":1907},{},[],"As relates to the inference user experience, we observed the following performance: ",{"data":21313,"content":21314,"nodeType":12447},{},[21315,21325,21335,21345],{"data":21316,"content":21317,"nodeType":12451},{},[21318],{"data":21319,"content":21320,"nodeType":1921},{},[21321],{"data":21322,"marks":21323,"value":21324,"nodeType":1907},{},[],"TTFT with prefill compute: 62 seconds",{"data":21326,"content":21327,"nodeType":12451},{},[21328],{"data":21329,"content":21330,"nodeType":1921},{},[21331],{"data":21332,"marks":21333,"value":21334,"nodeType":1907},{},[],"TTFT with KV$ load from VAST: 3 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end-to-end",{"data":21356,"content":21375,"nodeType":12289},{"target":21357},{"metadata":21358,"sys":21361,"fields":21368},{"tags":21359,"concepts":21360},[],[],{"space":21362,"id":21364,"type":934,"createdAt":21365,"updatedAt":21365,"environment":21366,"publishedVersion":847,"revision":1136,"locale":673},{"sys":21363},{"type":657,"linkType":658,"id":659},"6iUYK5lLzibqm1bNATj4fU","2025-12-16T20:20:40.755Z",{"sys":21367},{"id":665,"type":657,"linkType":666},{"title":20186,"description":20186,"file":21369},{"url":21370,"details":21371,"fileName":21374,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6iUYK5lLzibqm1bNATj4fU\u002F7b12f6b83e6af8fc8cd42491a9b27389\u002Fnvidia-dynamo-vast-scalable-optimized-inference-3.webp",{"size":21372,"image":21373},83064,{"width":1144,"height":1145},"nvidia-dynamo-vast-scalable-optimized-inference-3.webp",[],{"data":21377,"content":21378,"nodeType":20366},{},[],{"data":21380,"content":21381,"nodeType":12267},{},[21382],{"data":21383,"marks":21384,"value":21385,"nodeType":1907},{},[],"Next 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The full walkthrough is available ",{"data":21394,"content":21396,"nodeType":1916},{"uri":21395},"https:\u002F\u002Fgithub.com\u002Fvast-data\u002Fdynamo\u002Fblob\u002Fh100-0.7.0-demo\u002Fdocs\u002Fguides\u002Fbenchmark_on_vast.md",[21397],{"data":21398,"marks":21399,"value":20359,"nodeType":1907},{},[],{"data":21401,"marks":21402,"value":1920,"nodeType":1907},{},[],{"data":21404,"content":21405,"nodeType":1921},{},[21406],{"data":21407,"marks":21408,"value":21409,"nodeType":1907},{},[],"In a future blog post, we’ll discuss how inference requirements evolve for enterprises that prioritize governance, security, and reliability. 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Data Management with VAST and NVIDIA Run:AI","simplifying-data-management-with-vast-and-nvidia-run-ai","2025-12-17","Ekin Karabulut, AI\u002FML Developer Advocate, NVIDIA | Erez Kirson, Senior Solutions Architect, NVIDIA | Oz Perry, Senior Solution Engineer, VAST 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data for AI workloads can be complex, but combining the VAST CSI driver and NVIDIA Run:AI’s data source abstraction makes the process dramatically simpler. ",[],{},{"nodeType":1921,"data":21684,"content":21685},{},[21686,21690,21699],{"nodeType":1907,"value":21687,"marks":21688,"data":21689},"Together, ",[],{},{"nodeType":1916,"data":21691,"content":21693},{"uri":21692},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-run-ai-and-vast",[21694],{"nodeType":1907,"value":21695,"marks":21696,"data":21698},"VAST and NVIDIA Run.ai",[21697],{"type":13127},{},{"nodeType":1907,"value":21700,"marks":21701,"data":21702}," streamline provisioning, unify storage access, and remove low-level Kubernetes friction through seamless integration with VAST’s multi-protocol architecture and NVIDIA Run.ai’s PVC-based data management. ",[],{},{"nodeType":12267,"data":21704,"content":21705},{},[21706],{"nodeType":1907,"value":21707,"marks":21708,"data":21709},"Simplified Provisioning and Abstraction",[],{},{"nodeType":1921,"data":21711,"content":21712},{},[21713],{"nodeType":1907,"value":21714,"marks":21715,"data":21716},"To see how this integration improves real-world operations, let’s start with how VAST and NVIDIA Run:AI streamline the basic task of provisioning and accessing data.",[],{},{"nodeType":1921,"data":21718,"content":21719},{},[21720,21725],{"nodeType":1907,"value":21721,"marks":21722,"data":21724},"VAST Multi-Protocol Efficiency:",[21723],{"type":13576},{},{"nodeType":1907,"value":21726,"marks":21727,"data":21728}," The VAST CSI driver can provision both block volumes and file system (NFS) volumes (for shared access and model serving) from the same underlying data platform. This simplifies the administrator’s job as they only manage one data platform for all AI needs. Using VAST CSI, the administrator simply selects the appropriate storage class from the list and then provisions a new or existing PVC based on that selection.",[],{},{"nodeType":12289,"data":21730,"content":21750},{"target":21731},{"metadata":21732,"sys":21735,"fields":21743},{"tags":21733,"concepts":21734},[],[],{"space":21736,"id":21738,"type":934,"createdAt":21739,"updatedAt":21740,"environment":21741,"publishedVersion":712,"revision":901,"locale":673},{"sys":21737},{"type":657,"linkType":658,"id":659},"hLYNpMJCZalHaQRLe7zbb","2025-12-16T13:56:03.037Z","2025-12-17T08:24:19.414Z",{"sys":21742},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":21744},{"url":21745,"details":21746,"fileName":21749,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FhLYNpMJCZalHaQRLe7zbb\u002Fd9e8064fe548a4066f12ced7bd876f0f\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",{"size":21747,"image":21748},19930,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",[],{"nodeType":1921,"data":21752,"content":21753},{},[21754],{"nodeType":1907,"value":21755,"marks":21756,"data":21758},"In this example, the storage class for both block and file systems is installed using the VAST CSI.",[21757],{"type":12260},{},{"nodeType":1921,"data":21760,"content":21761},{},[21762,21767],{"nodeType":1907,"value":21763,"marks":21764,"data":21766},"NVIDIA Run:AI PVC Abstraction:",[21765],{"type":13576},{},{"nodeType":1907,"value":21768,"marks":21769,"data":21770}," NVIDIA Run:AI abstracts the complexity of Kubernetes PVCs via its Data Sources feature. An administrator pre-defines PVC type Data Sources (backed by VAST StorageClasses) and exposes them to researchers. Researcher Self-Service: Researchers request data access simply by name (e.g., “project-a-pvc”) rather than needing to know the complex details of the VAST provisioner, StorageClass mount parameters, or volume binding.",[],{},{"nodeType":12267,"data":21772,"content":21773},{},[21774],{"nodeType":1907,"value":21775,"marks":21776,"data":21777},"Consistency and Advanced Feature Utilization",[],{},{"nodeType":1921,"data":21779,"content":21780},{},[21781],{"nodeType":1907,"value":21782,"marks":21783,"data":21784},"Beyond simplifying provisioning, the integration also ensures that advanced VAST features are consistently available through Kubernetes without adding operational overhead.",[],{},{"nodeType":12447,"data":21786,"content":21787},{},[21788,21828],{"nodeType":12451,"data":21789,"content":21790},{},[21791],{"nodeType":1921,"data":21792,"content":21793},{},[21794,21799,21803,21812,21815,21824],{"nodeType":1907,"value":21795,"marks":21796,"data":21798},"Snapshots and Clones:",[21797],{"type":13576},{},{"nodeType":1907,"value":21800,"marks":21801,"data":21802}," VAST’s native storage efficiency and performance features, such as high-performance ",[],{},{"nodeType":1916,"data":21804,"content":21806},{"uri":21805},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-snapshots-to-vasts-element-store",[21807],{"nodeType":1907,"value":21808,"marks":21809,"data":21811},"snapshots",[21810],{"type":13127},{},{"nodeType":1907,"value":13487,"marks":21813,"data":21814},[],{},{"nodeType":1916,"data":21816,"content":21818},{"uri":21817},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fglobal-snapshot-clones-intelligent-multi-site-data-availability",[21819],{"nodeType":1907,"value":21820,"marks":21821,"data":21823},"clones",[21822],{"type":13127},{},{"nodeType":1907,"value":21825,"marks":21826,"data":21827},", are exposed directly through the CSI interface. The administrator manages these capabilities via standard Kubernetes VolumeSnapshot objects, ensuring fast data iteration and recovery without needing to learn separate VAST management tools for basic operations.",[],{},{"nodeType":12451,"data":21829,"content":21830},{},[21831],{"nodeType":1921,"data":21832,"content":21833},{},[21834,21839],{"nodeType":1907,"value":21835,"marks":21836,"data":21838},"Simplified Model Lifecycle:",[21837],{"type":13576},{},{"nodeType":1907,"value":21840,"marks":21841,"data":21842}," The ability to instantly clone a trained model PVC or restore from a snapshot makes managing the lifecycle of development, training, and deployment seamless and efficient for the administrator.",[],{},{"nodeType":12267,"data":21844,"content":21845},{},[21846],{"nodeType":1907,"value":21847,"marks":21848,"data":21849},"Multi-Tenancy on VAST",[],{},{"nodeType":1921,"data":21851,"content":21852},{},[21853,21857,21866],{"nodeType":1907,"value":21854,"marks":21855,"data":21856},"With provisioning and advanced features handled, the next challenge is ensuring each project or team has secure, isolated access to storage. VAST’s ",[],{},{"nodeType":1916,"data":21858,"content":21860},{"uri":21859},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fvast-4-6-multi-tenant-qos-demo",[21861],{"nodeType":1907,"value":21862,"marks":21863,"data":21865},"native multi-tenancy",[21864],{"type":13127},{},{"nodeType":1907,"value":21867,"marks":21868,"data":21869}," makes this simple.",[],{},{"nodeType":1921,"data":21871,"content":21872},{},[21873],{"nodeType":1907,"value":21874,"marks":21875,"data":21876},"The VAST Cluster’s multi-tenancy directly supports the needs of a job orchestrator like NVIDIA Run:AI by providing complete administrative and data isolation. Tenants are defined within VAST as isolated domains, each having its own file system hierarchy, unique security policies and performance controls (QoS). ",[],{},{"nodeType":1921,"data":21878,"content":21879},{},[21880],{"nodeType":1907,"value":21881,"marks":21882,"data":21883},"This structure directly enables NVIDIA Run:AI to create a dedicated VAST tenant for each project or department by mapping their respective network segments (Client IP Ranges) to distinct VIP Pools. This ensures every NVIDIA Run:ai project has segregated, high-performance storage access, preventing cross-project interference, managing consumption via individual QoS such as BW and IOPS limitations, quotas, and guaranteeing secure data isolation across NFS and S3 protocols.",[],{},{"nodeType":1921,"data":21885,"content":21886},{},[21887],{"nodeType":1907,"value":21888,"marks":21889,"data":21890},"First, we configure two tenants, differentiated by vippool, and create two corresponding storage classes, each assigned to one of the tenants.",[],{},{"nodeType":1921,"data":21892,"content":21893},{},[21894],{"nodeType":1907,"value":21895,"marks":21896,"data":21897},"Subsequently, we assign two projects in NVIDIA Run:AI, one for each storage class. This configuration ensures definitive data isolation between the projects.",[],{},{"nodeType":12289,"data":21899,"content":21920},{"target":21900},{"metadata":21901,"sys":21904,"fields":21912},{"tags":21902,"concepts":21903},[],[],{"space":21905,"id":21907,"type":934,"createdAt":21908,"updatedAt":21909,"environment":21910,"publishedVersion":712,"revision":901,"locale":673},{"sys":21906},{"type":657,"linkType":658,"id":659},"6yc2QsKV9sGw5SVVSci6Vs","2025-12-16T14:00:19.048Z","2025-12-17T08:24:05.430Z",{"sys":21911},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":21913},{"url":21914,"details":21915,"fileName":21919,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6yc2QsKV9sGw5SVVSci6Vs\u002F8445177ac0cbc88c3ba92b94d977cbe8\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",{"size":21916,"image":21917},34318,{"width":1144,"height":21918},734,"simplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",[],{"nodeType":1921,"data":21922,"content":21923},{},[21924],{"nodeType":1907,"value":21925,"marks":21926,"data":21928},"In the following example, two projects are assigned, each with its own dedicated storage class. This configuration ensures that every PVC, NFS mount, or S3 bucket is exclusively owned by and isolated within its respective project.",[21927],{"type":12260},{},{"nodeType":12289,"data":21930,"content":21950},{"target":21931},{"metadata":21932,"sys":21935,"fields":21943},{"tags":21933,"concepts":21934},[],[],{"space":21936,"id":21938,"type":934,"createdAt":21939,"updatedAt":21940,"environment":21941,"publishedVersion":712,"revision":901,"locale":673},{"sys":21937},{"type":657,"linkType":658,"id":659},"kdNFeI1EdTQVWw3l6H2HJ","2025-12-16T14:01:52.688Z","2025-12-17T08:23:52.260Z",{"sys":21942},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":21944},{"url":21945,"details":21946,"fileName":21949,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FkdNFeI1EdTQVWw3l6H2HJ\u002F5bc2cbf1b6c6a1c9da29b8fcf30a3269\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",{"size":21947,"image":21948},36150,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",[],{"nodeType":12267,"data":21952,"content":21953},{},[21954],{"nodeType":1907,"value":21955,"marks":21956,"data":21957},"Controlling Tenants’ QoS",[],{},{"nodeType":1921,"data":21959,"content":21960},{},[21961],{"nodeType":1907,"value":21962,"marks":21963,"data":21964},"Once tenants are defined, the next step is ensuring each project gets the right performance. ",[],{},{"nodeType":1921,"data":21966,"content":21967},{},[21968,21971,21980],{"nodeType":1907,"value":2031,"marks":21969,"data":21970},[],{},{"nodeType":1916,"data":21972,"content":21974},{"uri":21973},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fenforcing-io-discipline-dynamic-qos-model-training",[21975],{"nodeType":1907,"value":21976,"marks":21977,"data":21979},"VAST’s QoS policies",[21978],{"type":13127},{},{"nodeType":1907,"value":21981,"marks":21982,"data":21983}," give administrators precise control over bandwidth and IOPS for every tenant or user. The policies provide granular control over storage performance by defining minimum guaranteed and maximum allowed limits for bandwidth and IOPS on read\u002Fwrite operations.",[],{},{"nodeType":1921,"data":21985,"content":21986},{},[21987],{"nodeType":1907,"value":21988,"marks":21989,"data":21990},"Crucially, QoS can be applied granularly either to a specific view or to an individual user within a tenant. This capability is essential for a NVIDIA Run:AI administrator, as they can accurately map the isolated VAST tenant dedicated to each Run:AI project to a specific QoS policy.",[],{},{"nodeType":1921,"data":21992,"content":21993},{},[21994],{"nodeType":1907,"value":21995,"marks":21996,"data":21997},"By assigning these QoS policies, the administrator ensures that every project receives its properly provisioned performance limitations (or guarantees), effectively preventing resource contention and guaranteeing that no single project monopolizes the high-performance storage infrastructure.",[],{},{"nodeType":12267,"data":21999,"content":22000},{},[22001],{"nodeType":1907,"value":22002,"marks":22003,"data":22004},"Multiple Sites \u002F Global Namespace Integration",[],{},{"nodeType":1921,"data":22006,"content":22007},{},[22008,22012,22020],{"nodeType":1907,"value":22009,"marks":22010,"data":22011},"While we’ve concentrated on local PVC workloads with NVIDIA Run:AI, VAST’s global namespace (",[],{},{"nodeType":1916,"data":22013,"content":22015},{"uri":22014},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdataspace",[22016],{"nodeType":1907,"value":3818,"marks":22017,"data":22019},[22018],{"type":13127},{},{"nodeType":1907,"value":22021,"marks":22022,"data":22023},") is vital for extending resource utilization to remote sites and enabling advanced Run:AI scenarios. Let’s discuss two scenarios: ",[],{},{"nodeType":12447,"data":22025,"content":22026},{},[22027,22037],{"nodeType":12451,"data":22028,"content":22029},{},[22030],{"nodeType":1921,"data":22031,"content":22032},{},[22033],{"nodeType":1907,"value":22034,"marks":22035,"data":22036},"Seamless multi-site burst compute for both training and inference",[],{},{"nodeType":12451,"data":22038,"content":22039},{},[22040],{"nodeType":1921,"data":22041,"content":22042},{},[22043],{"nodeType":1907,"value":22044,"marks":22045,"data":22046},"Resilient LLM Training",[],{},{"nodeType":12289,"data":22048,"content":22068},{"target":22049},{"metadata":22050,"sys":22053,"fields":22060},{"tags":22051,"concepts":22052},[],[],{"space":22054,"id":22056,"type":934,"createdAt":22057,"updatedAt":22057,"environment":22058,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22055},{"type":657,"linkType":658,"id":659},"4VJFjVdk0kDHrqMAd5QX8L","2025-12-17T08:23:40.138Z",{"sys":22059},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22061},{"url":22062,"details":22063,"fileName":22067,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4VJFjVdk0kDHrqMAd5QX8L\u002Fb0e52250dfd10cb5363a5fc25784bb1e\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",{"size":22064,"image":22065},117848,{"width":1144,"height":22066},808,"simplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",[],{"nodeType":1921,"data":22070,"content":22071},{},[22072,22077,22083,22088,22094],{"nodeType":1907,"value":22073,"marks":22074,"data":22076},"Run:AI can dynamically burst the job queue to a ",[22075],{"type":12260},{},{"nodeType":1907,"value":22078,"marks":22079,"data":22082},"secondary site (Site B)",[22080,22081],{"type":12260},{"type":13576},{},{"nodeType":1907,"value":22084,"marks":22085,"data":22087}," or a ",[22086],{"type":12260},{},{"nodeType":1907,"value":22089,"marks":22090,"data":22093},"cloud environment",[22091,22092],{"type":12260},{"type":13576},{},{"nodeType":1907,"value":22095,"marks":22096,"data":22098}," where more compute resources are available.",[22097],{"type":12260},{},{"nodeType":12267,"data":22100,"content":22101},{},[22102],{"nodeType":1907,"value":22103,"marks":22104,"data":22105},"Burst Compute for Training and Inference",[],{},{"nodeType":1921,"data":22107,"content":22108},{},[22109,22113,22121],{"nodeType":1907,"value":22110,"marks":22111,"data":22112},"This is a classic use case enabled by the ",[],{},{"nodeType":1916,"data":22114,"content":22116},{"uri":22115},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fglobal-namespace-demo",[22117],{"nodeType":1907,"value":22118,"marks":22119,"data":22120},"global, persistent nature",[],{},{"nodeType":1907,"value":22122,"marks":22123,"data":22124}," of the VAST DataSpace.",[],{},{"nodeType":1921,"data":22126,"content":22127},{},[22128,22133],{"nodeType":1907,"value":22129,"marks":22130,"data":22132},"Scenario:",[22131],{"type":13576},{},{"nodeType":1907,"value":22134,"marks":22135,"data":22136}," You start a large training job on Site A (e.g., on older GPUs or fewer resources), but you acquire new, faster and much more compute resources at Site B.",[],{},{"nodeType":1921,"data":22138,"content":22139},{},[22140],{"nodeType":1907,"value":22141,"marks":22142,"data":22144},"Solution:",[22143],{"type":13576},{},{"nodeType":18247,"data":22146,"content":22147},{},[22148,22158,22168],{"nodeType":12451,"data":22149,"content":22150},{},[22151],{"nodeType":1921,"data":22152,"content":22153},{},[22154],{"nodeType":1907,"value":22155,"marks":22156,"data":22157},"A job on Site A writes its training checkpoints to the VAST file system mounted via NFS. ",[],{},{"nodeType":12451,"data":22159,"content":22160},{},[22161],{"nodeType":1921,"data":22162,"content":22163},{},[22164],{"nodeType":1907,"value":22165,"marks":22166,"data":22167},"The PVC for Site B is linked to the Site B VAST system, making the data instantly accessible at the location with superior compute resources. ",[],{},{"nodeType":12451,"data":22169,"content":22170},{},[22171],{"nodeType":1921,"data":22172,"content":22173},{},[22174],{"nodeType":1907,"value":22175,"marks":22176,"data":22177},"NVIDIA Run:AI orchestrates the shutdown of the training job on Site A and launches a new job on Site B, instructing it to resume training from the latest replicated checkpoint path, immediately utilizing the significantly greater GPU compute resources available there. ",[],{},{"nodeType":12289,"data":22179,"content":22198},{"target":22180},{"metadata":22181,"sys":22184,"fields":22191},{"tags":22182,"concepts":22183},[],[],{"space":22185,"id":22187,"type":934,"createdAt":22188,"updatedAt":22188,"environment":22189,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22186},{"type":657,"linkType":658,"id":659},"68urmPYKyxmQ5RH1ZwDewH","2025-12-17T08:26:53.179Z",{"sys":22190},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22192},{"url":22193,"details":22194,"fileName":22197,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F68urmPYKyxmQ5RH1ZwDewH\u002Fdc8556b00c19ac8043e5539c5a41fe85\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",{"size":22195,"image":22196},41180,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",[],{"nodeType":1921,"data":22200,"content":22201},{},[22202,22207],{"nodeType":1907,"value":22203,"marks":22204,"data":22206},"Benefit:",[22205],{"type":13576},{},{"nodeType":1907,"value":22208,"marks":22209,"data":22210}," The job transitions seamlessly, leveraging the global VAST namespace to access the same data instantly on a site with much higher GPU compute power. This utilization of optimal, high-powered resources eliminates data transfer bottlenecks, leading to dramatically faster time-to-model completion.",[],{},{"nodeType":12267,"data":22212,"content":22213},{},[22214],{"nodeType":1907,"value":22215,"marks":22216,"data":22217},"Resilient LLM Training: Checkpoint and Recover in Seconds",[],{},{"nodeType":1921,"data":22219,"content":22220},{},[22221],{"nodeType":1907,"value":22222,"marks":22223,"data":22224},"The training of cutting-edge AI models, often spanning thousands of GPUs over weeks or even months, carries an unavoidable risk: hardware or network failures. At scale, a single GPU crash can halt an entire synchronous training job, wasting GPU‑hours and delaying training.",[],{},{"nodeType":1921,"data":22226,"content":22227},{},[22228,22232,22240],{"nodeType":1907,"value":22229,"marks":22230,"data":22231},"What if you could build resilience directly into your pipeline so that, when disaster strikes, your training picks up from the last checkpoint in just seconds? In this section, we’ll show how VAST’s high-throughput storage combined with NVIDIA Run:AI’s orchestration layer can be combined for checkpointing, automatic recovery, and zero disruption to LLM training with no manual intervention. We’ll walk through a real example - complete with code you can run - and share a ",[],{},{"nodeType":1916,"data":22233,"content":22235},{"uri":22234},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency",[22236],{"nodeType":1907,"value":21442,"marks":22237,"data":22239},[22238],{"type":13127},{},{"nodeType":1907,"value":22241,"marks":22242,"data":22243}," to reproduce it.",[],{},{"nodeType":19541,"data":22245,"content":22246},{},[22247],{"nodeType":1907,"value":22248,"marks":22249,"data":22250},"The High Cost of Unplanned Interruptions",[],{},{"nodeType":1921,"data":22252,"content":22253},{},[22254,22258,22278],{"nodeType":1907,"value":22255,"marks":22256,"data":22257},"Failures during training aren’t reserved for hyperscale clusters - they can strike any environment, from a handful of GPUs to multi-region deployments. ",[],{},{"nodeType":1916,"data":22259,"content":22261},{"uri":22260},"https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fthe-llama-3-herd-of-models\u002F",[22262,22267,22273],{"nodeType":1907,"value":22263,"marks":22264,"data":22266},"In Meta’s ",[22265],{"type":13127},{},{"nodeType":1907,"value":22268,"marks":22269,"data":22272},"Llama 3",[22270,22271],{"type":13127},{"type":12260},{},{"nodeType":1907,"value":22274,"marks":22275,"data":22277}," pretraining report",[22276],{"type":13127},{},{"nodeType":1907,"value":22279,"marks":22280,"data":22281},", we get a glimpse into just how common these interruptions are:",[],{},{"nodeType":1921,"data":22283,"content":22284},{},[22285,22290,22296,22301,22307],{"nodeType":1907,"value":22286,"marks":22287,"data":22289},"Over a 54-day window, Meta’s 16,384-GPU cluster recorded ",[22288],{"type":12260},{},{"nodeType":1907,"value":22291,"marks":22292,"data":22295},"466 job interruptions",[22293,22294],{"type":12260},{"type":13576},{},{"nodeType":1907,"value":22297,"marks":22298,"data":22300},", 90% of which were unplanned. GPU hardware alone caused ",[22299],{"type":12260},{},{"nodeType":1907,"value":22302,"marks":22303,"data":22306},"59%",[22304,22305],{"type":12260},{"type":13576},{},{"nodeType":1907,"value":22308,"marks":22309,"data":22311}," of those failures.",[22310],{"type":12260},{},{"nodeType":1921,"data":22313,"content":22314},{},[22315],{"nodeType":1907,"value":22316,"marks":22317,"data":22318},"Even if your environment is 1 percent the size of Meta’s, the likelihood of encountering failure grows proportionally with the number of GPUs. Without checkpointing, a single crash can wipe out hours or days of training progress - a huge waste of compute time and a serious bottleneck for any ML team.",[],{},{"nodeType":19541,"data":22320,"content":22321},{},[22322],{"nodeType":1907,"value":22323,"marks":22324,"data":22325},"Why Checkpointing Matters",[],{},{"nodeType":1921,"data":22327,"content":22328},{},[22329,22332,22341],{"nodeType":1907,"value":2031,"marks":22330,"data":22331},[],{},{"nodeType":1916,"data":22333,"content":22335},{"uri":22334},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fa-checkpoint-on-checkpoints-in-llms",[22336],{"nodeType":1907,"value":22337,"marks":22338,"data":22340},"Checkpointing",[22339],{"type":13127},{},{"nodeType":1907,"value":22342,"marks":22343,"data":22344}," - periodically saving model weights and optimizer state - is more than a safety net. It enables you to recover from interruptions without restarting from zero, select the best‑performing model version based on validation metrics, and reproduce experiments reliably. The bigger the model that you are training, the longer a single epoch can take; losing even a few hours due to a crash costs a lot.",[],{},{"nodeType":1921,"data":22346,"content":22347},{},[22348,22351,22360],{"nodeType":1907,"value":2031,"marks":22349,"data":22350},[],{},{"nodeType":1916,"data":22352,"content":22354},{"uri":22353},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Foptimizing-checkpoint-bandwidth-for-llm-training",[22355],{"nodeType":1907,"value":22356,"marks":22357,"data":22359},"Checkpoint strategies",[22358],{"type":13127},{},{"nodeType":1907,"value":22361,"marks":22362,"data":22363}," vary depending on use case: you might save every few minutes (periodic), only when validation accuracy improves (best‑model), at the end of each epoch or steps (last‑epoch\u002Fsteps), or under custom conditions such as hitting a specific loss threshold. Regardless of the approach, the choice of storage for these checkpoints is equally crucial.",[],{},{"nodeType":19541,"data":22365,"content":22366},{},[22367],{"nodeType":1907,"value":22368,"marks":22369,"data":22370},"The Storage Dilemma",[],{},{"nodeType":1921,"data":22372,"content":22373},{},[22374],{"nodeType":1907,"value":22375,"marks":22376,"data":22377},"Many teams default to cloud object stores or local disks for checkpoint storage. While cloud storage offers geo‑redundancy, its latency can stretch multi‑GB checkpoint writes and reads into minutes. Local disks may deliver lower latency, but they live and die with the node - taking your checkpoints offline if the machine fails.",[],{},{"nodeType":1921,"data":22379,"content":22380},{},[22381,22385,22393,22397,22406],{"nodeType":1907,"value":22382,"marks":22383,"data":22384},"VAST bridges these gaps. VAST’s ",[],{},{"nodeType":1916,"data":22386,"content":22387},{"uri":14057},[22388],{"nodeType":1907,"value":22389,"marks":22390,"data":22392},"disaggregated architecture",[22391],{"type":13127},{},{"nodeType":1907,"value":22394,"marks":22395,"data":22396}," aggregates NVMe drives across an entire cluster into a single global namespace, providing parallel I\u002FO at tens of gigabytes per second plus ",[],{},{"nodeType":1916,"data":22398,"content":22400},{"uri":22399},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[22401],{"nodeType":1907,"value":22402,"marks":22403,"data":22405},"built‑in erasure coding",[22404],{"type":13127},{},{"nodeType":1907,"value":22407,"marks":22408,"data":22409}," for data durability.",[],{},{"nodeType":19541,"data":22411,"content":22412},{},[22413],{"nodeType":1907,"value":22414,"marks":22415,"data":22416},"Orchestration with NVIDIA Run:AI",[],{},{"nodeType":1921,"data":22418,"content":22419},{},[22420],{"nodeType":1907,"value":22421,"marks":22422,"data":22423},"On the compute side, NVIDIA Run:AI layers atop Kubernetes to manage GPU and data resources intelligently. It continuously monitors pod and node health, automatically rescheduling failed jobs to healthy GPU pools - even across regions. ",[],{},{"nodeType":1921,"data":22425,"content":22426},{},[22427],{"nodeType":1907,"value":22428,"marks":22429,"data":22430},"Mounting a VAST volume into your NVIDIA Run:AI‑managed pod is straightforward: no changes to your training code are required. Your script reads and writes checkpoints just as before, but now with NVMe‑equivalent performance and cross‑region accessibility.",[],{},{"nodeType":12267,"data":22432,"content":22433},{},[22434],{"nodeType":1907,"value":22435,"marks":22436,"data":22437},"Fast, Cross‑Region Recovery in Action",[],{},{"nodeType":1921,"data":22439,"content":22440},{},[22441],{"nodeType":1907,"value":22442,"marks":22443,"data":22444},"In the step-by-step walkthrough below, we launch a PyTorch training job for a LoRa adapter on Llama 3.1 in site A. Checkpoints are written in every 5 steps to a VAST‑backed volume. ",[],{},{"nodeType":1921,"data":22446,"content":22447},{},[22448],{"nodeType":1907,"value":22449,"marks":22450,"data":22451},"We then simulate a GPU host crash in site-A - Run:AI instantly detects the failure and reschedules the pod to a GPU cluster in site-B. ",[],{},{"nodeType":1921,"data":22453,"content":22454},{},[22455],{"nodeType":1907,"value":22456,"marks":22457,"data":22458},"Since the VAST volume is globally accessible, the job reconnects to the same storage namespace; the latest checkpoint streams at full NVMe speed, and training resumes within seconds. What would normally take minutes of downtime is reduced to under half a minute - without any manual intervention.",[],{},{"nodeType":1921,"data":22460,"content":22461},{},[22462],{"nodeType":1907,"value":22463,"marks":22464,"data":22465},"In our approach, we start by preloading the base model into VAST using a data mover container. This avoids the cold start of downloading the model every time training begins. Once that’s in place, we submit the training job using the same VAST-mounted volume. Checkpoints are written to \u002Fmodel\u002Fcheckpoints, and when a failure is triggered, recovery picks up exactly where it left off.",[],{},{"nodeType":1921,"data":22467,"content":22468},{},[22469],{"nodeType":1907,"value":22470,"marks":22471,"data":22473},"Putting it into Practice: A Step-by-Step Job Submission",[22472],{"type":13576},{},{"nodeType":1921,"data":22475,"content":22476},{},[22477,22481,22488],{"nodeType":1907,"value":22478,"marks":22479,"data":22480},"To show this resilience in action, we’ve built a complete working example that you can reproduce. The full source code, scripts, and container definitions are available in our ",[],{},{"nodeType":1916,"data":22482,"content":22483},{"uri":22234},[22484],{"nodeType":1907,"value":21442,"marks":22485,"data":22487},[22486],{"type":13127},{},{"nodeType":1907,"value":1920,"marks":22489,"data":22490},[],{},{"nodeType":1921,"data":22492,"content":22493},{},[22494],{"nodeType":1907,"value":22495,"marks":22496,"data":22498},"Step 1: Creating a PVC with VAST CSI filesystem",[22497],{"type":13576},{},{"nodeType":1921,"data":22500,"content":22501},{},[22502],{"nodeType":1907,"value":22503,"marks":22504,"data":22505},"Let’s create a PVC with VAST CSI filesystem and mount it to \u002Fmodel. ",[],{},{"nodeType":12289,"data":22507,"content":22526},{"target":22508},{"metadata":22509,"sys":22512,"fields":22519},{"tags":22510,"concepts":22511},[],[],{"space":22513,"id":22515,"type":934,"createdAt":22516,"updatedAt":22516,"environment":22517,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22514},{"type":657,"linkType":658,"id":659},"77QPXz8kJ5suw2HsXf7PIO","2025-12-17T08:28:42.070Z",{"sys":22518},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22520},{"url":22521,"details":22522,"fileName":22525,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F77QPXz8kJ5suw2HsXf7PIO\u002F87bbdebb1642d12787d37b08e1feff92\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",{"size":22523,"image":22524},51050,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",[],{"nodeType":1921,"data":22528,"content":22529},{},[22530],{"nodeType":1907,"value":22531,"marks":22532,"data":22533},"Create a new training workload job with pytorch-one-gpu:",[],{},{"nodeType":12289,"data":22535,"content":22555},{"target":22536},{"metadata":22537,"sys":22540,"fields":22547},{"tags":22538,"concepts":22539},[],[],{"space":22541,"id":22543,"type":934,"createdAt":22544,"updatedAt":22544,"environment":22545,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22542},{"type":657,"linkType":658,"id":659},"41lMeyZPrg1hwFMqvUlEdp","2025-12-17T08:30:29.308Z",{"sys":22546},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22548},{"url":22549,"details":22550,"fileName":22554,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F41lMeyZPrg1hwFMqvUlEdp\u002F8ed7d59c7fd81eff3525165efcfe2e2d\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",{"size":22551,"image":22552},12608,{"width":1144,"height":22553},521,"simplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",[],{"nodeType":1921,"data":22557,"content":22558},{},[22559,22563,22572],{"nodeType":1907,"value":22560,"marks":22561,"data":22562},"If the model isn’t already downloaded, start by fetching it - in this example, we’ll download Meta-Llama-3.1-8B-Instruct into \u002Fmodel and create the additional checkpoint directories. You can use the model-loading bash script included ",[],{},{"nodeType":1916,"data":22564,"content":22566},{"uri":22565},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency\u002Fblob\u002Fmain\u002Fmodel-loading\u002Fclone.sh",[22567],{"nodeType":1907,"value":22568,"marks":22569,"data":22571},"in this repository",[22570],{"type":13127},{},{"nodeType":1907,"value":22573,"marks":22574,"data":22575}," and run it after SSH-ing into the pod. This script downloads Meta-Llama-3.1-8B-Instruct from Hugging Face Hub and writes it to \u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct. It also creates the \u002Fmodel\u002Fcheckpoints directory with the correct permissions where training checkpoints will be written.",[],{},{"nodeType":1921,"data":22577,"content":22578},{},[22579],{"nodeType":1907,"value":22580,"marks":22581,"data":22582},"After this step completes, your VAST volume contains:",[],{},{"nodeType":12447,"data":22584,"content":22585},{},[22586,22596],{"nodeType":12451,"data":22587,"content":22588},{},[22589],{"nodeType":1921,"data":22590,"content":22591},{},[22592],{"nodeType":1907,"value":22593,"marks":22594,"data":22595},"\u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct - The base model (one-time download)",[],{},{"nodeType":12451,"data":22597,"content":22598},{},[22599],{"nodeType":1921,"data":22600,"content":22601},{},[22602],{"nodeType":1907,"value":22603,"marks":22604,"data":22605},"\u002Fmodel\u002Fcheckpoints\u002F - Empty directory ready for checkpoints",[],{},{"nodeType":1921,"data":22607,"content":22608},{},[22609],{"nodeType":1907,"value":22610,"marks":22611,"data":22613},"Step 2: Creating Training Script ",[22612],{"type":13576},{},{"nodeType":1921,"data":22615,"content":22616},{},[22617],{"nodeType":1907,"value":22618,"marks":22619,"data":22620},"Now let’s look at what makes this training resilient: checkpointing. The heart of this example is training\u002Fdistributed.py, which implements checkpointing and recovery; ",[],{},{"nodeType":12447,"data":22622,"content":22623},{},[22624,22639,22654,22669],{"nodeType":12451,"data":22625,"content":22626},{},[22627],{"nodeType":1921,"data":22628,"content":22629},{},[22630,22635],{"nodeType":1907,"value":22631,"marks":22632,"data":22634},"Checkpoint frequency: ",[22633],{"type":13576},{},{"nodeType":1907,"value":22636,"marks":22637,"data":22638},"Saves every 5 training steps to \u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F",[],{},{"nodeType":12451,"data":22640,"content":22641},{},[22642],{"nodeType":1921,"data":22643,"content":22644},{},[22645,22650],{"nodeType":1907,"value":22646,"marks":22647,"data":22649},"Automatic resume logic: ",[22648],{"type":13576},{},{"nodeType":1907,"value":22651,"marks":22652,"data":22653},"On startup, the script searches for existing checkpoints and automatically resumes from the latest one",[],{},{"nodeType":12451,"data":22655,"content":22656},{},[22657],{"nodeType":1921,"data":22658,"content":22659},{},[22660,22665],{"nodeType":1907,"value":22661,"marks":22662,"data":22664},"LoRa fine-tuning:",[22663],{"type":13576},{},{"nodeType":1907,"value":22666,"marks":22667,"data":22668}," Uses parameter-efficient LoRa adapters to fine-tune Llama 3.1 with minimal memory overhead",[],{},{"nodeType":12451,"data":22670,"content":22671},{},[22672],{"nodeType":1921,"data":22673,"content":22674},{},[22675,22680],{"nodeType":1907,"value":22676,"marks":22677,"data":22679},"Distributed training ready:",[22678],{"type":13576},{},{"nodeType":1907,"value":22681,"marks":22682,"data":22683}," Leverages torchrun for multi-GPU training via the launch.sh entrypoint",[],{},{"nodeType":1921,"data":22685,"content":22686},{},[22687],{"nodeType":1907,"value":22688,"marks":22689,"data":22691},"Step 3: Build the Training Image (or Use Ours)",[22690],{"type":13576},{},{"nodeType":1921,"data":22693,"content":22694},{},[22695],{"nodeType":1907,"value":22696,"marks":22697,"data":22698},"The training\u002F directory includes a Dockerfile that packages everything needed. ",[],{},{"nodeType":1921,"data":22700,"content":22701},{},[22702],{"nodeType":1907,"value":22703,"marks":22704,"data":22705},"If you want to build your own image:",[],{},{"nodeType":1921,"data":22707,"content":22708},{},[22709],{"nodeType":1907,"value":22710,"marks":22711,"data":22714},"cd training \ndocker build --platform linux\u002Famd64 -t \u003CYOUR_REGISTRY>\u002Frunai-vast-training\ndocker push \u003CYOUR_REGISTRY>\u002Frunai-vast-training",[22712],{"type":22713},"code",{},{"nodeType":1921,"data":22716,"content":22717},{},[22718,22722,22731],{"nodeType":1907,"value":22719,"marks":22720,"data":22721},"Alternatively, you can use ",[],{},{"nodeType":1916,"data":22723,"content":22725},{"uri":22724},"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fekarabulut844\u002Frunai-vast-training",[22726],{"nodeType":1907,"value":22727,"marks":22728,"data":22730},"our pre-built Docker image",[22729],{"type":13127},{},{"nodeType":1907,"value":22732,"marks":22733,"data":22734}," that’s ready to go. ",[],{},{"nodeType":1921,"data":22736,"content":22737},{},[22738],{"nodeType":1907,"value":22739,"marks":22740,"data":22742},"Step 4: Launch the Training Job",[22741],{"type":13576},{},{"nodeType":1921,"data":22744,"content":22745},{},[22746],{"nodeType":1907,"value":22747,"marks":22748,"data":22749},"With the model loaded on VAST and the training image ready, submit your training job via Run:AI:",[],{},{"nodeType":1921,"data":22751,"content":22752},{},[22753],{"nodeType":1907,"value":22754,"marks":22755,"data":22757},"runai training pytorch submit lora-fine-tune-model --existing-pvc \"claimname=$DATA,path=\u002Fmodel\" -i docker.io\u002F\u003CREGISTRY>\u002Frunai-vast-training -g 1",[22756],{"type":22713},{},{"nodeType":1921,"data":22759,"content":22760},{},[22761],{"nodeType":1907,"value":22762,"marks":22763,"data":22764},"Once the job starts, you’ll see training logs showing the LoRa fine-tuning in progress. Every 5 steps, you’ll see checkpoint confirmations: ",[],{},{"nodeType":12289,"data":22766,"content":22786},{"target":22767},{"metadata":22768,"sys":22771,"fields":22778},{"tags":22769,"concepts":22770},[],[],{"space":22772,"id":22774,"type":934,"createdAt":22775,"updatedAt":22775,"environment":22776,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22773},{"type":657,"linkType":658,"id":659},"qqhA5Y8rP0XN94LDN7MeA","2025-12-17T08:38:12.304Z",{"sys":22777},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22779},{"url":22780,"details":22781,"fileName":22785,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FqqhA5Y8rP0XN94LDN7MeA\u002F5f56d596378a69b3341bfeea28ae4fbe\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",{"size":22782,"image":22783},204520,{"width":1144,"height":22784},972,"simplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",[],{"nodeType":1921,"data":22788,"content":22789},{},[22790],{"nodeType":1907,"value":22791,"marks":22792,"data":22793},"Checkpoints are already saving to \u002Fmodel. Now we can simulate a failure on site A, and load, for example, an inference job on site B using checkpoint-40…",[],{},{"nodeType":1921,"data":22795,"content":22796},{},[22797,22802,22808],{"nodeType":1907,"value":22798,"marks":22799,"data":22801},"root@training-0-0:\u002Fmodel# ls -ltr checkpoints\u002F\ntotal Ø\n\ndrwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:21 Meta-Llama-3.1-8B-Instruct-finetuned root@training-0-ø:\u002Fmodel# ls -ltr checkpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F\ntotal 2\n\n",[22800],{"type":22713},{},{"nodeType":1907,"value":22803,"marks":22804,"data":22807},"drwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:18 checkpoint-40\n",[22805,22806],{"type":13576},{"type":22713},{},{"nodeType":1907,"value":22809,"marks":22810,"data":22812},"-rw-r-r-1 nobody nogroup 1429 Noy 19 17:21 README.md\ndrwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:21 checkpoint-80",[22811],{"type":22713},{},{"nodeType":1921,"data":22814,"content":22815},{},[22816],{"nodeType":1907,"value":22817,"marks":22818,"data":22819},"… by creating an inference workload with the following arguments:",[],{},{"nodeType":1921,"data":22821,"content":22822},{},[22823],{"nodeType":1907,"value":22824,"marks":22825,"data":22827},"cmd: vllm\narggs: serve \u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct --enable-lora --lora-modules adapter=\u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002Fcheckpoint-40 --max-model-len 1000 --enforce-eager --max-lora-rank 32",[22826],{"type":22713},{},{"nodeType":12289,"data":22829,"content":22849},{"target":22830},{"metadata":22831,"sys":22834,"fields":22841},{"tags":22832,"concepts":22833},[],[],{"space":22835,"id":22837,"type":934,"createdAt":22838,"updatedAt":22838,"environment":22839,"publishedVersion":847,"revision":1136,"locale":673},{"sys":22836},{"type":657,"linkType":658,"id":659},"38SHUNOFYtJfSLk5DvLWJm","2025-12-17T08:41:04.447Z",{"sys":22840},{"id":665,"type":657,"linkType":666},{"title":21637,"description":21637,"file":22842},{"url":22843,"details":22844,"fileName":22848,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F38SHUNOFYtJfSLk5DvLWJm\u002F145deaae37713e46c143c7bc48b78aba\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",{"size":22845,"image":22846},18250,{"width":1144,"height":22847},341,"simplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",[],{"nodeType":12267,"data":22851,"content":22852},{},[22853],{"nodeType":1907,"value":22854,"marks":22855,"data":22856},"Final Thoughts",[],{},{"nodeType":1921,"data":22858,"content":22859},{},[22860,22863,22872],{"nodeType":1907,"value":2031,"marks":22861,"data":22862},[],{},{"nodeType":1916,"data":22864,"content":22866},{"uri":22865},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-rack-level-resilience-to-vast-part-one",[22867],{"nodeType":1907,"value":22868,"marks":22869,"data":22871},"Resilience isn’t optional",[22870],{"type":13127},{},{"nodeType":1907,"value":22873,"marks":22874,"data":22875}," when training large models. Between hardware faults, preemptions, and scaling challenges, your pipeline needs to recover gracefully without human intervention. By combining a fast shared data store from VAST with automated orchestration from NVIDIA Run:AI, this illustrated example shows that checkpointing can be fast, global, and fully integrated into your training workflow.",[],{},{"nodeType":1921,"data":22877,"content":22878},{},[22879],{"nodeType":1907,"value":22880,"marks":22881,"data":22882},"Disaster becomes a speed bump, not a 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the Loop: Architecting a VAST, Multi-Agent AI System","beyond-the-loop-architecting-a-vast-multi-agent-ai-system","2025-12-02","Anat Heilper, Director of AI Infrastructure, and Ofir Zan, VP, AI Solutions & Enterprise 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the future of AI is a swarm of millions of agents, and how the DASE architecture provides the foundation to scale it.",[23138],{"type":12260},{},{"nodeType":20366,"data":23141,"content":23142},{},[],{"nodeType":1921,"data":23144,"content":23145},{},[23146],{"nodeType":1907,"value":23147,"marks":23148,"data":23149},"We are at a critical inflection point, one that fundamentally reshapes the future for both cloud service providers and enterprise buyers. The industry is rapidly moving beyond “AI as a chat box” and hurtling toward “AI as an autonomous workforce.” Today’s single-agent demos, running in a simple while loop, are the “hello, world” prototypes of a new computing era. ",[],{},{"nodeType":1921,"data":23151,"content":23152},{},[23153,23157],{"nodeType":1907,"value":23154,"marks":23155,"data":23156},"The future isn’t one hyper-intelligent agent; it’s a swarm of millions. Why? ",[],{},{"nodeType":1907,"value":23158,"marks":23159,"data":23161},"Specialization.",[23160],{"type":13576},{},{"nodeType":1921,"data":23163,"content":23164},{},[23165,23169,23174],{"nodeType":1907,"value":23166,"marks":23167,"data":23168},"A single company isn’t one hyper-productive CEO. It’s a team of specialists in finance, engineering, marketing, and logistics, all collaborating. Complex AI problems are no different. You wouldn’t ask one agent to run a global supply chain, design a silicon chip, or manage a live marketing campaign. These tasks require a ",[],{},{"nodeType":1907,"value":23170,"marks":23171,"data":23173},"team of specialized agents",[23172],{"type":12260},{},{"nodeType":1907,"value":23175,"marks":23176,"data":23177}," —a PlannerAgent, a ResearchAgent, a DataAnalystAgent, a SecurityMonitorAgent, and a NotificationAgent — all working in concert.",[],{},{"nodeType":1921,"data":23179,"content":23180},{},[23181],{"nodeType":1907,"value":23182,"marks":23183,"data":23184},"For CSPs, this shift represents the next generation of massive compute demand and a new, high-value AI platform to host. For enterprise buyers, this marks the opportunity to achieve unprecedented levels of automation and operational efficiency.",[],{},{"nodeType":1921,"data":23186,"content":23187},{},[23188,23192,23201,23205],{"nodeType":1907,"value":23189,"marks":23190,"data":23191},"However, this also creates an unprecedented infrastructure problem. A swarm of millions of autonomous agents, all reasoning, communicating, and accessing data simultaneously, ",[],{},{"nodeType":1916,"data":23193,"content":23195},{"uri":23194},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbuilding-enterprise-infrastructure-for-ai-agents",[23196],{"nodeType":1907,"value":23197,"marks":23198,"data":23200},"cannot run on today’s siloed architecture",[23199],{"type":13576},{},{"nodeType":1907,"value":1920,"marks":23202,"data":23204},[23203],{"type":13576},{},{"nodeType":1907,"value":23206,"marks":23207,"data":23208}," It’s an infrastructure nightmare, creating problems of cost, security, and complexity.",[],{},{"nodeType":12267,"data":23210,"content":23211},{},[23212],{"nodeType":1907,"value":23213,"marks":23214,"data":23215},"The Bottleneck: The Environment vs. The Swarm",[],{},{"nodeType":1921,"data":23217,"content":23218},{},[23219],{"nodeType":1907,"value":23220,"marks":23221,"data":23222},"The primary challenge in scaling from a single agent to a swarm is the lack of infrastructure designed for multi-agent orchestration. Today’s agent deployments typically rely on general-purpose infrastructure—virtual machines, containers, or serverless functions—that wasn’t built with swarm coordination in mind. While modern platforms can scale individual components, they lack the integrated primitives agents need to operate collectively at scale.",[],{},{"nodeType":1921,"data":23224,"content":23225},{},[23226],{"nodeType":1907,"value":23227,"marks":23228,"data":23229},"This creates critical bottlenecks that make swarm deployment challenging:",[],{},{"nodeType":12447,"data":23231,"content":23232},{},[23233,23248,23263,23278],{"nodeType":12451,"data":23234,"content":23235},{},[23236],{"nodeType":1921,"data":23237,"content":23238},{},[23239,23244],{"nodeType":1907,"value":23240,"marks":23241,"data":23243},"The coupled scaling problem:",[23242],{"type":13576},{},{"nodeType":1907,"value":23245,"marks":23246,"data":23247}," General-purpose infrastructure doesn’t natively understand agent-specific resource patterns. An agent’s compute needs (LLM inference) and memory needs (vector stores, conversation history) scale differently, but standard deployment models don’t provide first-class primitives for independent scaling of agent-specific resources. You end up either over-provisioning or constantly reconfiguring infrastructure to match workload patterns.",[],{},{"nodeType":12451,"data":23249,"content":23250},{},[23251],{"nodeType":1921,"data":23252,"content":23253},{},[23254,23259],{"nodeType":1907,"value":23255,"marks":23256,"data":23258},"The state and amnesia problem:",[23257],{"type":13576},{},{"nodeType":1907,"value":23260,"marks":23261,"data":23262}," Agents need durable, accessible state across invocations—conversation history, tool outputs, intermediate reasoning. Standard platforms offer generic databases or object storage, but lack agent-native state management that understands semantic context, supports efficient retrieval, handles state synchronization across multiple agents, and survives infrastructure changes. Developers must cobble together solutions from generic components, leading to fragility.",[],{},{"nodeType":12451,"data":23264,"content":23265},{},[23266],{"nodeType":1921,"data":23267,"content":23268},{},[23269,23274],{"nodeType":1907,"value":23270,"marks":23271,"data":23273},"The communication bottleneck:",[23272],{"type":13576},{},{"nodeType":1907,"value":23275,"marks":23276,"data":23277}," Coordinating thousands of agents requires high-throughput, asynchronous, observable communication patterns. While message queues and event brokers exist, they’re not agent-aware—they don’t understand task delegation, result aggregation, or semantic routing. Implementing swarm coordination on generic messaging infrastructure means building the orchestration layer yourself, which is complex and error-prone.",[],{},{"nodeType":12451,"data":23279,"content":23280},{},[23281],{"nodeType":1921,"data":23282,"content":23283},{},[23284,23289,23293,23298],{"nodeType":1907,"value":23285,"marks":23286,"data":23288},"The security and identity silo:",[23287],{"type":13576},{},{"nodeType":1907,"value":23290,"marks":23291,"data":23292}," In a monolithic setup, each agent is an island. It has to manage its ",[],{},{"nodeType":1907,"value":23294,"marks":23295,"data":23297},"own",[23296],{"type":12260},{},{"nodeType":1907,"value":23299,"marks":23300,"data":23301}," secrets and API keys. The platform provides no central, secure identity or tool execution service, creating thousands of individual security risks instead of one manageable one. This lack of centralized management extends to vital infrastructure like Model Context Protocol tools, which also become disparate and difficult to govern across the agent ecosystem.",[],{},{"nodeType":1921,"data":23303,"content":23304},{},[23305],{"nodeType":1907,"value":23306,"marks":23307,"data":23308},"These aren’t agent failures; they’re platform gaps - the absence of infrastructure primitives purpose-built for multi-agent systems.",[],{},{"nodeType":12267,"data":23310,"content":23311},{},[23312],{"nodeType":1907,"value":23313,"marks":23314,"data":23315},"A Shared, Decoupled Foundation for Running AI Agents",[],{},{"nodeType":1921,"data":23317,"content":23318},{},[23319],{"nodeType":1907,"value":23320,"marks":23321,"data":23322},"VAST’s architectural approach to this problem is built on a core design principle: compute and state must be fully separated, but all compute resources must have shared, high-speed access to a global, consistent state layer.",[],{},{"nodeType":1921,"data":23324,"content":23325},{},[23326,23330,23335],{"nodeType":1907,"value":23327,"marks":23328,"data":23329},"It turns out that agentic workloads are a perfect fit for this model. Rather than running agents in an “all-in-one-box” environment, we can run the ",[],{},{"nodeType":1907,"value":23331,"marks":23332,"data":23334},"separate functions",[23333],{"type":12260},{},{"nodeType":1907,"value":23336,"marks":23337,"data":23338}," of an agent - its compute logic, its reasoning, its state, and its communication - as their own layers on our platform. The agent’s logic (CPU) runs on one set of resources, while its state and memory (storage) live on another, accessible by all. CPU compute, GPU compute, storage, and networking can all be scaled independently, which opens the door for efficiently and economically serving large numbers of autonomous agents.",[],{},{"nodeType":1921,"data":23340,"content":23341},{},[23342,23346,23351],{"nodeType":1907,"value":23343,"marks":23344,"data":23345},"The primary advantage of this disaggregated architecture is the ability to scale ",[],{},{"nodeType":1907,"value":23347,"marks":23348,"data":23350},"only",[23349],{"type":12260},{},{"nodeType":1907,"value":23352,"marks":23353,"data":23354}," the component that is the bottleneck.",[],{},{"nodeType":12447,"data":23356,"content":23357},{},[23358,23373,23396,23411],{"nodeType":12451,"data":23359,"content":23360},{},[23361],{"nodeType":1921,"data":23362,"content":23363},{},[23364,23369],{"nodeType":1907,"value":23365,"marks":23366,"data":23368},"Scaling CPU (kernels)",[23367],{"type":13576},{},{"nodeType":1907,"value":23370,"marks":23371,"data":23372},": If 10,000 tasks arrive, the platform spins up 10,000 cheap, stateless Agent Kernels (on VAST C-Nodes).",[],{},{"nodeType":12451,"data":23374,"content":23375},{},[23376],{"nodeType":1921,"data":23377,"content":23378},{},[23379,23384,23388,23392],{"nodeType":1907,"value":23380,"marks":23381,"data":23383},"Scaling GPU (reasoning)",[23382],{"type":13576},{},{"nodeType":1907,"value":23385,"marks":23386,"data":23387},": If those agents create 100,000 reasoning requests, ",[],{},{"nodeType":1907,"value":23347,"marks":23389,"data":23391},[23390],{"type":12260},{},{"nodeType":1907,"value":23393,"marks":23394,"data":23395}," the GPU servers are scaled, backed by the VAST KVCache to maximize GPU utility.",[],{},{"nodeType":12451,"data":23397,"content":23398},{},[23399],{"nodeType":1921,"data":23400,"content":23401},{},[23402,23407],{"nodeType":1907,"value":23403,"marks":23404,"data":23406},"Scaling storage (memory)",[23405],{"type":13576},{},{"nodeType":1907,"value":23408,"marks":23409,"data":23410},": If agents need to access shared knowledge, the VAST AI OS (on VAST D-Nodes) scales its read replicas and internal bandwidth independently of compute.",[],{},{"nodeType":12451,"data":23412,"content":23413},{},[23414],{"nodeType":1921,"data":23415,"content":23416},{},[23417,23422],{"nodeType":1907,"value":23418,"marks":23419,"data":23421},"Scaling I\u002FO (comms)",[23420],{"type":13576},{},{"nodeType":1907,"value":23423,"marks":23424,"data":23425},": If agents pass massive data payloads, the Event Broker and the VAST fabric’s internal network handle the I\u002FO without the compute nodes becoming a bottleneck.",[],{},{"nodeType":12289,"data":23427,"content":23445},{"target":23428},{"metadata":23429,"sys":23432,"fields":23439},{"tags":23430,"concepts":23431},[],[],{"space":23433,"id":23435,"type":934,"createdAt":23436,"updatedAt":23436,"environment":23437,"publishedVersion":847,"revision":1136,"locale":673},{"sys":23434},{"type":657,"linkType":658,"id":659},"1O2NCk6JSuSs1UXnguRpiS","2025-11-19T14:14:44.267Z",{"sys":23438},{"id":665,"type":657,"linkType":666},{"title":23092,"description":23092,"file":23440},{"url":23441,"details":23442,"fileName":23128,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1O2NCk6JSuSs1UXnguRpiS\u002Fa2a722a7966a4271d9fc7ff17f7fa313\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"size":23443,"image":23444},159002,{"width":1144,"height":1145},[],{"nodeType":12267,"data":23447,"content":23448},{},[23449],{"nodeType":1907,"value":23450,"marks":23451,"data":23452},"Architectural and Engineering Deep Dive: Pillars of a Scalable Agentic Platform",[],{},{"nodeType":1921,"data":23454,"content":23455},{},[23456],{"nodeType":1907,"value":23457,"marks":23458,"data":23459},"This section examines the core components required to run disaggregated agents at scale on the VAST AI OS, addressing the fundamental challenges of coordination, state, scaling, and operability.",[],{},{"nodeType":19541,"data":23461,"content":23462},{},[23463],{"nodeType":1907,"value":23464,"marks":23465,"data":23466},"1. The Execution Model: Stateless Compute with Shared State Access",[],{},{"nodeType":1921,"data":23468,"content":23469},{},[23470],{"nodeType":1907,"value":23471,"marks":23472,"data":23473},"The fundamental architectural pattern is simple: agent logic runs as stateless compute, while all persistent state lives in a globally-accessible storage layer. Applying this pattern to agents requires solving specific problems around latency, consistency, and scale.",[],{},{"nodeType":1921,"data":23475,"content":23476},{},[23477],{"nodeType":1907,"value":23478,"marks":23479,"data":23481},"Agent Kernels as Ephemeral Functions",[23480],{"type":13576},{},{"nodeType":1921,"data":23483,"content":23484},{},[23485],{"nodeType":1907,"value":23486,"marks":23487,"data":23488},"Agent “kernels” (the executable logic of an agent) run as serverless functions on VAST C-Nodes (compute nodes). These functions are stateless and short-lived. They:",[],{},{"nodeType":12447,"data":23490,"content":23491},{},[23492,23502,23512,23522],{"nodeType":12451,"data":23493,"content":23494},{},[23495],{"nodeType":1921,"data":23496,"content":23497},{},[23498],{"nodeType":1907,"value":23499,"marks":23500,"data":23501},"Execute in response to events (e.g., file writes, object creation, table mutations)",[],{},{"nodeType":12451,"data":23503,"content":23504},{},[23505],{"nodeType":1921,"data":23506,"content":23507},{},[23508],{"nodeType":1907,"value":23509,"marks":23510,"data":23511},"Have direct NVMe access to the underlying storage fabric",[],{},{"nodeType":12451,"data":23513,"content":23514},{},[23515],{"nodeType":1921,"data":23516,"content":23517},{},[23518],{"nodeType":1907,"value":23519,"marks":23520,"data":23521},"Scale horizontally without coordination, so spawning 10,000 agent kernels doesn’t require inter-kernel communication",[],{},{"nodeType":12451,"data":23523,"content":23524},{},[23525],{"nodeType":1921,"data":23526,"content":23527},{},[23528],{"nodeType":1907,"value":23529,"marks":23530,"data":23531},"Die after execution; state persistence is the storage layer’s responsibility, not the kernel’s",[],{},{"nodeType":1921,"data":23533,"content":23534},{},[23535],{"nodeType":1907,"value":23536,"marks":23537,"data":23538},"This is a different architectural approach from traditional container-based agents, where the agent’s logic, memory, and state are bundled. Here, the kernel is throwaway; only the storage persists.",[],{},{"nodeType":1921,"data":23540,"content":23541},{},[23542],{"nodeType":1907,"value":23543,"marks":23544,"data":23546},"The Data Locality Advantage",[23545],{"type":13576},{},{"nodeType":1921,"data":23548,"content":23549},{},[23550],{"nodeType":1907,"value":23551,"marks":23552,"data":23553},"Because kernels run on C-Nodes that are part of the storage fabric, data access is local NVMe, not remote network I\u002FO. When an agent needs to read a 10GB dataset, that data doesn’t move across a network—the compute moves to the data. This is critical for agents doing data-intensive work (e.g., a DataAnalysisAgent processing logs or a VectorSearchAgent scanning embeddings).",[],{},{"nodeType":1921,"data":23555,"content":23556},{},[23557],{"nodeType":1907,"value":23558,"marks":23559,"data":23561},"Execution Triggers and Lifecycle",[23560],{"type":13576},{},{"nodeType":1921,"data":23563,"content":23564},{},[23565],{"nodeType":1907,"value":23566,"marks":23567,"data":23568},"Agent kernel triggers can be:",[],{},{"nodeType":18247,"data":23570,"content":23571},{},[23572,23587,23602],{"nodeType":12451,"data":23573,"content":23574},{},[23575],{"nodeType":1921,"data":23576,"content":23577},{},[23578,23583],{"nodeType":1907,"value":23579,"marks":23580,"data":23582},"Event-driven",[23581],{"type":13576},{},{"nodeType":1907,"value":23584,"marks":23585,"data":23586},": File write, object creation, table insert → kernel spawns automatically",[],{},{"nodeType":12451,"data":23588,"content":23589},{},[23590],{"nodeType":1921,"data":23591,"content":23592},{},[23593,23598],{"nodeType":1907,"value":23594,"marks":23595,"data":23597},"API-driven",[23596],{"type":13576},{},{"nodeType":1907,"value":23599,"marks":23600,"data":23601},": External system calls an endpoint → kernel executes on-demand",[],{},{"nodeType":12451,"data":23603,"content":23604},{},[23605],{"nodeType":1921,"data":23606,"content":23607},{},[23608,23613],{"nodeType":1907,"value":23609,"marks":23610,"data":23612},"Scheduled",[23611],{"type":13576},{},{"nodeType":1907,"value":23614,"marks":23615,"data":23616},": Cron-like triggers for periodic agent tasks",[],{},{"nodeType":1921,"data":23618,"content":23619},{},[23620],{"nodeType":1907,"value":23621,"marks":23622,"data":23624},"When is it Important to Write Agents Output Storage:",[23623],{"type":13576},{},{"nodeType":1921,"data":23626,"content":23627},{},[23628],{"nodeType":1907,"value":23629,"marks":23630,"data":23631},"In-memory storage is used for short-lived, task-specific context such as conversational state, temporary results, inter-agent messages, or intermediate computations. This minimizes latency and provides fast, stateful interactions crucial for responsiveness in ongoing agent flows.",[],{},{"nodeType":1921,"data":23633,"content":23634},{},[23635],{"nodeType":1907,"value":23636,"marks":23637,"data":23638},"Persistent storage (databases, object stores, file systems) holds data that must survive agent restarts, session losses, or system failures, such as project histories, decision logs, audit trails, user profiles, and learned strategies or policies. This ensures reliability, traceability, and long-term knowledge accumulation",[],{},{"nodeType":1921,"data":23640,"content":23641},{},[23642],{"nodeType":1907,"value":23643,"marks":23644,"data":23645},"Write everything to persistent storage when:",[],{},{"nodeType":12447,"data":23647,"content":23648},{},[23649,23659,23669,23679],{"nodeType":12451,"data":23650,"content":23651},{},[23652],{"nodeType":1921,"data":23653,"content":23654},{},[23655],{"nodeType":1907,"value":23656,"marks":23657,"data":23658},"Regulatory or compliance requirements mandate traceability (finance, healthcare)",[],{},{"nodeType":12451,"data":23660,"content":23661},{},[23662],{"nodeType":1921,"data":23663,"content":23664},{},[23665],{"nodeType":1907,"value":23666,"marks":23667,"data":23668},"System reliability is paramount",[],{},{"nodeType":12451,"data":23670,"content":23671},{},[23672],{"nodeType":1921,"data":23673,"content":23674},{},[23675],{"nodeType":1907,"value":23676,"marks":23677,"data":23678},"Agents work on long-horizon tasks",[],{},{"nodeType":12451,"data":23680,"content":23681},{},[23682],{"nodeType":1921,"data":23683,"content":23684},{},[23685],{"nodeType":1907,"value":23686,"marks":23687,"data":23688},"Agent interactions should be inspectable, replayable, or learning-enabled for future deployments.",[],{},{"nodeType":19541,"data":23690,"content":23691},{},[23692],{"nodeType":1907,"value":23693,"marks":23694,"data":23695},"2. Event-Driven Coordination: Communication Through the Environment",[],{},{"nodeType":1921,"data":23697,"content":23698},{},[23699],{"nodeType":1907,"value":23700,"marks":23701,"data":23702},"Agents don’t communicate through direct messages alone. They communicate through the environment — by modifying shared state, writing files, updating tables, or inserting vectors. Other agents observe these changes and react.",[],{},{"nodeType":1921,"data":23704,"content":23705},{},[23706],{"nodeType":1907,"value":23707,"marks":23708,"data":23709},"This is a fundamentally different communication model from traditional RPC or message-passing. Instead of AgentA explicitly calling AgentB, AgentA modifies the environment (writes a file, updates a table), and the platform automatically triggers AgentB based on registered event patterns.",[],{},{"nodeType":1921,"data":23711,"content":23712},{},[23713],{"nodeType":1907,"value":23714,"marks":23715,"data":23717},"Environment-Driven Agent Triggers",[23716],{"type":13576},{},{"nodeType":1921,"data":23719,"content":23720},{},[23721],{"nodeType":1907,"value":23722,"marks":23723,"data":23724},"Agents register themselves to be triggered by specific environmental changes:",[],{},{"nodeType":12447,"data":23726,"content":23727},{},[23728,23743,23758,23773],{"nodeType":12451,"data":23729,"content":23730},{},[23731],{"nodeType":1921,"data":23732,"content":23733},{},[23734,23739],{"nodeType":1907,"value":23735,"marks":23736,"data":23738},"File system events",[23737],{"type":13576},{},{"nodeType":1907,"value":23740,"marks":23741,"data":23742},": An agent triggers when a file matching \u002Fvast\u002Fdatasets\u002Fsales_*.parquet is created",[],{},{"nodeType":12451,"data":23744,"content":23745},{},[23746],{"nodeType":1921,"data":23747,"content":23748},{},[23749,23754],{"nodeType":1907,"value":23750,"marks":23751,"data":23753},"Database mutations",[23752],{"type":13576},{},{"nodeType":1907,"value":23755,"marks":23756,"data":23757},": An agent triggers when a new row is inserted into the pending_tasks table",[],{},{"nodeType":12451,"data":23759,"content":23760},{},[23761],{"nodeType":1921,"data":23762,"content":23763},{},[23764,23769],{"nodeType":1907,"value":23765,"marks":23766,"data":23768},"Vector updates",[23767],{"type":13576},{},{"nodeType":1907,"value":23770,"marks":23771,"data":23772},": An agent triggers when new embeddings are added to the knowledge base",[],{},{"nodeType":12451,"data":23774,"content":23775},{},[23776],{"nodeType":1921,"data":23777,"content":23778},{},[23779,23784],{"nodeType":1907,"value":23780,"marks":23781,"data":23783},"Time-based events",[23782],{"type":13576},{},{"nodeType":1907,"value":23785,"marks":23786,"data":23787},": An agent triggers on a schedule to perform periodic analysis",[],{},{"nodeType":1921,"data":23789,"content":23790},{},[23791],{"nodeType":1907,"value":23792,"marks":23793,"data":23794},"Here’s an example workflow:",[],{},{"nodeType":18247,"data":23796,"content":23797},{},[23798,23808,23818,23828,23838,23848],{"nodeType":12451,"data":23799,"content":23800},{},[23801],{"nodeType":1921,"data":23802,"content":23803},{},[23804],{"nodeType":1907,"value":23805,"marks":23806,"data":23807},"DataCollectorAgent writes a new file: \u002Fvast\u002Fdatasets\u002Fsales_2025_q3.parquet",[],{},{"nodeType":12451,"data":23809,"content":23810},{},[23811],{"nodeType":1921,"data":23812,"content":23813},{},[23814],{"nodeType":1907,"value":23815,"marks":23816,"data":23817},"The file write generates a file.created event on the Event Broker",[],{},{"nodeType":12451,"data":23819,"content":23820},{},[23821],{"nodeType":1921,"data":23822,"content":23823},{},[23824],{"nodeType":1907,"value":23825,"marks":23826,"data":23827},"DataValidatorAgent, registered to trigger on file.created events in \u002Fvast\u002Fdatasets\u002F, automatically spawns",[],{},{"nodeType":12451,"data":23829,"content":23830},{},[23831],{"nodeType":1921,"data":23832,"content":23833},{},[23834],{"nodeType":1907,"value":23835,"marks":23836,"data":23837},"DataValidatorAgent reads the file, validates it, and updates a database table with status = “validated”",[],{},{"nodeType":12451,"data":23839,"content":23840},{},[23841],{"nodeType":1921,"data":23842,"content":23843},{},[23844],{"nodeType":1907,"value":23845,"marks":23846,"data":23847},"The table update generates a table.updated event",[],{},{"nodeType":12451,"data":23849,"content":23850},{},[23851],{"nodeType":1921,"data":23852,"content":23853},{},[23854],{"nodeType":1907,"value":23855,"marks":23856,"data":23857},"AnalysisPipelineAgent, registered to trigger on validation status changes, automatically spawns",[],{},{"nodeType":1921,"data":23859,"content":23860},{},[23861],{"nodeType":1907,"value":23862,"marks":23863,"data":23864},"No agent explicitly called another agent. They communicated through changes to the shared environment, with the Event Broker mediating the triggers.",[],{},{"nodeType":1921,"data":23866,"content":23867},{},[23868],{"nodeType":1907,"value":23869,"marks":23870,"data":23872},"Why This Model Scales",[23871],{"type":13576},{},{"nodeType":1921,"data":23874,"content":23875},{},[23876],{"nodeType":1907,"value":23877,"marks":23878,"data":23879},"Direct agent-to-agent messaging requires agents to know about each other. At scale (10,000+ agents), this creates a dependency graph nightmare. When 10,000 agents are operating simultaneously, each producing dozens of state changes per second, you’re looking at hundreds of thousands to millions of events per second. Most event systems collapse under this load.",[],{},{"nodeType":1921,"data":23881,"content":23882},{},[23883],{"nodeType":1907,"value":23884,"marks":23885,"data":23886},"Environment-driven communication decouples agents completely:",[],{},{"nodeType":12447,"data":23888,"content":23889},{},[23890,23900,23910],{"nodeType":12451,"data":23891,"content":23892},{},[23893],{"nodeType":1921,"data":23894,"content":23895},{},[23896],{"nodeType":1907,"value":23897,"marks":23898,"data":23899},"Agents don’t need to know which other agents exist",[],{},{"nodeType":12451,"data":23901,"content":23902},{},[23903],{"nodeType":1921,"data":23904,"content":23905},{},[23906],{"nodeType":1907,"value":23907,"marks":23908,"data":23909},"New agents can be added without modifying existing agents",[],{},{"nodeType":12451,"data":23911,"content":23912},{},[23913],{"nodeType":1921,"data":23914,"content":23915},{},[23916],{"nodeType":1907,"value":23917,"marks":23918,"data":23919},"Failed agents don’t cause cascading failures—their work simply remains in the environment until they recover",[],{},{"nodeType":1921,"data":23921,"content":23922},{},[23923,23926,23934],{"nodeType":1907,"value":2031,"marks":23924,"data":23925},[],{},{"nodeType":1916,"data":23927,"content":23929},{"uri":23928},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fannouncing-the-vast-event-broker",[23930],{"nodeType":1907,"value":23931,"marks":23932,"data":23933},"VAST’s Event Broker",[],{},{"nodeType":1907,"value":23935,"marks":23936,"data":23937}," is architecturally different because it’s not a separate service; it’s integrated directly into the storage fabric. This integration provides scalability and performance characteristics that are critical for multi-agent environments. The broker doesn’t maintain events in separate in-memory buffers or on dedicated broker nodes. Events are written directly to D-Nodes as structured data on the same storage fabric that handles all other I\u002FO. This means:",[],{},{"nodeType":12447,"data":23939,"content":23940},{},[23941,23956,23971],{"nodeType":12451,"data":23942,"content":23943},{},[23944],{"nodeType":1921,"data":23945,"content":23946},{},[23947,23952],{"nodeType":1907,"value":23948,"marks":23949,"data":23951},"No broker bottleneck",[23950],{"type":13576},{},{"nodeType":1907,"value":23953,"marks":23954,"data":23955},": Event capacity scales with storage capacity, not broker node count",[],{},{"nodeType":12451,"data":23957,"content":23958},{},[23959],{"nodeType":1921,"data":23960,"content":23961},{},[23962,23967],{"nodeType":1907,"value":23963,"marks":23964,"data":23966},"Unlimited retention",[23965],{"type":13576},{},{"nodeType":1907,"value":23968,"marks":23969,"data":23970},": Events can persist indefinitely because storage is cheap and abundant",[],{},{"nodeType":12451,"data":23972,"content":23973},{},[23974],{"nodeType":1921,"data":23975,"content":23976},{},[23977,23982],{"nodeType":1907,"value":23978,"marks":23979,"data":23981},"Parallel consumption",[23980],{"type":13576},{},{"nodeType":1907,"value":23983,"marks":23984,"data":23985},": Thousands of agents can consume events concurrently without overwhelming broker nodes—they’re reading directly from distributed storage",[],{},{"nodeType":1921,"data":23987,"content":23988},{},[23989],{"nodeType":1907,"value":23990,"marks":23991,"data":23992},"Because events are part of the storage fabric:",[],{},{"nodeType":12447,"data":23994,"content":23995},{},[23996,24011,24026],{"nodeType":12451,"data":23997,"content":23998},{},[23999],{"nodeType":1921,"data":24000,"content":24001},{},[24002,24007],{"nodeType":1907,"value":24003,"marks":24004,"data":24006},"Sub-millisecond publish latency",[24005],{"type":13576},{},{"nodeType":1907,"value":24008,"marks":24009,"data":24010},": Writing an event is a storage write, leveraging NVMe and RDMA",[],{},{"nodeType":12451,"data":24012,"content":24013},{},[24014],{"nodeType":1921,"data":24015,"content":24016},{},[24017,24022],{"nodeType":1907,"value":24018,"marks":24019,"data":24021},"Massive parallel throughput",[24020],{"type":13576},{},{"nodeType":1907,"value":24023,"marks":24024,"data":24025},": The storage fabric can handle millions of events per second across the cluster",[],{},{"nodeType":12451,"data":24027,"content":24028},{},[24029],{"nodeType":1921,"data":24030,"content":24031},{},[24032,24037],{"nodeType":1907,"value":24033,"marks":24034,"data":24036},"No data movement for triggers",[24035],{"type":13576},{},{"nodeType":1907,"value":24038,"marks":24039,"data":24040},": When an event triggers a DataEngine function, the function executes on the same C-Node where the event is stored—zero data movement",[],{},{"nodeType":19541,"data":24042,"content":24043},{},[24044],{"nodeType":1907,"value":24045,"marks":24046,"data":24047},"3. State Management: Transactional Tables, Vectors, and Consistency",[],{},{"nodeType":1921,"data":24049,"content":24050},{},[24051],{"nodeType":1907,"value":24052,"marks":24053,"data":24054},"Agent state must be persistent, queryable, and consistent. This is where most traditional agent architectures fail—they rely on external databases (e.g., Postgres for state, Pinecone for vectors) which introduces latency, complexity, and data movement.",[],{},{"nodeType":1921,"data":24056,"content":24057},{},[24058],{"nodeType":1907,"value":24059,"marks":24060,"data":24061},"VAST DataBase unifies three capabilities in a single storage layer:",[],{},{"nodeType":18247,"data":24063,"content":24064},{},[24065,24080,24095],{"nodeType":12451,"data":24066,"content":24067},{},[24068],{"nodeType":1921,"data":24069,"content":24070},{},[24071,24076],{"nodeType":1907,"value":24072,"marks":24073,"data":24075},"OLTP: ",[24074],{"type":13576},{},{"nodeType":1907,"value":24077,"marks":24078,"data":24079},"Transactional state (agent metadata, task status, workflow state)",[],{},{"nodeType":12451,"data":24081,"content":24082},{},[24083],{"nodeType":1921,"data":24084,"content":24085},{},[24086,24091],{"nodeType":1907,"value":24087,"marks":24088,"data":24090},"OLAP:",[24089],{"type":13576},{},{"nodeType":1907,"value":24092,"marks":24093,"data":24094}," Analytical queries (aggregate agent performance, system metrics)",[],{},{"nodeType":12451,"data":24096,"content":24097},{},[24098],{"nodeType":1921,"data":24099,"content":24100},{},[24101,24106],{"nodeType":1907,"value":24102,"marks":24103,"data":24105},"Vector storage:",[24104],{"type":13576},{},{"nodeType":1907,"value":24107,"marks":24108,"data":24109}," Embeddings for RAG, semantic search, and memory retrieval",[],{},{"nodeType":1921,"data":24111,"content":24112},{},[24113],{"nodeType":1907,"value":24114,"marks":24115,"data":24116},"The DataBase is built on VAST’s disaggregated storage architecture. Data is stored in columnar format on D-Nodes, with multiple layers of indexing:",[],{},{"nodeType":12447,"data":24118,"content":24119},{},[24120,24135,24150],{"nodeType":12451,"data":24121,"content":24122},{},[24123],{"nodeType":1921,"data":24124,"content":24125},{},[24126,24131],{"nodeType":1907,"value":24127,"marks":24128,"data":24130},"Row-based index: ",[24129],{"type":13576},{},{"nodeType":1907,"value":24132,"marks":24133,"data":24134},"For transactional lookups (e.g., “fetch state for agent_id=123”)",[],{},{"nodeType":12451,"data":24136,"content":24137},{},[24138],{"nodeType":1921,"data":24139,"content":24140},{},[24141,24146],{"nodeType":1907,"value":24142,"marks":24143,"data":24145},"Column-based index:",[24144],{"type":13576},{},{"nodeType":1907,"value":24147,"marks":24148,"data":24149}," For analytical scans (e.g., “count all agents with status=failed”)",[],{},{"nodeType":12451,"data":24151,"content":24152},{},[24153],{"nodeType":1921,"data":24154,"content":24155},{},[24156,24161],{"nodeType":1907,"value":24157,"marks":24158,"data":24160},"Vector index:",[24159],{"type":13576},{},{"nodeType":1907,"value":24162,"marks":24163,"data":24164}," approximate nearest neighbor search for embeddings",[],{},{"nodeType":1921,"data":24166,"content":24167},{},[24168],{"nodeType":1907,"value":24169,"marks":24170,"data":24172},"Vector Operations at Scale",[24171],{"type":13576},{},{"nodeType":1921,"data":24174,"content":24175},{},[24176],{"nodeType":1907,"value":24177,"marks":24178,"data":24179},"The vector storage subsystem is designed for trillion-scale vector search. Key characteristics include:",[],{},{"nodeType":12447,"data":24181,"content":24182},{},[24183,24198,24213],{"nodeType":12451,"data":24184,"content":24185},{},[24186],{"nodeType":1921,"data":24187,"content":24188},{},[24189,24194],{"nodeType":1907,"value":24190,"marks":24191,"data":24193},"Parallel updates",[24192],{"type":13576},{},{"nodeType":1907,"value":24195,"marks":24196,"data":24197},": Unlike traditional vector DBs that require full re-indexing, VAST’s parallel architecture allows concurrent inserts without locking the entire index",[],{},{"nodeType":12451,"data":24199,"content":24200},{},[24201],{"nodeType":1921,"data":24202,"content":24203},{},[24204,24209],{"nodeType":1907,"value":24205,"marks":24206,"data":24208},"Freshness of data: ",[24207],{"type":13576},{},{"nodeType":1907,"value":24210,"marks":24211,"data":24212},"All of the data is searchable, including data that was not already converted to the vectorDB",[],{},{"nodeType":12451,"data":24214,"content":24215},{},[24216],{"nodeType":1921,"data":24217,"content":24218},{},[24219,24224],{"nodeType":1907,"value":24220,"marks":24221,"data":24223},"Trillion-vector scale",[24222],{"type":13576},{},{"nodeType":1907,"value":24225,"marks":24226,"data":24227},": No sharding and no performance degradation at scale",[],{},{"nodeType":19541,"data":24229,"content":24230},{},[24231],{"nodeType":1907,"value":24232,"marks":24233,"data":24234},"4. The Memory Hierarchy: KV Cache Offloading for GPU Efficiency and Endless Context",[],{},{"nodeType":1921,"data":24236,"content":24237},{},[24238],{"nodeType":1907,"value":24239,"marks":24240,"data":24241},"In multi-agent systems, the GPU inference layer is the most expensive and constrained resource. When thousands of agents are simultaneously calling LLM reasoning services, GPU utilization becomes the critical bottleneck that determines system throughput and cost efficiency.",[],{},{"nodeType":1921,"data":24243,"content":24244},{},[24245,24248,24255],{"nodeType":1907,"value":19621,"marks":24246,"data":24247},[],{},{"nodeType":1916,"data":24249,"content":24250},{"uri":19449},[24251],{"nodeType":1907,"value":24252,"marks":24253,"data":24254},"KV cache is the key to unlocking high GPU utilization,",[],{},{"nodeType":1907,"value":24256,"marks":24257,"data":24258}," reducing latency of response, and the ability to support endless context conversations that multi-agent systems require.",[],{},{"nodeType":1921,"data":24260,"content":24261},{},[24262],{"nodeType":1907,"value":24263,"marks":24264,"data":24266},"The GPU Utilization Problem",[24265],{"type":13576},{},{"nodeType":1921,"data":24268,"content":24269},{},[24270],{"nodeType":1907,"value":24271,"marks":24272,"data":24273},"A GPU’s compute capacity is wasted if its memory (HBM) fills up before its processors are fully utilized. ",[],{},{"nodeType":1921,"data":24275,"content":24276},{},[24277],{"nodeType":1907,"value":24278,"marks":24279,"data":24280},"Long-context inference on models like Llama 3.1 70B faces different bottlenecks across its two phases. The prefill phase to compute a 128K-token KV cache (~40 GB in FP16, 50% of an H100 80GB’s memory) saturates tensor cores at 60-90% utilization—expensive computation you’d want to reuse whenever possible. The subsequent autoregressive decode phase becomes memory-bound, dropping to only 20-30% utilization as each step fetches the entire KV cache but performs minimal computation. With model weights at ~140 GB in FP16, a 4×H100 80GB configuration (320 GB total HBM) leaves only ~20-25 GB per GPU for KV cache after accounting for operational overhead. This memory constraint severely limits concurrent long-context requests, forcing deployments to provision 3-5× more GPUs than compute needs would suggest—purely for memory capacity to hold KV caches. Even with optimizations like FP8 quantization, continuous batching, and multi-query attention, memory capacity remains the primary scaling constraint.",[],{},{"nodeType":1921,"data":24282,"content":24283},{},[24284],{"nodeType":1907,"value":24285,"marks":24286,"data":24287},"The result: you’re paying for a $30,000 GPU but only using a fraction of its processing power because memory is the bottleneck, not compute. At scale, this means you need 3-5x more GPUs than you should, purely due to memory constraints.",[],{},{"nodeType":1921,"data":24289,"content":24290},{},[24291],{"nodeType":1907,"value":24292,"marks":24293,"data":24295},"The Solution: KV Cache Offloading",[24294],{"type":13576},{},{"nodeType":1921,"data":24297,"content":24298},{},[24299],{"nodeType":1907,"value":24300,"marks":24301,"data":24302},"By offloading the KV cache from GPU HBM to VAST’s high-speed storage fabric, you break the memory bottleneck:",[],{},{"nodeType":12447,"data":24304,"content":24305},{},[24306,24316,24326],{"nodeType":12451,"data":24307,"content":24308},{},[24309],{"nodeType":1921,"data":24310,"content":24311},{},[24312],{"nodeType":1907,"value":24313,"marks":24314,"data":24315},"Hot cache blocks (actively being used for generation) stay in GPU HBM",[],{},{"nodeType":12451,"data":24317,"content":24318},{},[24319],{"nodeType":1921,"data":24320,"content":24321},{},[24322],{"nodeType":1907,"value":24323,"marks":24324,"data":24325},"Cold cache blocks (common prefixes, older context, inactive requests) are moved to VAST storage",[],{},{"nodeType":12451,"data":24327,"content":24328},{},[24329],{"nodeType":1921,"data":24330,"content":24331},{},[24332],{"nodeType":1907,"value":24333,"marks":24334,"data":24335},"When needed, cold blocks are fetched back to HBM in under 1ms",[],{},{"nodeType":1921,"data":24337,"content":24338},{},[24339],{"nodeType":1907,"value":24340,"marks":24341,"data":24342},"This dramatically increases GPU utilization because memory is no longer the constraint. You can now serve 50-100+ concurrent agent requests per GPU instead of 10-20, fully utilizing the GPU’s compute capacity.",[],{},{"nodeType":1921,"data":24344,"content":24345},{},[24346],{"nodeType":1907,"value":24347,"marks":24348,"data":24350},"Enabling Endless Context for Multi-Agent Systems",[24349],{"type":13576},{},{"nodeType":1921,"data":24352,"content":24353},{},[24354],{"nodeType":1907,"value":24355,"marks":24356,"data":24357},"Multi-agent systems require agents to maintain long-running context across multiple interactions. A ResearchAgent might accumulate more than 100,000 tokens of context over hours of operation. A ConversationAgent might maintain weeks of chat history. Without KV cache offloading, this is impossible—the context simply won’t fit in GPU memory.",[],{},{"nodeType":1921,"data":24359,"content":24360},{},[24361],{"nodeType":1907,"value":24362,"marks":24363,"data":24364},"With KV cache offloading:",[],{},{"nodeType":12447,"data":24366,"content":24367},{},[24368,24378,24388],{"nodeType":12451,"data":24369,"content":24370},{},[24371],{"nodeType":1921,"data":24372,"content":24373},{},[24374],{"nodeType":1907,"value":24375,"marks":24376,"data":24377},"Agents can maintain effectively unlimited context length—bounded only by storage, not GPU memory",[],{},{"nodeType":12451,"data":24379,"content":24380},{},[24381],{"nodeType":1921,"data":24382,"content":24383},{},[24384],{"nodeType":1907,"value":24385,"marks":24386,"data":24387},"Long-running agents don’t need to truncate or summarize their context to fit memory constraints",[],{},{"nodeType":12451,"data":24389,"content":24390},{},[24391],{"nodeType":1921,"data":24392,"content":24393},{},[24394],{"nodeType":1907,"value":24395,"marks":24396,"data":24397},"Multi-turn reasoning tasks can operate over massive context windows without performance degradation",[],{},{"nodeType":1921,"data":24399,"content":24400},{},[24401],{"nodeType":1907,"value":24402,"marks":24403,"data":24405},"The Technical Mechanism",[24404],{"type":13576},{},{"nodeType":1921,"data":24407,"content":24408},{},[24409],{"nodeType":1907,"value":24410,"marks":24411,"data":24412},"VAST achieves KV cache offloading by integrating with inference frameworks (e.g., NVIDIA Dynamo, LMCache) at the KV cache layer:",[],{},{"nodeType":12447,"data":24414,"content":24415},{},[24416,24426,24436,24446,24456],{"nodeType":12451,"data":24417,"content":24418},{},[24419],{"nodeType":1921,"data":24420,"content":24421},{},[24422],{"nodeType":1907,"value":24423,"marks":24424,"data":24425},"The inference engine generates KV cache blocks during forward passes",[],{},{"nodeType":12451,"data":24427,"content":24428},{},[24429],{"nodeType":1921,"data":24430,"content":24431},{},[24432],{"nodeType":1907,"value":24433,"marks":24434,"data":24435},"The cache manager (modified to use VAST as backing storage) keeps frequently-accessed blocks in GPU HBM",[],{},{"nodeType":12451,"data":24437,"content":24438},{},[24439],{"nodeType":1921,"data":24440,"content":24441},{},[24442],{"nodeType":1907,"value":24443,"marks":24444,"data":24445},"When HBM fills up, cold blocks are evicted to VAST storage via high-speed NVMe and RDMA",[],{},{"nodeType":12451,"data":24447,"content":24448},{},[24449],{"nodeType":1921,"data":24450,"content":24451},{},[24452],{"nodeType":1907,"value":24453,"marks":24454,"data":24455},"On a cache miss, the block is fetched from VAST storage with sub-millisecond latency",[],{},{"nodeType":12451,"data":24457,"content":24458},{},[24459],{"nodeType":1921,"data":24460,"content":24461},{},[24462],{"nodeType":1907,"value":24463,"marks":24464,"data":24465},"Prefetch hints and access patterns allow proactive loading of likely-needed blocks",[],{},{"nodeType":1921,"data":24467,"content":24468},{},[24469],{"nodeType":1907,"value":24470,"marks":24471,"data":24472},"Technically, there is a trade-off when offloading KV cache to external storage: generation latency increases by 1-2ms per cache miss. 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As costs add up fast and GPU efficiency becomes a bottleneck, KV cache offloading is a crucial component in multi-agent deployments.",[],{},{"nodeType":19541,"data":24529,"content":24530},{},[24531],{"nodeType":1907,"value":24532,"marks":24533,"data":24534},"5. Security and Isolation: Identity, Permissions, and Audit",[],{},{"nodeType":1921,"data":24536,"content":24537},{},[24538],{"nodeType":1907,"value":24539,"marks":24540,"data":24541},"When thousands of agents are executing autonomously, security cannot be an afterthought. The traditional approach—giving each service a long-lived API key or shared credentials—breaks down catastrophically at scale. A compromised key becomes a skeleton key to your entire system. Credential rotation becomes an operational nightmare. And when something goes wrong, tracing which agent did what becomes nearly impossible.",[],{},{"nodeType":1921,"data":24543,"content":24544},{},[24545],{"nodeType":1907,"value":24546,"marks":24547,"data":24548},"The alternative is to treat agents as first-class principals in your security model, with the same rigor you’d apply to human users or external services. Each agent must have:",[],{},{"nodeType":18247,"data":24550,"content":24551},{},[24552,24567,24582],{"nodeType":12451,"data":24553,"content":24554},{},[24555],{"nodeType":1921,"data":24556,"content":24557},{},[24558,24563],{"nodeType":1907,"value":24559,"marks":24560,"data":24562},"Identity",[24561],{"type":13576},{},{"nodeType":1907,"value":24564,"marks":24565,"data":24566},": Who is this agent?",[],{},{"nodeType":12451,"data":24568,"content":24569},{},[24570],{"nodeType":1921,"data":24571,"content":24572},{},[24573,24578],{"nodeType":1907,"value":24574,"marks":24575,"data":24577},"Authorization",[24576],{"type":13576},{},{"nodeType":1907,"value":24579,"marks":24580,"data":24581},": What can this agent access?",[],{},{"nodeType":12451,"data":24583,"content":24584},{},[24585],{"nodeType":1921,"data":24586,"content":24587},{},[24588,24593],{"nodeType":1907,"value":24589,"marks":24590,"data":24592},"Auditability",[24591],{"type":13576},{},{"nodeType":1907,"value":24594,"marks":24595,"data":24596},": What did this agent do?",[],{},{"nodeType":1921,"data":24598,"content":24599},{},[24600],{"nodeType":1907,"value":24601,"marks":24602,"data":24604},"Identity Fabric: Short-Lived Credentials",[24603],{"type":13576},{},{"nodeType":1921,"data":24606,"content":24607},{},[24608],{"nodeType":1907,"value":24609,"marks":24610,"data":24611},"Agents don’t have static API keys. 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Pair this with additional supply chain issues around memory chips overall, and it looks like a pretty dire picture, indeed. AI giveth new capabilities, and AI taketh all available chip-manufacturing capacity.",[],{},{"nodeType":1921,"data":29513,"content":29514},{},[29515],{"nodeType":1907,"value":29516,"marks":29517,"data":29518},"If there’s a silver lining, however, it’s this: Shortages are a great forcing function to reconsider how we’ve been doing things and whether “just buy more” was ever really a viable strategy. 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And with VAST, the amount of storage you actually need is likely significantly less than with other data platforms.",[],{},{"nodeType":1921,"data":29568,"content":29569},{},[29570,29574,29582,29586,29591,29595,29600,29604,29609],{"nodeType":1907,"value":29571,"marks":29572,"data":29573},"Back in September, in fact, one of our major hardware suppliers, Solidigm, ",[],{},{"nodeType":1916,"data":29575,"content":29577},{"uri":29576},"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html",[29578],{"nodeType":1907,"value":29579,"marks":29580,"data":29581},"published a whitepaper",[],{},{"nodeType":1907,"value":29583,"marks":29584,"data":29585}," detailing (like, in great detail) the efficiencies of running VAST on Solidigm SSDs. They found that the combination can ",[],{},{"nodeType":1907,"value":29587,"marks":29588,"data":29590},"cut TCO",[29589],{"type":13576},{},{"nodeType":1907,"value":29592,"marks":29593,"data":29594}," for an exabyte of storage ",[],{},{"nodeType":1907,"value":29596,"marks":29597,"data":29599},"by nearly 60% ",[29598],{"type":13576},{},{"nodeType":1907,"value":29601,"marks":29602,"data":29603},"compared with storing the same data on hard disks, and ",[],{},{"nodeType":1907,"value":29605,"marks":29606,"data":29608},"compressed the hardware footprint from 52 racks to 5 racks",[29607],{"type":13576},{},{"nodeType":1907,"value":1920,"marks":29610,"data":29611},[],{},{"nodeType":1921,"data":29613,"content":29614},{},[29615,29619,29628],{"nodeType":1907,"value":29616,"marks":29617,"data":29618},"We supplemented the whitepaper with ",[],{},{"nodeType":1916,"data":29620,"content":29622},{"uri":29621},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity",[29623],{"nodeType":1907,"value":29624,"marks":29625,"data":29627},"a blog post that addresses the findings",[29626],{"type":13127},{},{"nodeType":1907,"value":29629,"marks":29630,"data":29631}," at a higher level, some highlights from which are here:",[],{},{"nodeType":12447,"data":29633,"content":29634},{},[29635,29657,29680],{"nodeType":12451,"data":29636,"content":29637},{},[29638],{"nodeType":1921,"data":29639,"content":29640},{},[29641,29645,29653],{"nodeType":1907,"value":29642,"marks":29643,"data":29644},"Solidigm’s ",[],{},{"nodeType":1916,"data":29646,"content":29647},{"uri":29576},[29648],{"nodeType":1907,"value":29649,"marks":29650,"data":29652},"122TB drive, based on 192-layer, QLC NAND flash",[29651],{"type":13127},{},{"nodeType":1907,"value":29654,"marks":29655,"data":29656},", provides unprecedented storage density. 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When duplicate data is removed, many sequential reads look like random IO, which flash drives excel at, but HDDs struggle with.",[],{},{"nodeType":12451,"data":29681,"content":29682},{},[29683],{"nodeType":1921,"data":29684,"content":29685},{},[29686,29689,29697],{"nodeType":1907,"value":2031,"marks":29687,"data":29688},[],{},{"nodeType":1916,"data":29690,"content":29691},{"uri":22399},[29692],{"nodeType":1907,"value":29693,"marks":29694,"data":29696},"Locally decodable codes",[29695],{"type":13127},{},{"nodeType":1907,"value":29698,"marks":29699,"data":29700}," provide an extremely efficient and resilient form of data protection, further enhancing cost-effectiveness by reducing wasted overhead.",[],{},{"nodeType":12289,"data":29702,"content":29723},{"target":29703},{"metadata":29704,"sys":29707,"fields":29714},{"tags":29705,"concepts":29706},[],[],{"space":29708,"id":29710,"type":934,"createdAt":29711,"updatedAt":29711,"environment":29712,"publishedVersion":847,"revision":1136,"locale":673},{"sys":29709},{"type":657,"linkType":658,"id":659},"3E6tKrtcliZ4T9MdGRdogh","2025-09-15T14:07:10.188Z",{"sys":29713},{"id":665,"type":657,"linkType":666},{"title":29715,"description":29715,"file":29716},"Calculated Raw Capacity Required (EB)",{"url":29717,"details":29718,"fileName":29722,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3E6tKrtcliZ4T9MdGRdogh\u002F8fe6bbe268068d2fc2bcfc16aa0f2ef3\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp",{"size":29719,"image":29720},26834,{"width":1144,"height":29721},939,"new-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp",[],{"nodeType":1921,"data":29725,"content":29726},{},[29727],{"nodeType":1907,"value":29728,"marks":29729,"data":29730},"More broadly, though, the VAST DataStore is just built for efficiency. Global namespace? Check. Block, file, and object storage on a single platform? Check. Automatic and real-time deduplication, similarity reduction, and compression across all of it? Check.",[],{},{"nodeType":1921,"data":29732,"content":29733},{},[29734,29737,29746,29750,29759],{"nodeType":1907,"value":2031,"marks":29735,"data":29736},[],{},{"nodeType":1916,"data":29738,"content":29740},{"uri":29739},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers",[29741],{"nodeType":1907,"value":29742,"marks":29743,"data":29745},"Our customers",[29744],{"type":13127},{},{"nodeType":1907,"value":29747,"marks":29748,"data":29749}," are happy to share their stories of revamping their storage architectures with VAST, and this quote from ",[],{},{"nodeType":1916,"data":29751,"content":29753},{"uri":29752},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Flola-vfx",[29754],{"nodeType":1907,"value":29755,"marks":29756,"data":29758},"Lola VFX",[29757],{"type":13127},{},{"nodeType":1907,"value":29760,"marks":29761,"data":29762}," founder Edson Williams sums up a lot of what we hear:",[],{},{"nodeType":29764,"data":29765,"content":29766},"blockquote",{},[29767],{"nodeType":1921,"data":29768,"content":29769},{},[29770],{"nodeType":1907,"value":29771,"marks":29772,"data":29773},"After we evaluated all our options, we chose VAST because of the scalability, the performance, and the value. 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years, the storage industry has been conditioned to treat shortages as temporary. Cyclical downturns, followed by density gains, followed by relief.",{"data":30144,"content":30145,"nodeType":1921},{},[30146,30150,30158],{"data":30147,"marks":30148,"value":30149,"nodeType":1907},{},[],"But as we unpack in a",{"data":30151,"content":30153,"nodeType":1916},{"uri":30152},"https:\u002F\u002Fshared-everything.simplecast.com\u002Fepisodes\u002Fbeating-the-flash-crunch-with-efficiency-not-supply",[30154],{"data":30155,"marks":30156,"value":30157,"nodeType":1907},{},[]," recent episode of the Shared Everything",{"data":30159,"marks":30160,"value":30161,"nodeType":1907},{},[]," podcast from VAST, what’s unfolding now looks different.",{"data":30163,"content":30164,"nodeType":1921},{},[30165],{"data":30166,"marks":30167,"value":30168,"nodeType":1907},{},[],"The current flash supply crunch is not simply a matter of demand outrunning manufacturing for a quarter or two. Instead we’re looking at the collision of AI-scale consumption with the physical limits of NAND production, the long timelines of fab expansion, and a market that no longer has meaningful slack.",{"data":30170,"content":30171,"nodeType":1921},{},[30172],{"data":30173,"marks":30174,"value":30175,"nodeType":1907},{},[],"That reality is forcing a reframing of where leverage actually exists, as VAST Co-Founder Jeff Denworth and Solidigm’s Scott Shadley explain.",{"data":30177,"content":30178,"nodeType":1921},{},[30179],{"data":30180,"marks":30181,"value":30182,"nodeType":1907},{},[],"AI infrastructure has changed the profile of storage demand in three important ways. First, the sheer volume of data being ingested has exploded. Training pipelines, inference logs, embeddings, checkpoints, and intermediate artifacts all want to live close to compute and stay accessible.",{"data":30184,"content":30185,"nodeType":1921},{},[30186],{"data":30187,"marks":30188,"value":30189,"nodeType":1907},{},[],"Second, access patterns have shifted. Random access is no longer a performance optimization; it is a requirement.",{"data":30191,"content":30192,"nodeType":1921},{},[30193],{"data":30194,"marks":30195,"value":30196,"nodeType":1907},{},[],"And third, tolerance for latency and inefficiency has collapsed. When GPUs cost tens of thousands of dollars per unit and are deployed by the hundreds of thousands, wasted I\u002FO capacity becomes wasted compute.",{"data":30198,"content":30199,"nodeType":1921},{},[30200],{"data":30201,"marks":30202,"value":30203,"nodeType":1907},{},[],"At the same time, NAND manufacturing has lost the easy gains it once enjoyed. Layer counts continue to rise, but fab throughput does not scale linearly with density. Process times increase, toolchains diverge, and new fabs take years to bring online. Even the hard drive industry, long assumed to be the safety valve during flash shortages, is facing its own mechanical and power constraints. The result is a market where buying more media is no longer a reliable strategy.",{"data":30205,"content":30206,"nodeType":1921},{},[30207],{"data":30208,"marks":30209,"value":30210,"nodeType":1907},{},[],"In conversations with customers navigating the current crunch, one pattern shows up repeatedly. Organizations are discovering that their real flash consumption has far less to do with the amount of data they generate than with how inefficiently that data is stored. Triplicated databases sitting on top of triplicated object stores. Erasure codes chosen for simplicity rather than efficiency. Performance workarounds that multiply capacity requirements by three, six, or nine times.",{"data":30212,"content":30213,"nodeType":1921},{},[30214],{"data":30215,"marks":30216,"value":30217,"nodeType":1907},{},[],"Those design decisions were tolerable when flash was abundant and prices were falling, but they are untenable when allocation is fixed and lead times stretch into quarters.",{"data":30219,"content":30220,"nodeType":1921},{},[30221],{"data":30222,"marks":30223,"value":30224,"nodeType":1907},{},[],"VAST approaches the problem from a different direction, as Denworth outlines in this episode.",{"data":30226,"content":30227,"nodeType":1921},{},[30228,30232,30240,30244,30252,30256,30264],{"data":30229,"marks":30230,"value":30231,"nodeType":1907},{},[],"Instead of assuming flash is cheap and infinite, the system is built around ",{"data":30233,"content":30235,"nodeType":1916},{"uri":30234},"..\u002F..\u002Fblog\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity",[30236],{"data":30237,"marks":30238,"value":30239,"nodeType":1907},{},[],"extracting maximum usable capacity",{"data":30241,"marks":30242,"value":30243,"nodeType":1907},{},[]," from every drive. That starts with ",{"data":30245,"content":30247,"nodeType":1916},{"uri":30246},"..\u002F..\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[30248],{"data":30249,"marks":30250,"value":30251,"nodeType":1907},{},[],"erasure coding",{"data":30253,"marks":30254,"value":30255,"nodeType":1907},{},[]," designed for flash from day one, delivering overhead on the order of a few percent rather than the double-digit penalties common in legacy systems. It continues with ",{"data":30257,"content":30259,"nodeType":1916},{"uri":30258},"..\u002F..\u002Fblog\u002Fbreaking-data-reduction-trade-offs-with-global-compression",[30260],{"data":30261,"marks":30262,"value":30263,"nodeType":1907},{},[],"data reduction that operates globally",{"data":30265,"marks":30266,"value":30267,"nodeType":1907},{},[]," across the dataset, not as a bolt-on feature or a best-effort optimization, but as a first-class architectural principle.",{"data":30269,"content":30270,"nodeType":1921},{},[30271],{"data":30272,"marks":30273,"value":30274,"nodeType":1907},{},[],"This goes well beyond theory. Across production environments, customers consistently see usable capacity expand by multiples relative to raw flash. In practical terms, this means organizations constrained by NAND allocation can meet ingest and retention goals without waiting for the supply chain to catch up. In extreme cases, environments that previously required multiple replicated copies for performance and protection collapse into a single, efficient footprint.",{"data":30276,"content":30277,"nodeType":1921},{},[30278],{"data":30279,"marks":30280,"value":30281,"nodeType":1907},{},[],"This also reframes the flash shortage from a procurement problem into a software problem.",{"data":30283,"content":30284,"nodeType":1921},{},[30285],{"data":30286,"marks":30287,"value":30288,"nodeType":1907},{},[],"What’s notable is how quickly this shift is happening. Efficiency used to be discussed in the context of power, cooling, and cost optimization. Those still matter, but they are no longer the primary drivers. Today, efficiency determines whether a deployment can happen at all. When hyperscalers are willing to place open-ended orders and enterprises are competing for the same constrained supply, the ability to make flash go two or three times further becomes a strategic advantage.",{"data":30290,"content":30291,"nodeType":1921},{},[30292],{"data":30293,"marks":30294,"value":30295,"nodeType":1907},{},[],"There’s also a second-order effect. As AI workloads increasingly demand instant access to data beyond GPU and CPU memory, flash becomes an extension of the compute fabric rather than a passive storage tier. Algorithms that assume random access and consistent latency unlock capabilities that simply are not possible on spinning media or heavily replicated architectures. This tight coupling between compute and data makes inefficiency even more expensive, because it directly impacts utilization upstream.",{"data":30297,"content":30298,"nodeType":1921},{},[30299],{"data":30300,"marks":30301,"value":30302,"nodeType":1907},{},[],"None of this eliminates the need for more flash. Fabs will expand, supply will eventually loosen, and the market will find a new equilibrium. But the lesson of this cycle is unlikely to fade. AI has exposed how fragile capacity planning becomes when it relies on abundance rather than design.",{"data":30304,"content":30305,"nodeType":1921},{},[30306],{"data":30307,"marks":30308,"value":30309,"nodeType":1907},{},[],"In the near term, the organizations that succeed will not be the ones that secured the largest allocations, but the ones that extracted the most value from what they already had. In that sense, the flash crunch is doing something the industry rarely gets forced to do: confront inefficiency head-on, and treat software architecture as a first-order supply chain lever.",{"data":30311,"content":30312,"nodeType":12267},{},[30313],{"data":30314,"marks":30315,"value":30316,"nodeType":1907},{},[],"Efficiency is the new supply. Learn how to maximize every bit.",{"data":30318,"content":30319,"nodeType":12447},{},[30320,30340,30361],{"data":30321,"content":30322,"nodeType":12451},{},[30323],{"data":30324,"content":30325,"nodeType":1921},{},[30326,30330,30337],{"data":30327,"marks":30328,"value":30329,"nodeType":1907},{},[],"Listen:",{"data":30331,"content":30332,"nodeType":1916},{"uri":30152},[30333],{"data":30334,"marks":30335,"value":30336,"nodeType":1907},{},[]," Beating the Flash Crunch With Efficiency, Not Supply (Shared Everything podcast)",{"data":30338,"marks":30339,"value":2031,"nodeType":1907},{},[],{"data":30341,"content":30342,"nodeType":12451},{},[30343],{"data":30344,"content":30345,"nodeType":1921},{},[30346,30350,30358],{"data":30347,"marks":30348,"value":30349,"nodeType":1907},{},[],"Read:",{"data":30351,"content":30353,"nodeType":1916},{"uri":30352},"..\u002F..\u002Fblog\u002Fovercoming-the-ssd-shortage-with-vast",[30354],{"data":30355,"marks":30356,"value":30357,"nodeType":1907},{},[]," Overcoming the SSD Shortage with VAST",{"data":30359,"marks":30360,"value":2031,"nodeType":1907},{},[],{"data":30362,"content":30363,"nodeType":12451},{},[30364],{"data":30365,"content":30366,"nodeType":1921},{},[30367,30371,30379],{"data":30368,"marks":30369,"value":30370,"nodeType":1907},{},[],"Read: ",{"data":30372,"content":30373,"nodeType":1916},{"uri":29576},[30374],{"data":30375,"marks":30376,"value":30378,"nodeType":1907},{},[30377],{"type":13127},"The Economics of Exabyte Storage (Solidigm TCO analysis)",{"data":30380,"marks":30381,"value":2031,"nodeType":1907},{},[],{"data":30383,"content":30384,"nodeType":1921},{},[30385],{"data":30386,"marks":30387,"value":2031,"nodeType":1907},{},[],[30389,30414,30439,30464,30489],{"metadata":30390,"sys":30393,"fields":30400},{"tags":30391,"concepts":30392},[],[],{"space":30394,"id":12555,"type":14,"createdAt":12556,"updatedAt":12557,"environment":30396,"publishedVersion":12560,"revision":12561,"contentType":30398,"locale":673},{"sys":30395},{"type":657,"linkType":658,"id":659},{"sys":30397},{"id":665,"type":657,"linkType":666},{"sys":30399},{"type":657,"linkType":671,"id":922},{"entryTitle":942,"title":942,"url":12565,"image":30401},{"metadata":30402,"sys":30405,"fields":30410},{"tags":30403,"concepts":30404},[],[],{"space":30406,"id":12573,"type":934,"createdAt":12574,"updatedAt":12575,"environment":30408,"publishedVersion":12578,"revision":6276,"locale":673},{"sys":30407},{"type":657,"linkType":658,"id":659},{"sys":30409},{"id":665,"type":657,"linkType":666},{"title":677,"description":677,"file":30411},{"url":12581,"details":30412,"fileName":12586,"contentType":949},{"size":12583,"image":30413},{"width":12585,"height":12585},{"metadata":30415,"sys":30418,"fields":30425},{"tags":30416,"concepts":30417},[],[],{"space":30419,"id":12594,"type":14,"createdAt":12595,"updatedAt":12596,"environment":30421,"publishedVersion":12599,"revision":1727,"contentType":30423,"locale":673},{"sys":30420},{"type":657,"linkType":658,"id":659},{"sys":30422},{"id":665,"type":657,"linkType":666},{"sys":30424},{"type":657,"linkType":671,"id":922},{"entryTitle":697,"title":697,"image":30426},{"metadata":30427,"sys":30430,"fields":30435},{"tags":30428,"concepts":30429},[],[],{"space":30431,"id":12610,"type":934,"createdAt":12611,"updatedAt":12612,"environment":30433,"publishedVersion":12615,"revision":12616,"locale":673},{"sys":30432},{"type":657,"linkType":658,"id":659},{"sys":30434},{"id":665,"type":657,"linkType":666},{"title":697,"description":697,"file":30436},{"url":12619,"details":30437,"fileName":12623,"contentType":949},{"size":12621,"image":30438},{"width":12585,"height":12585},{"metadata":30440,"sys":30443,"fields":30450},{"tags":30441,"concepts":30442},[],[],{"space":30444,"id":12631,"type":14,"createdAt":12632,"updatedAt":12633,"environment":30446,"publishedVersion":12615,"revision":12616,"contentType":30448,"locale":673},{"sys":30445},{"type":657,"linkType":658,"id":659},{"sys":30447},{"id":665,"type":657,"linkType":666},{"sys":30449},{"type":657,"linkType":671,"id":922},{"entryTitle":12639,"title":12639,"url":12640,"image":30451},{"metadata":30452,"sys":30455,"fields":30460},{"tags":30453,"concepts":30454},[],[],{"space":30456,"id":12648,"type":934,"createdAt":12649,"updatedAt":12650,"environment":30458,"publishedVersion":7383,"revision":12616,"locale":673},{"sys":30457},{"type":657,"linkType":658,"id":659},{"sys":30459},{"id":665,"type":657,"linkType":666},{"title":12639,"description":12639,"file":30461},{"url":12655,"details":30462,"fileName":12660,"contentType":949},{"size":12657,"image":30463},{"width":12585,"height":12659},{"metadata":30465,"sys":30468,"fields":30475},{"tags":30466,"concepts":30467},[],[],{"space":30469,"id":12668,"type":14,"createdAt":12669,"updatedAt":12670,"environment":30471,"publishedVersion":12615,"revision":12616,"contentType":30473,"locale":673},{"sys":30470},{"type":657,"linkType":658,"id":659},{"sys":30472},{"id":665,"type":657,"linkType":666},{"sys":30474},{"type":657,"linkType":671,"id":922},{"entryTitle":797,"title":797,"url":777,"image":30476},{"metadata":30477,"sys":30480,"fields":30485},{"tags":30478,"concepts":30479},[],[],{"space":30481,"id":12683,"type":934,"createdAt":12684,"updatedAt":12685,"environment":30483,"publishedVersion":1727,"revision":12616,"locale":673},{"sys":30482},{"type":657,"linkType":658,"id":659},{"sys":30484},{"id":665,"type":657,"linkType":666},{"title":797,"description":797,"file":30486},{"url":12690,"details":30487,"fileName":12694,"contentType":949},{"size":12692,"image":30488},{"width":12585,"height":12585},{"metadata":30490,"sys":30493,"fields":30500},{"tags":30491,"concepts":30492},[],[],{"space":30494,"id":12702,"type":14,"createdAt":12703,"updatedAt":12704,"environment":30496,"publishedVersion":1727,"revision":12707,"contentType":30498,"locale":673},{"sys":30495},{"type":657,"linkType":658,"id":659},{"sys":30497},{"id":665,"type":657,"linkType":666},{"sys":30499},{"type":657,"linkType":671,"id":922},{"entryTitle":12711,"title":12712,"image":30501},{"metadata":30502,"sys":30505,"fields":30510},{"tags":30503,"concepts":30504},[],[],{"space":30506,"id":12720,"type":934,"createdAt":12721,"updatedAt":12722,"environment":30508,"publishedVersion":12725,"revision":12726,"locale":673},{"sys":30507},{"type":657,"linkType":658,"id":659},{"sys":30509},{"id":665,"type":657,"linkType":666},{"title":657,"description":657,"file":30511},{"url":12729,"details":30512,"fileName":12733,"contentType":949},{"size":12731,"image":30513},{"width":12585,"height":12585},{"metadata":30515,"sys":30518,"fields":30527},{"tags":30516,"concepts":30517},[],[],{"space":30519,"id":30521,"type":14,"createdAt":30522,"updatedAt":30522,"environment":30523,"publishedVersion":847,"revision":1136,"contentType":30525,"locale":673},{"sys":30520},{"type":657,"linkType":658,"id":659},"1PL7SOHTLv8qVO7aSj1NP4","2026-01-08T12:32:14.559Z",{"sys":30524},{"id":665,"type":657,"linkType":666},{"sys":30526},{"type":657,"linkType":671,"id":1437},{"title":30098,"description":30528,"image":30529,"autoGenerateSocialImage":19,"noindex":19},"Don't let the flash crunch stall your AI roadmap. Discover how VAST Data and Solidigm are redefining storage economics to make every TB go further.",{"metadata":30530,"sys":30533,"fields":30540},{"tags":30531,"concepts":30532},[],[],{"space":30534,"id":30536,"type":934,"createdAt":30537,"updatedAt":30537,"environment":30538,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30535},{"type":657,"linkType":658,"id":659},"67tad290kMuwifs5wilA44","2026-01-08T12:32:10.520Z",{"sys":30539},{"id":665,"type":657,"linkType":666},{"title":30098,"description":30098,"file":30541},{"url":30542,"details":30543,"fileName":30546,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F67tad290kMuwifs5wilA44\u002F3a33595ff4c0b74bacc831542c235105\u002Ffbeating-the-flash-crunch-with-efficiency-not-supply.jpg",{"size":30544,"image":30545},66831,{"width":1457,"height":1458},"fbeating-the-flash-crunch-with-efficiency-not-supply.jpg",{"metadata":30548,"sys":30553,"fields":30563},{"tags":30549,"concepts":30552},[30550],{"sys":30551},{"type":657,"linkType":1502,"id":12749},[],{"space":30554,"id":30556,"type":14,"createdAt":30557,"updatedAt":30557,"environment":30558,"publishedVersion":30560,"revision":1136,"contentType":30561,"locale":673},{"sys":30555},{"type":657,"linkType":658,"id":659},"3z4la6EIFE3zP3PaTD6ZYi","2025-09-18T12:52:08.523Z",{"sys":30559},{"id":665,"type":657,"linkType":666},335,{"sys":30562},{"type":657,"linkType":671,"id":12765},{"title":30564,"slug":30565,"publishedDate":30566,"author":30567,"thumbnailImage":30568,"markdownContent":30600,"socialIcons":30601,"hidden":19,"seo":30727},"The New Storage Economics: Solidigm and VAST Deliver All-SSD Capacity at Half the Cost of HDD","new-storage-economics-solidigm-vast-deliver-all-ssd-capacity","2025-09-18","Brian Verkley, Product Marketing Manager",{"metadata":30569,"sys":30572,"fields":30581},{"tags":30570,"concepts":30571},[],[],{"space":30573,"id":30575,"type":14,"createdAt":30576,"updatedAt":30576,"environment":30577,"publishedVersion":901,"revision":1136,"contentType":30579,"locale":673},{"sys":30574},{"type":657,"linkType":658,"id":659},"6YIf6pfUcPfL6HcofY8bD","2025-09-15T14:27:00.539Z",{"sys":30578},{"id":665,"type":657,"linkType":666},{"sys":30580},{"type":657,"linkType":671,"id":12173},{"entryTitle":30564,"image":30582},{"metadata":30583,"sys":30586,"fields":30593},{"tags":30584,"concepts":30585},[],[],{"space":30587,"id":30589,"type":934,"createdAt":30590,"updatedAt":30590,"environment":30591,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30588},{"type":657,"linkType":658,"id":659},"1nJgZvqCKZvqs3NqeNyEhi","2025-09-15T14:26:51.858Z",{"sys":30592},{"id":665,"type":657,"linkType":666},{"title":30564,"description":30564,"file":30594},{"url":30595,"details":30596,"fileName":30599,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1nJgZvqCKZvqs3NqeNyEhi\u002F5115d442b711aec3bb49149687988423\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp",{"size":30597,"image":30598},44864,{"width":1144,"height":1145},"new-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp","For years, enterprise IT has played a strategic, often frustrating, game of resource allocation. Flash storage devices, with their undeniable speed, were the prized assets - reserved for the \"hot tier,\" the critical applications, the workloads requiring the lowest latencies and the highest throughput. The rest of the \"warm\" or \"cold\" data was relegated to slower, more economical HDD tiers. This was the accepted compromise, a necessary balancing act between performance and budget.\n\nHowever, when analyzing the Total Cost of Ownership (TCO), that equation has been inverted. Flash now provides a superior economic advantage. A \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html?fbclid=IwY2xjawMCs9lleHRuA2FlbQIxMQBicmlkETBrdkJHVmZROXhWcWFVa3RWAR7Cdu2fEX4BwDCDDhbs7LoO83F0iz2-nlVbvkztCC58CiA3aPgGAKVOFDrHGw_aem_9gUqa28yTbJw3i2IvSirKQ\">whitepaper by Solidigm and VAST\u003C\u002Fa> quantifies this, revealing that flash offers a __58.9% lower TCO__ than traditional HDD tiers.\n\nRecent innovations from Solidigm and VAST are changing storage economics and eliminating the tiered storage paradigm completely.  It is not cheaper to store cold data on flash. The cheapest medium for data is flash. Gone is the need for tiers, and with them the auto-tiering policies, the need for separate storage cluster designs, and the constant moving of data.\n\n#### The Technology Driving the Economic Shift\n\nCurrent hardware and software architectures have fundamentally altered storage economics, making a universal all-flash infrastructure more cost-effective than traditional hard drive systems. This shift is driven by two key innovations working in tandem.\n\n- __Hardware Breakthroughs in Density:__ The primary hardware change is the introduction of \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Fdata-center\u002Fd5\u002Fp5336.html#form=U.2%2015mm&cap=122.88TB\">Solidigm's 122-layer QLC NAND flash\u003C\u002Fa>, which provides unprecedented storage density. This allows for a massive reduction in the physical footprint of storage systems.\n- __Compounding Software Efficiencies:__ These hardware gains are amplified by VAST’s innovative software.\n  - VAST’s advanced \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper#a-breakthrough-approach-to-data-reduction\">Similarity data reduction\u003C\u002Fa> technique goes far beyond standard compression to find and eliminate redundancy at a granular level, meaning you need nearly 3x less raw storage capacity compared to HDD solutions. This level of granular data reduction can only be practical on flash because spinning media depends on sequential data for speed. When duplicate data is removed, many sequential reads look like random IO, which flash drives excel at, but HDDs struggle with.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes\">Locally decodable codes\u003C\u002Fa> provide an extremely efficient and resilient form of data protection, further enhancing cost-effectiveness by reducing wasted overhead.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper\u002F#VASTForesight\">VAST Foresight\u003C\u002Fa> is an intelligent data placement feature that groups data based on its predicted retention time before writing it to QLC flash, a strategy that improves the endurance and extends the lifespan of the storage system.\n  - Crucially, this entire solution is built for longevity. By offering 10-year support on QLC SSDs, the VAST platform eliminates the costly and disruptive hardware refresh cycles required by HDD systems every few years.\n\nTogether, these technologies mean a VAST system can store significantly more data on less physical hardware. An Exabyte of data that once required 52 racks of HDDs can now fit into just 5 racks, cutting data center space costs by over 90% and power and cooling costs by more than 75% leading to a total cost of ownership (TCO) that is over 50% lower than traditional HDD-based systems.\n\n#### The Proof is in the Numbers: A TCO Analysis\n\nAll of this is now documented in a \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html\">10-year Total Cost of Ownership (TCO) analysis\u003C\u002Fa>. While this analysis focuses on a 1EB deployment, the technical and economic advantages delivered by the platform begin at the smallest VAST cluster and extend to Zettabyte (ZB) scale. These savings come from several compounding factors:\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Eliminating Refresh Cycles:\u003C\u002Fspan> Traditional HDD infrastructures require costly and disruptive hardware refreshes every 3-5 years. The VAST Data platform offers 10-year support on QLC SSDs, avoiding multiple hardware repurchases and the associated labor costs. While the initial outlay for the SSD\u002FVAST solution might be higher, the total 10-year hardware cost is nearly 30% lower because those expensive HDD refresh cycles are eliminated.\u003C\u002Fp>\n\n![Total 10-year Hardware Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6IYwEHSOKe6RK1ThlhdQwD\u002F9713a93eb27ff55820b87e92a334a0af\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-1.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Effective Data Reduction:\u003C\u002Fspan> VAST’s Similarity data reduction technology achieves remarkable data reduction rates without the performance penalties that cripple deduplication solutions on HDDs. Because HDDs depend on sequential I\u002FO, HDD based tiers often use little to no effective data reduction (modeled at 1:1). The result: you need only one-third of the raw physical storage capacity with VAST compared to an HDD solution to achieve the same usable capacity.\u003C\u002Fp>\n\n![Calculated Raw Capacity Required (EB)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3E6tKrtcliZ4T9MdGRdogh\u002F8fe6bbe268068d2fc2bcfc16aa0f2ef3\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Higher Density, LowerFootprint:\u003C\u002Fspan> The combination of Solidigm's ultra-dense 122TB SSDs and VAST's software efficiencies means a dramatically smaller physical presence. What might require 52 racks for an Exabyte of usable HDD storage can be consolidated into just five racks with the Solidigm + VAST solution. This translates into over 90% savings in data center space costs and a power and cooling cost reduction of over 75%. Together, the lower power and space requirements support more sustainable data center operations.\u003C\u002Fp>\n\n![Racks Needed](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1VE9KfFfGqxbQ8rkxyUbl3\u002Fb7968b4faaa541547d72bc192d111ae8\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-3.webp)\n\n![Total 10-Year Power Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4GG8JLYtJ9YcwCrQ58kfSI\u002Fecd0faceed24172dfb3b466f7e1e534a\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-4.webp)\n\nThis combination of extended hardware life, superior data reduction, and higher density fundamentally rewires the economics of enterprise storage.\n\nWhen flash is no longer a scarce, premium resource, the whole equation of enterprise computing changes. IT leaders no longer have to ration performance or make uncomfortable compromises between speed and cost. Instead, they can design around possibilities:\n\n- __AI at Full Fidelity:__ With flash now the most economical tier, enterprises can finally train and analyze on their entire data history, not just today’s “hot” subset. Aquatic Capital Management, for example, uses VAST to unify decades-worth of structured and unstructured market, documentation, and proprietary data into a single high-performance flash infrastructure—giving researchers both full corpus fidelity and extended historical breadth. As their Head of Infrastructure notes: “the more different multi-variant data sources that they can assemble, the more accurate their models can become.” The result? AI models that are smarter, more contextually rich, and more accurate—made possible by collapsing the old trade-offs of cost, speed, and retention.\n- __Analytics Without Delay:__ The NHL rebuilt its media platform on VAST, shifting a massive video archive onto flash and expanding into real-time game-footage operations so editors and teams can cut highlights and move content while games are still underway. With replication across all 32 arenas, the league shortened the path from capture to distribution and set itself up for computer-vision and AI discovery on the same library.   A flash-backed foundation turned archival footage into something you can actually work with in the moment, rather than overnight.  \n- __Operational Agility:__ At spinTwo, VAST transformed months-long life sciences compute workloads into just weeks, freeing researchers to focus on discovery instead of infrastructure headaches. As their team notes: “With VAST Data, we process large datasets much faster, allowing scientists to spend more time on research rather than waiting for data to process. What used to take years, can now be completed in weeks, and we’re working to reduce that to hours.”    This kind of performance lets developers and data scientists spin up full datasets on the fly, test new pipelines, and iterate rapidly, without entangling in storage approvals or juggling quotas.\n\n#### A New Era of Storage Economics\n\nTiering solved an old cost problem. This Solidigm + VAST study shows that a single all-flash tier wins on the numbers: 58.9% lower 10-year TCO, 5 racks instead of 52, power and cooling down by more than 75%, and space costs down by more than 90%. Density, Similarity data reduction, locally decodable protection, and 10-year media support combine into a simpler system that stores more data on less hardware.\n\nTo see the model and assumptions, read the Solidigm + VAST TCO analysis. To get a custom TCO analysis for your business, schedule time with a \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fpersonalized-demo\">VAST Sales Engineer\u003C\u002Fa>.\n\n\u003Cstyle>\n  p::marker {color: var(--color-vastBlueDarker) 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and VAST Deliver All-SSD Capacity at Half the Cost of HDD","Discover how VAST and Solidigm’s all-flash infrastructure cuts 10-year TCO by 58.9%, shrinking racks, reducing power, and simplifying enterprise 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VAST Amplify unlocks stranded capacity, delivering up to 6× effective storage without new hardware.",{"metadata":30777,"sys":30780,"fields":30787},{"tags":30778,"concepts":30779},[],[],{"space":30781,"id":30783,"type":934,"createdAt":30784,"updatedAt":30784,"environment":30785,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30782},{"type":657,"linkType":658,"id":659},"4XLHV5r7dFp6KAoyCHmtFd","2026-01-25T13:46:06.063Z",{"sys":30786},{"id":665,"type":657,"linkType":666},{"title":28728,"description":28728,"file":30788},{"url":30789,"details":30790,"fileName":30793,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4XLHV5r7dFp6KAoyCHmtFd\u002F45b7a4fd8d01c6f74b6aa00166045894\u002Fvast-amplify-turning-capacity-scarcity-into-strategic-advantage.jpg",{"size":30791,"image":30792},84617,{"width":1457,"height":1458},"vast-amplify-turning-capacity-scarcity-into-strategic-advantage.jpg",{"metadata":30795,"sys":30798,"fields":30808},{"tags":30796,"concepts":30797},[],[],{"space":30799,"id":30801,"type":14,"createdAt":30802,"updatedAt":30803,"environment":30804,"publishedVersion":1117,"revision":712,"contentType":30806,"locale":673},{"sys":30800},{"type":657,"linkType":658,"id":659},"5dvmnu2YnegJUVHMPsKzN2","2023-03-09T05:04:19.532Z","2026-02-19T09:26:03.326Z",{"sys":30805},{"id":665,"type":657,"linkType":666},{"sys":30807},{"type":657,"linkType":671,"id":1437},{"entryTitle":17420,"title":30809,"description":30810,"image":30811,"noindex":19},"NVIDIA GPU Direct Storage: The VAST Data Story","NVIDIA's GPUDirect Storage provides a direct path between storage and GPU memory. VAST's NFS over RDMA combined with GPUDirect speeds up computation with GPUs...",{"metadata":30812,"sys":30815,"fields":30822},{"tags":30813,"concepts":30814},[],[],{"space":30816,"id":30818,"type":934,"createdAt":30819,"updatedAt":30819,"environment":30820,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30817},{"type":657,"linkType":658,"id":659},"3wAnAtvhLdXO6G2flS8ZpV","2026-02-19T09:25:31.449Z",{"sys":30821},{"id":665,"type":657,"linkType":666},{"title":30809,"description":30809,"file":30823},{"url":30824,"details":30825,"fileName":30828,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3wAnAtvhLdXO6G2flS8ZpV\u002F900452033db150124741b9c78e61ff62\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story.jpg",{"size":30826,"image":30827},56236,{"width":1457,"height":1458},"nvidia-gpu-direct-for-storage-gds-the-vast-data-story.jpg",{"metadata":30830,"sys":30841,"fields":30852},{"tags":30831,"concepts":30840},[30832,30834,30836,30838],{"sys":30833},{"type":657,"linkType":1502,"id":17400},{"sys":30835},{"type":657,"linkType":1502,"id":13704},{"sys":30837},{"type":657,"linkType":1502,"id":1081},{"sys":30839},{"type":657,"linkType":1502,"id":12752},[],{"space":30842,"id":30844,"type":14,"createdAt":30845,"updatedAt":30846,"environment":30847,"publishedVersion":30849,"revision":939,"contentType":30850,"locale":673},{"sys":30843},{"type":657,"linkType":658,"id":659},"6cOqQ5EkislLAUbfmWY7WB","2025-07-01T17:33:29.807Z","2025-08-21T13:58:33.494Z",{"sys":30848},{"id":665,"type":657,"linkType":666},429,{"sys":30851},{"type":657,"linkType":671,"id":12765},{"title":30853,"slug":30854,"publishedDate":30855,"author":30856,"thumbnailImage":30857,"markdownContent":30889,"socialIcons":30890,"hidden":19,"seo":31016},"Accelerating Inference","accelerating-inference","2025-07-01","Dan Aloni, Senior Software Engineer | Matthew Rogers, Field CTO AI and Security | Alon Horev, Co-Founder and CTO | Subramanian Kartik, PhD, Global Systems Engineering Lead |  Dave Graham, Technical Marketing Manager",{"metadata":30858,"sys":30861,"fields":30870},{"tags":30859,"concepts":30860},[],[],{"space":30862,"id":30864,"type":14,"createdAt":30865,"updatedAt":30865,"environment":30866,"publishedVersion":901,"revision":1136,"contentType":30868,"locale":673},{"sys":30863},{"type":657,"linkType":658,"id":659},"48ZQjmjBn1KLlQt8zUmhi4","2025-06-30T20:07:29.204Z",{"sys":30867},{"id":665,"type":657,"linkType":666},{"sys":30869},{"type":657,"linkType":671,"id":12173},{"entryTitle":30853,"image":30871},{"metadata":30872,"sys":30875,"fields":30882},{"tags":30873,"concepts":30874},[],[],{"space":30876,"id":30878,"type":934,"createdAt":30879,"updatedAt":30879,"environment":30880,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30877},{"type":657,"linkType":658,"id":659},"5CwyvZEcHf9nCfVoxBwM3M","2025-06-30T20:07:18.389Z",{"sys":30881},{"id":665,"type":657,"linkType":666},{"title":30853,"description":30853,"file":30883},{"url":30884,"details":30885,"fileName":30888,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5CwyvZEcHf9nCfVoxBwM3M\u002Ff71368207b83770140a23a159344584a\u002Faccelerating-inference.webp",{"size":30886,"image":30887},155940,{"width":1144,"height":1145},"accelerating-inference.webp","#### The Inference Evolution\nOver the past year, we’ve observed substantial growth in inference workloads and innovation in inference frameworks. As GPUs and models evolve, they can accomplish more complicated tasks. One of the enablers for this evolution is the context window size growth.\n\nThe context window enables applications to pack more information into prompts or maintain extended conversations without summarizing or losing historical data. More importantly, reasoning models can achieve better results by iteratively gathering more information from external sources in their journey to generate an answer.\n\nThe landscape of inference has transformed dramatically to support this growth, evolving from simple model serving to schedulers routing requests and tracking sessions across many environments.\n\nA significant amount of innovation occurred in how inference is scheduled and executed, with techniques like [Continuous Batching](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fosdi22\u002Fpresentation\u002Fyu), [Chunked Prefill](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.16369), and [Disaggregated Prefill and Decode](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.09670) emerging to maximize hardware utilization. One key opportunity for optimization is avoiding redundant computation altogether; this is where Key-Value (KV) Caching plays a crucial role.\n\n#### What is KV Cache? \nUnderstanding [KV cache](https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fopen-and-undivided-attention) requires an understanding of how the two phases function:\n\n__Prefill phase:__\n\nWhen you input a prompt, the model calculates attention values for each token. This is a computationally intensive process that enables the model to determine which tokens should receive the most attention at every step of generating a response. The prefill stage concludes with this [attention data](https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fintroducing-undivided-attention) stored in memory, known as KV cache (each token has key and value states).\n\n__Decode phase:__ \n\nThe decode phase commences once the prefill is complete and the KV cache is populated with the prompt’s context. During this phase, the model generates output, one token at a time, utilizing the cached context. Additionally, the cache size expands quadratically with the attention information for the generated tokens.\n\n#### Can KV Cache be optimized?\nReusing a cached state is crucial, especially with long context models and use cases. An example of this can be seen in the pricing of the [OpenAI O3-Pro model](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fmodels\u002Fo3-pro), where costs range from $20 per million tokens for input to $80 per million tokens for output. For applications like RAG or multi-turn chat, which require large contexts (documents, system prompts, chat history) to be repeatedly sent as input, a persistent KV cache prevents re-processing and eliminates the need to pay for computing these same input tokens on every single turn. The cost is incurred only once to generate the initial cache, which can be retrieved from the [VAST AI OS](https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fai-os) for subsequent requests, resulting in substantial savings.\n\nTime to First Token (TTFT) is primarily influenced by the compute-intensive prefill phase. By completely bypassing this computation and loading a pre-computed KV cache, the model can begin generating a response almost instantaneously. As a result, this improves the user experience from a noticeable initial lag to one that feels immediate and highly responsive.\n\n#### How Does KV Cache... Get Cached?\nAn active session or conversation is sitting in GPU memory in expectation of follow-up questions from a user, agent or a reasoning model that iterates towards an objective.\n\nAfter it becomes inactive for a while or when other prompts need GPU memory for KV cache, it can be offloaded to CPU memory, local storage or remote storage. An inference engine or framework needs to decide when to offload and reload each KV cache.\n\nThere are several great solutions out there that are worth mentioning:\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> vLLM is a popular inference engine that efficiently manages KV cache in GPU memory. It also supports extensions via plugins for offloading the KV cache to other locations.\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> LMCache works very closely with vLLM and enables offloading KV cache:\n\u003C\u002Fp>\n\u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">a. \u003C\u002Fb>  CPU memory is the first location where the KV cache is destaged as it’s typically larger than GPU memory. It’s faster to store\u002Fload the KV cache to CPU memory as it stays local to the machine.\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">b. \u003C\u002Fb> The next location is remote storage, which enables unbound capacity.\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fb> \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fsharedeverything\u002Fwhy-everyones-talking-about-nvidia-dynamo-and-why-it-actually-matters\">NVIDIA Dynamo\u003C\u002Fa> is a low-latency inference serving framework for large-scale distributed environments It is compatible with all popular inference engines like SGLang, TensorRT-LLM, and vLLM.\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">a. \u003C\u002Fb> It enables transferring KV cache across GPUs, CPUs and storage using an accelerated data movement library called NVIDIA NIXL (NVIDIA Inference Xfer Library).\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">b. \u003C\u002Fb> The distributed architecture behind Dynamo naturally supports implementing disaggregated prefill and decode. This serves as another strategy for enhancing scheduling across accelerated computings to boost inference throughput and minimize latency. It works by assigning a set of GPUs to run prefill and having NIXL move the data using RDMA to a different set of GPUs that will perform the decode process, as seen in the diagram below.\u003C\u002Fp>\n\n\u003Cp align=\"center\" style=\"margin: 40px 0\">\u003Cimg src=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3kMn3tnx9sC7yb17LUHQxe\u002F7e9d64f1c259cfba03d58fa49cb043f2\u002Faccelerating-inference-1.webp\" alt=\"Disaggregated Prefill\" style=\"margin-bottom: 0\" \u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\" style=\"margin: 40px 0\">\u003Cimg src=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2WLTc0HQhtjn2avMc9SFUc\u002Ff57aeaca9d2bffbeff8133eddfb6c355\u002Faccelerating-inference-2.webp\" alt=\"Control & Execution Tiers\" style=\"margin-bottom: 0\" \u002F>\u003C\u002Fp>\n\n#### Does it work?\nTo understand the implications of these solutions, we chose to test the impact of the KV cache in an environment that our customers use daily. We executed a multi-stage process to quantify the performance benefits of offloading LLM KV caches to the VAST AI OS. The methodology was designed to assess the entire stack, from the foundational I\u002FO capabilities of the infrastructure to the application-level impact on inference latency within an LLM serving framework.\n\nFor validation purposes, we would like to test two layers independently:\n\n  \u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> I\u002FO layer - using \u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fgpudirect?ncid=no-ncid\">NVIDIA Magnum IOTM GPUDirect® Storage\u003C\u002Fa> (GDS) to read\u002Fwrite to remote storage while bypassing CPU memory to reduce CPU and bandwidth utilization of the host RAM.\u003C\u002Fp>\n  \u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> Application layer - LMCache that would read\u002Fwrite KV caches from\u002Fto VAST.\u003C\u002Fp>\n\n__1.  I\u002FO and NVIDIA GPUDirect Storage (GDS)__\n\nThe initial phase focused on establishing a verified performance baseline for the underlying hardware and data path to ensure the environment was functional and configured for high-throughput, low-latency storage access.\n\n- __GDS Path Validation:__ We began by integrating and validating the NVIDIA NIXL GDS plugin with the VAST AI OS, utilizing the nixlbench benchmark tool configuration highlighted below. This step was critical to confirm the correct functioning of the GDS data path and to measure the raw, unimpeded I\u002FO performance between the NVIDIA H100 GPU memory and the VAST AI OS.\n\n` CUDA_VISIBLE_DEVICES=0 nixlbench \\ -backend=GDS \\ -mode=SG \\ -worker_type=nixl \\ -op_type=READ \\ -gds_filepath=\u002Fmnt\u002Fvast\u002F1 \\ -num_threads=32 \\ -total_buffer_size=$((32*1024*1024*1024)) \\ -start_block_size=$((1024*1024)) \\ -max_block_size=$((1024*1024)) \\ -target_seg_type=VRAM \\ -initiator_seg_type=VRAM \\ -storage_enable_direct=1 `\n\n- __Single-GPU Saturation Test:__ A primary objective was to demonstrate that the storage platform could deliver data at a rate sufficient to saturate a single GPU, with and without GDS. Our tests successfully achieved GPU saturation, driving a single H100 GPU at 35GB\u002Fs using GDS without saturating the VAST AI OS’s available throughput. This confirmed that storage would not be a bottleneck for the subsequent stages, and the GPU's processing capacity would dictate that performance.\n\n\u003Cp align=\"center\" style=\"margin: 40px 0\">\u003Cimg src=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F39zHxv3gt9avq87MXBU7rK\u002F91d6f886fb734b85ca1b57c2f8ed6085\u002Faccelerating-inference-3.webp\" alt=\"Accelerating Inference\" style=\"margin-bottom: 0\" \u002F>\u003C\u002Fp>\n\n__2. Application-Level Integration and Comparative Testing__\nWith a high-performance I\u002FO baseline established, we transitioned from synthetic benchmarks to a real-world LLM inference environment.\n\n\u003Cp style=\"text-indent: 0; margin-left: 30px; display: list-item;\">\u003Cb>Framework Integration:\u003C\u002Fb>  We utilized a build of the vLLM inference engine, which incorporated the LMCache \u003Cspan style=\"font-family: monospace; font-size: 16px;\">kv_connector\u003C\u002Fspan>. This connector enables vLLM to persist and retrieve KV caches from an external namespace, in this case, a file-based repository on the VAST AI OS.\u003C\u002Fp>\n\n\u003Cp style=\"text-indent: 0; margin-left: 30px; display: list-item;\">\u003Cb>Prefill Time Analysis:\u003C\u002Fb> The core of our analysis was a comparative measurement of prefill duration. We benchmarked inference requests for a range of input token sequence lengths under two distinct conditions:\n\n \u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> \u003Cb>Compute-Bound Prefill (Baseline):\u003C\u002Fb> Generating the KV cache from scratch using GPU compute resources.\u003C\u002Fp>\n\n \u003Cp style=\"text-indent: 0; margin-left: 60px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> \u003Cb>Cache-Read Prefill (Optimized):\u003C\u002Fb> Bypassing the initial computation by loading a pre-computed KV cache directly from the VAST platform. This comparison directly measures the reduction in TTFT achieved by leveraging a persistent KV cache.\u003C\u002Fp>\n\n__TTFT for Qwen3-32B__\n\nTo understand the real-world implications of this performance, we tested to determine the TTFT for a prefill task on a currently popular LLM. The parameters for this are as follows:\n\n- __Model:__ [Qwen\u002FQwen3-32B](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3-32B)\n- __Context Size:__ 130,858 tokens  (max: 128,000)\n- __KV Cache Size:__ 32 GB (for a 130K token context)\n- __Conventional Prefill Rate:__ 500 Tokens Per Second (TPS)\n- __System Overhead:__ 1 second (for initialization and other processes)\n\n__KV Cache Loading Calculation:__\n\nUsing vLLM v0.9.0-170 and LMCache v0.3.0-1, the pre-computed 32 GB KV cache can be loaded directly into the GPU.\n\n\u003Cp align=\"center\" style=\"margin: 40px 0\">\u003Cimg src=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5nmXDvbRV4cGwVFF8BqryG\u002Fc98bd19d6b88b3aeebf72b04a9daaccd\u002Faccelerating-inference-4.webp\" alt=\"Accelerating Inference\" style=\"margin-bottom: 0\" \u002F>\u003C\u002Fp>\n\nThe results of our testing indicate a dramatic reduction in the time required for the initial prefill stage. By directly loading the KV cache, vLLM+LMCache can reduce the token read time (TTFT) from over 11 seconds to just 1.5 seconds at the maximum context window size (128,000).\n\nThe time spent generating the KV cache vs. reading it from storage represents a __10x improvement__ in *request_prefill_time* performance for large context scenarios!\n\nThis environment adheres to NVIDIA DGX [SuperPOD™](https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fnvidia-dgx-superpod-vast) and [NVIDIA Cloud Partner (NCP)](https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fhigh-performance-storage-reference-architecture-for-nvidia-cloud-partners) reference architectures. The storage traffic traverses the north-south fabric, and VAST AI OS routes all storage traffic over a high-speed 400 Gbps connection with NVIDIA BlueField-3 DPUs.\n\nWhile the platform supports NFSoRDMA with GPUDirect Storage (GDS) for optimized data paths, these features are optional. Our architecture is engineered to drive [full line-rate performance using a standard NFSoTCP](https:\u002F\u002Fdocs.lmcache.ai\u002Fkv_cache\u002Fgds.html) implementation, providing maximum deployment flexibility without compromising throughput.\n\n#### The Way Forward\n\u003Cp>The landscape of LLM infrastructure is rapidly evolving towards open, standardized interfaces that decouple storage and compute, a critical step for enabling, high-performance AI factories. NVIDIA \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fdynamo\">Dynamo\u003C\u002Fa> and the underlying \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fnixl\">NIXL library are helping drive this transformation\u003C\u002Fa>. Dynamo enables disaggregated serving, separating prefill and decode on district GPUs. NIXL creates a standardized API layer for low-latency data transfer , effectively abstracting the complexities of data movement between GPU memory and various external storage tiers,. It offers a GPU-native library for managing and accessing KV cache movement between data caching tiers, including external storage, which is what we validated with \u003Cspan style=\"font-family: monospace; font-size: 16px;\">nixlbench\u003C\u002Fspan> in our testing.\u003C\u002Fp>\n\n\u003Cp>Projects like \u003Ca href=\"https:\u002F\u002Fllm-d.ai\u002Fdocs\u002Farchitecture\u002Farchitecture\">LMCache, vLLM Production-Stack\u003C\u002Fa>, and the broader \u003Ca href=\"https:\u002F\u002Fllm-d.ai\u002Fdocs\u002Farchitecture\u002Farchitecture\">LLM-d initiative\u003C\u002Fa>, which advocate for disaggregated LLM components, fit perfectly within this paradigm. Using the NIXL GDS plugin and an LMCache-enabled version of vLLM, our methodology directly engages with this next-generation stack. It demonstrates how a high-performance, disaggregated, shared-everything data platform can serve as the persistent tier for GPU memory extension, leveraging the open, standardized I\u002FO paths being championed by NVIDIA and the wider community to deliver scalable, stateful, and cost-effective LLM inference.\u003C\u002Fp>\n\nConvergence is continually occurring in the broader model-supporting frameworks space, and we’re ensuring that at every step along the way, the VAST AI OS is available as a fundamental starting point for development, iteration, testing, and production.\n\n\u003Ch4>Partnerships Make Possibilities\u003C\u002Fh4>\n  \u003Cp>We want to thank our partners at \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Fscaleway\">Scaleway\u003C\u002Fa>, a \u003Ca href=\"https:\u002F\u002Fwww.scaleway.com\u002F\">leading AI cloud service provider\u003C\u002Fa> in France, for their invaluable assistance with our recent KV cache testing initiatives. Their team provided a truly cutting-edge environment that was instrumental in helping us validate and push the boundaries of our research. It's a privilege to collaborate with partners who are as committed to high-performance infrastructure as we are.\n\nThe modern technology stack provided by Scaleway represents a powerful blueprint for AI infrastructure, demonstrating how best-of-breed components can be integrated for maximum performance. This setup, built on the \u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fnetworking\u002Fspectrumx\u002F\">NVIDIA Spectrum™-X networking platform\u003C\u002Fa>, takes advantage of the capabilities of NVIDIA Spectrum-4 switches and NVIDIA BlueField-3 DPUs and RoCE (RDMA over Converged Ethernet) to enable advanced, low-latency protocols such as NFSoRDMA and GPUDirect Storage, all seamlessly integrated with the VAST AI OS.\n\n\u003Ch4>Join the Technical Discussion\u003C\u002Fh4>\n\nThe evolution of inference optimization is just beginning, and the challenges surrounding KV cache management, memory efficiency, and scheduling algorithms are transforming how we create AI infrastructure at scale.\n\n\u003Cp>If you're facing similar optimization challenges or want to delve into the technical aspects behind these benchmarks, we would love for you to join our engineering discussions in the \u003Ca href=\"https:\u002F\u002Fcommunity.vastdata.com\u002Fc\u002Fvast-news\u002F23\u002F\">Cosmos community\u003C\u002Fa>, the \u003Ca href=\"https:\u002F\u002Flmcacheworkspace.slack.com\u002Fjoin\u002Fshared_invite\u002Fzt-36x1m765z-8FgDA_73vcXtlZ_4XvpE6Q#\u002Fshared-invite\u002Femail\">LMCache Slack community\u003C\u002Fa>, Dynamo Discord Server, and contribute to our \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fvast-data\">open-source projects\u003C\u002Fa>. It's a space where we and other practitioners share insights on everything from storage architecture to AI workload optimization - the type of technical deep-dives that don't always make it into blog posts.\u003C\u002Fp>\n\n\u003Cstyle>\n  p::marker {color: var(--color-vastBlueDarker) !important;}\n  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how KV cache optimization slashes LLM inference latency, cuts costs, and boosts performance with vLLM, LMCache, and the VAST AI 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