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Image_ADAS_VAST-Data.jpg",{"data":23496,"content":23497,"nodeType":936},{},[23498,23505,23512,23520,23527,23534,23541,23548,23555,23562,23582,23589,23596,23603,23610,23617,23624,23643,23650,23657,23677,23684,23691,23721,23728,23735,23742,23749],{"data":23499,"content":23500,"nodeType":940},{},[23501],{"data":23502,"marks":23503,"value":23504,"nodeType":944},{},[],"The automotive industry is becoming a software industry. As advanced driver assistance systems (ADAS) evolve from basic lane-keeping and adaptive cruise control toward full autonomous decision-making, automakers and their technology partners are transforming into software-defined enterprises. The AI models powering this shift are getting dramatically more complex, and the data required to train, validate, and continuously improve them is growing at a pace that legacy infrastructure simply cannot sustain.",{"data":23506,"content":23507,"nodeType":940},{},[23508],{"data":23509,"marks":23510,"value":23511,"nodeType":944},{},[],"The companies winning this race aren't just building better algorithms. They're rethinking the data platform that supports their network of connected, autonomous vehicles, and the ecosystem supporting it.",{"data":23513,"content":23514,"nodeType":23519},{},[23515],{"data":23516,"marks":23517,"value":23518,"nodeType":944},{},[],"The Scale Problem Is Compounding","heading-4",{"data":23521,"content":23522,"nodeType":940},{},[23523],{"data":23524,"marks":23525,"value":23526,"nodeType":944},{},[],"A single sensor-equipped test vehicle generates terabytes of data per day across cameras, LiDAR, radar, and ultrasonics. Multiply that across a global fleet of data collection vehicles, then factor in synthetic data generation and simulation workloads, and you're looking at hundreds of petabytes under management.",{"data":23528,"content":23529,"nodeType":940},{},[23530],{"data":23531,"marks":23532,"value":23533,"nodeType":944},{},[],"But volume alone isn't what makes ADAS data infrastructure so challenging. It's the diversity of access patterns across the pipeline. Ingesting raw sensor data looks nothing like serving randomized video clips to a GPU cluster for training. Running validation workloads against safety-critical edge cases requires different performance characteristics than syncing refined model outputs back to vehicles through over-the-air updates. And increasingly, the platform also needs to handle real-time telematics from connected vehicles already on the road, streaming telemetry data that feeds both analytics dashboards and the next generation of model training.",{"data":23535,"content":23536,"nodeType":940},{},[23537],{"data":23538,"marks":23539,"value":23540,"nodeType":944},{},[],"Every stage of this pipeline has different throughput, latency, and governance requirements. Traditional architectures force teams to build and maintain separate systems for each one, creating deep data fragmentation across global development centers. When data is scattered across incompatible silos, the sheer scale of the datasets makes consolidation impractical. Data becomes too massive and too dispersed to move efficiently between sites or from on-prem to cloud. This fragmentation prevents teams from establishing a single source of truth and slows collaboration between developers and data scientists worldwide.",{"data":23542,"content":23543,"nodeType":940},{},[23544],{"data":23545,"marks":23546,"value":23547,"nodeType":944},{},[],"The result is a fragile patchwork of HPC file systems, self-managed object stores, and cloud staging environments held together by custom ETL pipelines. Teams end up spending more engineering cycles managing data movement than improving models.",{"data":23549,"content":23550,"nodeType":23519},{},[23551],{"data":23552,"marks":23553,"value":23554,"nodeType":944},{},[],"Legacy Infrastructure Breaks Down at ADAS Scale",{"data":23556,"content":23557,"nodeType":940},{},[23558],{"data":23559,"marks":23560,"value":23561,"nodeType":944},{},[],"The pattern is consistent across the industry. Companies that started with traditional HPC file systems like GPFS found them powerful for early workloads but operationally complex and brittle as data volumes grew into the hundreds of petabytes. These shared-nothing parallel file systems were designed for the sequential I\u002FO patterns of traditional HPC simulation, not the random I\u002FO demands of deep learning. The result is I\u002FO starvation, where expensive GPU clusters sit idle because the storage layer simply can't supply data fast enough to keep them saturated. Bolting on separate S3-compatible object storage introduced protocol silos. Scaling metadata performance to handle billions of small files (think individual labeled video frames) required over-provisioning capacity just to avoid bottlenecks.",{"data":23563,"content":23564,"nodeType":940},{},[23565,23569,23578],{"data":23566,"marks":23567,"value":23568,"nodeType":944},{},[],"One global autonomous vehicle software company, now running hundreds of ",{"data":23570,"content":23572,"nodeType":6165},{"uri":23571},"http:\u002F\u002Fwww.vastdata.com\u002Fnvidia",[23573],{"data":23574,"marks":23575,"value":874,"nodeType":944},{},[23576],{"type":23577},"underline",{"data":23579,"marks":23580,"value":23581,"nodeType":944},{},[]," H100-class GPUs and over 100,000 CPU cores across on-premises and cloud environments, hit exactly this wall. Their legacy GPFS and self-managed S3 infrastructure couldn't scale without multiplying operational complexity alongside it. Every expansion meant more systems to manage, more data copies to synchronize, and more failure modes to troubleshoot. GPU utilization suffered because the storage architecture couldn't keep pace with the compute investments the company was making.",{"data":23583,"content":23584,"nodeType":940},{},[23585],{"data":23586,"marks":23587,"value":23588,"nodeType":944},{},[],"A second company, a safety-focused ADAS software developer, faced a different but related challenge. Their team was making the leap from training perception models on still images to training on motion video, a shift that dramatically increased throughput demands and data volumes simultaneously. With hundreds of GPUs shared across a research team of hundreds of data scientists, their existing infrastructure couldn't independently scale performance to keep pace. Metadata constraints were causing outages. And legacy compression was delivering just 1.1:1 reduction on their JPEG and PNG training datasets, meaning raw storage costs scaled nearly linearly with data growth.",{"data":23590,"content":23591,"nodeType":23519},{},[23592],{"data":23593,"marks":23594,"value":23595,"nodeType":944},{},[],"What ADAS Pipelines Actually Require",{"data":23597,"content":23598,"nodeType":940},{},[23599],{"data":23600,"marks":23601,"value":23602,"nodeType":944},{},[],"When you strip away vendor positioning and look at what autonomous driving teams actually need from their data platform, the requirements converge:",{"data":23604,"content":23605,"nodeType":940},{},[23606],{"data":23607,"marks":23608,"value":23609,"nodeType":944},{},[],"Sustained, predictable throughput measured in tens of gigabytes per second to keep GPU clusters fully utilized during training, eliminating the I\u002FO starvation that plagues legacy architectures. Native streaming data ingest that can handle terabytes per day of sensor and telematics data in real time, with Kafka-compatible interfaces so the same platform that stores training data can also serve as the event backbone for live vehicle telemetry. Multi-protocol access spanning NFS and high-performance S3 so the same data can serve HPC workloads and cloud-native pipelines without copies or migration. The ability to scale performance, capacity, and metadata independently so teams aren't forced to over-provision one dimension just to get more of another. Data reduction that works on already-compressed formats like JPEG, PNG, and encoded video. A global namespace that extends across on-prem clusters and into public cloud environments for burst compute, solving data fragmentation by bringing compute to the data rather than the other way around. And cross-site data sharing that doesn't require replicating entire petabyte-scale datasets between locations.",{"data":23611,"content":23612,"nodeType":940},{},[23613],{"data":23614,"marks":23615,"value":23616,"nodeType":944},{},[],"No single legacy system checks all of these boxes. That's why leading ADAS companies are consolidating onto a unified data platform built for AI workloads from the ground up.",{"data":23618,"content":23619,"nodeType":23519},{},[23620],{"data":23621,"marks":23622,"value":23623,"nodeType":944},{},[],"How Leading AV Companies Are Solving It",{"data":23625,"content":23626,"nodeType":940},{},[23627,23631,23639],{"data":23628,"marks":23629,"value":23630,"nodeType":944},{},[],"The global AV software company consolidated their GPFS and self-managed S3 infrastructure onto the VAST Data AI Operating System and NVIDIA DGX SuperPOD for ADAS and LLM development. The results were immediate and measurable. A unified global namespace spanning NFS and high-performance S3 eliminated the protocol silos that had been driving data duplication. VAST's ",{"data":23632,"content":23634,"nodeType":6165},{"uri":23633},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fhow-it-works",[23635],{"data":23636,"marks":23637,"value":23638,"nodeType":944},{},[],"disaggregated, shared-everything (DASE) architecture",{"data":23640,"marks":23641,"value":23642,"nodeType":944},{},[],", where every CPU and GPU can access all data across all SSDs simultaneously, replaced the east-west traffic bottlenecks of their legacy shared-nothing systems with truly parallel data access. Data reduction delivered up to 12:1 efficiency on code repositories. And the platform's cloud bursting capabilities allowed the team to dynamically push compute workloads into public cloud environments and sync results back to on-prem, all through a single namespace.",{"data":23644,"content":23645,"nodeType":940},{},[23646],{"data":23647,"marks":23648,"value":23649,"nodeType":944},{},[],"Today, this company has more than 10 petabytes of effective capacity on VAST and is on track to double that within two years. Their real-time mapping systems, which detect and share road hazards between vehicles at centimeter-level precision, now run on infrastructure that can actually keep pace with the data volumes involved.",{"data":23651,"content":23652,"nodeType":940},{},[23653],{"data":23654,"marks":23655,"value":23656,"nodeType":944},{},[],"The safety-focused ADAS software developer saw equally significant gains. VAST sustains 50GB\u002Fs of throughput for their training workloads, keeping their GPU fleet utilized rather than starved for data. The platform's advanced similarity-based data reduction algorithms achieve 2.5:1 reduction on JPEG and PNG datasets, more than doubling the effective capacity compared to legacy systems limited to 1.1:1. The DASE architecture allowed them to add compute and storage nodes independently, and as needed, without disruption. This proved critical and allowed them to add compute and storage nodes independently of capacity licenses, which proved critical for scaling metadata performance and resolving the outage-causing constraints they experienced on previous infrastructure.",{"data":23658,"content":23659,"nodeType":940},{},[23660,23664,23673],{"data":23661,"marks":23662,"value":23663,"nodeType":944},{},[],"With multiple VAST clusters spanning separate data centers, the team now uses global snapshots through",{"data":23665,"content":23667,"nodeType":6165},{"uri":23666},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdataspace",[23668],{"data":23669,"marks":23670,"value":23672,"nodeType":944},{},[23671],{"type":23577}," VAST DataSpace",{"data":23674,"marks":23675,"value":23676,"nodeType":944},{},[]," to share complete datasets across locations without replication, directly addressing the data fragmentation that had previously limited cross-site collaboration. Researchers can access what they need from any site, scramble data for model variation, and collaborate across geographies without data bloat. For a team of hundreds of data scientists competing for GPU time in a shared scheduler, this kind of frictionless data access is a strategic advantage.",{"data":23678,"content":23679,"nodeType":23519},{},[23680],{"data":23681,"marks":23682,"value":23683,"nodeType":944},{},[],"From Training to Real-Time Inference",{"data":23685,"content":23686,"nodeType":940},{},[23687],{"data":23688,"marks":23689,"value":23690,"nodeType":944},{},[],"The ADAS conversation often centers on training, but training is only half the story. The models that power scene understanding, prediction, and path planning need to run in production, processing continuous streams of sensor data and making decisions in real time. The infrastructure that trains the model needs to also serve the inference pipeline, and the data flowing between vehicles, edge systems, and the data center needs to be ingested, enriched, and acted upon as a continuous stream, not as a batch job that runs overnight.",{"data":23692,"content":23693,"nodeType":940},{},[23694,23698,23706,23710,23718],{"data":23695,"marks":23696,"value":23697,"nodeType":944},{},[],"This is where VAST's architecture extends beyond what legacy storage platforms can offer. The ",{"data":23699,"content":23701,"nodeType":6165},{"uri":23700},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdatabase",[23702],{"data":23703,"marks":23704,"value":16679,"nodeType":944},{},[23705],{"type":23577},{"data":23707,"marks":23708,"value":23709,"nodeType":944},{},[]," functions as a real-time event broker with Kafka-compatible and Apache Arrow-native interfaces, capable of ingesting and querying streaming telematics and sensor data at scale. The same platform that stores petabytes of training data can simultaneously ingest live vehicle telemetry, run real-time analytics, and feed inference workloads ",{"data":23711,"content":23713,"nodeType":6165},{"uri":23712},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fannouncing-the-vast-event-broker",[23714],{"data":23715,"marks":23716,"value":23717,"nodeType":944},{},[],"without requiring a separate streaming infrastructure stack",{"data":23719,"marks":23720,"value":6169,"nodeType":944},{},[],{"data":23722,"content":23723,"nodeType":940},{},[23724],{"data":23725,"marks":23726,"value":23727,"nodeType":944},{},[],"For ADAS teams, this means the collect-to-train-to-validate loop doesn't end at model export. The trained perception model that identifies a construction zone, the planning model that reroutes around it, and the prediction model that anticipates the behavior of surrounding vehicles all depend on the same underlying data platform to deliver context at speed. OTA update validation, digital twin simulation, and real-time dashboards on fleet behavior are not future use cases that require a separate architecture. They run on the same VAST AI OS that powers training today.",{"data":23729,"content":23730,"nodeType":940},{},[23731],{"data":23732,"marks":23733,"value":23734,"nodeType":944},{},[],"This end-to-end capability is what separates a data platform, or an operating system, from a storage system. ADAS development doesn't stop at training a model that works in simulation. It requires infrastructure that can carry that model through validation, deployment, and continuous improvement in the real world, all on a single platform.",{"data":23736,"content":23737,"nodeType":23519},{},[23738],{"data":23739,"marks":23740,"value":23741,"nodeType":944},{},[],"The Bigger Picture",{"data":23743,"content":23744,"nodeType":940},{},[23745],{"data":23746,"marks":23747,"value":23748,"nodeType":944},{},[],"ADAS is the leading edge of a much larger shift. Beyond automotive, robotics companies, industrial automation platforms, and drone fleet operators are converging on the same fundamental challenge: building AI systems that perceive, reason about, and act in the physical world generates data at a scale and complexity that legacy infrastructure was never designed to handle. The same architectural requirements that ADAS demands today, massive streaming ingest, real-time inference, global data access, and continuous model improvement, are becoming table stakes for any organization building physical AI.",{"data":23750,"content":23751,"nodeType":940},{},[23752],{"data":23753,"marks":23754,"value":23755,"nodeType":944},{},[],"The companies that treat data infrastructure as a strategic investment rather than a cost center are the ones shipping safer, smarter systems faster. The autonomous driving industry is proving that the AI operating system underneath the models matters just as much as the models 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While GPU compute remains foundational, overall performance increasingly depends on how efficiently inference session history (context) can be stored, retrieved, reused, and shared under sustained load.",[],{},{"nodeType":940,"data":24387,"content":24388},{},[24389,24393,24401],{"nodeType":944,"value":24390,"marks":24391,"data":24392},"This shift is what NVIDIA is addressing today with their ",[],{},{"nodeType":6165,"data":24394,"content":24396},{"uri":24395},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fintroducing-nvidia-bluefield-4-powered-inference-context-memory-storage-platform-for-the-next-frontier-of-ai\u002F",[24397],{"nodeType":944,"value":24398,"marks":24399,"data":24400},"announcement of the Inference Context Memory Storage (ICMS) Platform",[],{},{"nodeType":944,"value":24402,"marks":24403,"data":24404}," for the NVIDIA Rubin platform. 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The NVIDIA Inference Context Memory Storage Platform takes a new ultra-high-performance approach to solving the pressing capacity issue by moving inference context out of GPU and CPU memory and into persistent RDMA-connected NVMe storage so context size is not limited by a cluster’s memory space. ",[],{},{"nodeType":940,"data":24440,"content":24441},{},[24442],{"nodeType":944,"value":24443,"marks":24444,"data":24445},"What ultimately determines success is not just that context can be stored, but how the system behaves once that context becomes large, shared, and continuously accessed.",[],{},{"nodeType":23519,"data":24447,"content":24448},{},[24449],{"nodeType":944,"value":24450,"marks":24451,"data":24452},"Where traditional approaches fall short",[],{},{"nodeType":940,"data":24454,"content":24455},{},[24456],{"nodeType":944,"value":24457,"marks":24458,"data":24459},"When latency matters, every single operation in the data path will determine the effectiveness of how systems can support inference context data services. Looking across how traditional storage systems are built today, it’s clear that there are a number of inefficiencies that needed to be engineered out of a shared KV$ namespace in order to ensure the absolute lowest time-to-first-token (TTFT).",[],{},{"nodeType":24461,"data":24462,"content":24463},"heading-5",{},[24464],{"nodeType":944,"value":24465,"marks":24466,"data":24467},"The Data Path",[],{},{"nodeType":940,"data":24469,"content":24470},{},[24471,24475,24483],{"nodeType":944,"value":24472,"marks":24473,"data":24474},"Since KV retrieval time dominates the inference experience, let’s focus on the time to retrieve data from shared storage. The journey of a piece of data from a shared SSD (or SSDs) to the GPU memory of a client has never been completely optimal. SSD data gets copied into storage controllers, controllers copy data into file servers, file servers copy data into a host’s CPU memory and data is then copied from CPU memory into GPU memory. Here, RDMA helps - where NVMe-over-Fabrics can eliminate a memory copy operation into a controller or file server, and ",[],{},{"nodeType":6165,"data":24476,"content":24478},{"uri":24477},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-gpu-direct-for-storage-gds-the-vast-data-story",[24479],{"nodeType":944,"value":24480,"marks":24481,"data":24482},"GPUDirect Storage",[],{},{"nodeType":944,"value":24484,"marks":24485,"data":24486}," can eliminate the copy from the file server into CPU memory - placing data directly into GPU memory from the file server.",[],{},{"nodeType":24488,"data":24489,"content":24509},"embedded-asset-block",{"target":24490},{"metadata":24491,"sys":24494,"fields":24501},{"tags":24492,"concepts":24493},[],[],{"space":24495,"id":24497,"type":42,"createdAt":24498,"updatedAt":24498,"environment":24499,"publishedVersion":140,"revision":47,"locale":29},{"sys":24496},{"type":12,"linkType":13,"id":14},"73hbkpyjPBkshaIMlVJtUJ","2026-01-03T00:14:21.425Z",{"sys":24500},{"id":21,"type":12,"linkType":22},{"title":24314,"description":24314,"file":24502},{"url":24503,"details":24504,"fileName":24508,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F73hbkpyjPBkshaIMlVJtUJ\u002F418bcec4ea27e6467866584ff7d65583\u002Fmore-inference-less-infrastructure-1.webp",{"size":24505,"image":24506},132228,{"width":55,"height":24507},810,"more-inference-less-infrastructure-1.webp",[],{"nodeType":940,"data":24511,"content":24512},{},[24513],{"nodeType":944,"value":24514,"marks":24515,"data":24516},"While RDMA has made it possible to build streamlined data paths, there’s still one more copy to eliminate to make the NVIDIA Inference Context Memory Storage Platform architecture maximally performant.",[],{},{"nodeType":24461,"data":24518,"content":24519},{},[24520],{"nodeType":944,"value":24521,"marks":24522,"data":24523},"Server Contention",[],{},{"nodeType":940,"data":24525,"content":24526},{},[24527],{"nodeType":944,"value":24528,"marks":24529,"data":24530},"Once we’ve optimized the data path, there’s still the issue of resource contention. Since the dawn of distributed computing, the classic client:server architecture has always dictated that a large number of clients typically compete for a pooled set of shared and limited resources that never assume that all clients are writing to and reading from storage servers at the same time… which is the way that KV Caching operates. To build the most optimized Inference Context Memory Storage Platform architecture, we also need to rethink the classic client:server model to ensure all hosts are concurrently equipped with dedicated and high-performance access to storage server infrastructure. ",[],{},{"nodeType":23519,"data":24532,"content":24533},{},[24534],{"nodeType":944,"value":24535,"marks":24536,"data":24537},"What VAST changes at the system level",[],{},{"nodeType":940,"data":24539,"content":24540},{},[24541,24545,24552],{"nodeType":944,"value":24542,"marks":24543,"data":24544},"The ",[],{},{"nodeType":6165,"data":24546,"content":24548},{"uri":24547},"https:\u002F\u002Fwww.vastdata.com\u002F",[24549],{"nodeType":944,"value":13962,"marks":24550,"data":24551},[],{},{"nodeType":944,"value":24553,"marks":24554,"data":24555}," delivers the fastest AI data systems we’ve ever built, powered by NVIDIA accelerated computing and AI stack. Through this effort, we can deliver massively-scalable shared KV cache clusters that are not only working to minimize the TTFT, but also minimize the power, space and cost requirements to enable optimzed Inference Context Memory Storage for NVIDIA Rubin clusters.",[],{},{"nodeType":24488,"data":24557,"content":24576},{"target":24558},{"metadata":24559,"sys":24562,"fields":24569},{"tags":24560,"concepts":24561},[],[],{"space":24563,"id":24565,"type":42,"createdAt":24566,"updatedAt":24566,"environment":24567,"publishedVersion":140,"revision":47,"locale":29},{"sys":24564},{"type":12,"linkType":13,"id":14},"3QRqwU9vittu2SJ1sQdDt1","2026-01-03T00:15:32.167Z",{"sys":24568},{"id":21,"type":12,"linkType":22},{"title":24314,"description":24314,"file":24570},{"url":24571,"details":24572,"fileName":24575,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3QRqwU9vittu2SJ1sQdDt1\u002F3b1af9a83a8c03c86177c5b077d1f1a9\u002Fmore-inference-less-infrastructure-2.webp",{"size":24573,"image":24574},54620,{"width":55,"height":56},"more-inference-less-infrastructure-2.webp",[],{"nodeType":940,"data":24578,"content":24579},{},[24580],{"nodeType":944,"value":24581,"marks":24582,"data":24583},"VAST approaches this problem by rethinking systems architecture altogether.",[],{},{"nodeType":23519,"data":24585,"content":24586},{},[24587],{"nodeType":944,"value":24588,"marks":24589,"data":24590},"Server and Data Path Consolidation",[],{},{"nodeType":940,"data":24592,"content":24593},{},[24594],{"nodeType":944,"value":24595,"marks":24596,"data":24597},"The NVIDIA Inference Context Memory Storage Platform embeds data management directly into the GPU server by allowing VAST CNode software to run natively in a node’s BlueField-4 data processing unit (DPU). NVIDIA BlueField DPUs provide the CPU cycles to host all aspects of VAST software - colocating placement decisions, access enforcement, and fast-path metadata resolution directly onto the GPU host and eliminating a whole fleet of x86 servers. Now, I\u002FO processing happens close to execution, where inference actually runs.",[],{},{"nodeType":24488,"data":24599,"content":24619},{"target":24600},{"metadata":24601,"sys":24604,"fields":24611},{"tags":24602,"concepts":24603},[],[],{"space":24605,"id":24607,"type":42,"createdAt":24608,"updatedAt":24608,"environment":24609,"publishedVersion":186,"revision":47,"locale":29},{"sys":24606},{"type":12,"linkType":13,"id":14},"5b0HAep7m2ctGBHAWiJ7Ls","2026-01-06T11:35:01.093Z",{"sys":24610},{"id":21,"type":12,"linkType":22},{"title":24314,"description":24314,"file":24612},{"url":24613,"details":24614,"fileName":24618,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5b0HAep7m2ctGBHAWiJ7Ls\u002F41cf913eba901439a089093ff8a54beb\u002Fmore-inference-less-infrastructure-vast-nvidia-3.webp",{"size":24615,"image":24616},40530,{"width":55,"height":24617},618,"more-inference-less-infrastructure-vast-nvidia-3.webp",[],{"nodeType":940,"data":24621,"content":24622},{},[24623],{"nodeType":944,"value":24624,"marks":24625,"data":24626},"The result is a solution that not only saves big money on x86 HW costs, it also saves power.",[],{},{"nodeType":24488,"data":24628,"content":24649},{"target":24629},{"metadata":24630,"sys":24633,"fields":24641},{"tags":24631,"concepts":24632},[],[],{"space":24634,"id":24636,"type":42,"createdAt":24637,"updatedAt":24638,"environment":24639,"publishedVersion":72,"revision":186,"locale":29},{"sys":24635},{"type":12,"linkType":13,"id":14},"31WWlz5mE1NiFffkgo8vEx","2026-01-05T20:36:54.025Z","2026-01-09T21:43:19.857Z",{"sys":24640},{"id":21,"type":12,"linkType":22},{"title":24642,"description":24642,"file":24643},"VAST’s CME reduces KV$ power by 75%",{"url":24644,"details":24645,"fileName":24648,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F31WWlz5mE1NiFffkgo8vEx\u002F04af8f38c44df24c2e95167a7251ba97\u002Fmore-inference-less-infrastructure-vast-nvidia-1.webp",{"size":24646,"image":24647},160336,{"width":55,"height":56},"more-inference-less-infrastructure-vast-nvidia-1.webp",[],{"nodeType":940,"data":24651,"content":24652},{},[24653,24657,24664],{"nodeType":944,"value":24654,"marks":24655,"data":24656},"Second, by eliminating the x86 server layer, VAST RDMA clients can implement zero-copy I\u002FO operations directly from GPU memory (using ",[],{},{"nodeType":6165,"data":24658,"content":24660},{"uri":24659},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fgpudirect",[24661],{"nodeType":944,"value":24480,"marks":24662,"data":24663},[],{},{"nodeType":944,"value":24665,"marks":24666,"data":24667},") all the way to the NVMe SSDs that KV data is stored on and retrieved from. By eliminating a copy operation, we can further reduce time-to-first token.",[],{},{"nodeType":24488,"data":24669,"content":24688},{"target":24670},{"metadata":24671,"sys":24674,"fields":24681},{"tags":24672,"concepts":24673},[],[],{"space":24675,"id":24677,"type":42,"createdAt":24678,"updatedAt":24678,"environment":24679,"publishedVersion":140,"revision":47,"locale":29},{"sys":24676},{"type":12,"linkType":13,"id":14},"6zFnKHnCYwmkdmIn81c200","2026-01-03T00:16:41.897Z",{"sys":24680},{"id":21,"type":12,"linkType":22},{"title":24314,"description":24314,"file":24682},{"url":24683,"details":24684,"fileName":24687,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6zFnKHnCYwmkdmIn81c200\u002F2c1607adf3b5b251ec25347b33652408\u002Fmore-inference-less-infrastructure-4.webp",{"size":24685,"image":24686},116542,{"width":55,"height":24507},"more-inference-less-infrastructure-4.webp",[],{"nodeType":940,"data":24690,"content":24691},{},[24692,24696,24703],{"nodeType":944,"value":24693,"marks":24694,"data":24695},"How is it possible to embed servers into GPU hosts without adding complication? This is all possible thanks to ",[],{},{"nodeType":6165,"data":24697,"content":24698},{"uri":23633},[24699],{"nodeType":944,"value":24700,"marks":24701,"data":24702},"VAST’s revolutionary DASE architecture",[],{},{"nodeType":944,"value":24704,"marks":24705,"data":24706},". DASE enables each CNode server container to operate completely in parallel, imposing zero east-west traffic within a multi-server storage environment. Since none of the servers are exclusive owners of a partition of a file systems storage device (as is the case with conventional enterprise NAS systems and parallel file systems), then each host can have its own dedicated file server without getting bothered by any other host-resident file server in a large GPU cluster.",[],{},{"nodeType":24488,"data":24708,"content":24728},{"target":24709},{"metadata":24710,"sys":24713,"fields":24721},{"tags":24711,"concepts":24712},[],[],{"space":24714,"id":24716,"type":42,"createdAt":24717,"updatedAt":24718,"environment":24719,"publishedVersion":280,"revision":346,"locale":29},{"sys":24715},{"type":12,"linkType":13,"id":14},"2ddyElKsae6cNQY2abGbDw","2026-01-03T00:17:28.710Z","2026-01-13T04:29:53.821Z",{"sys":24720},{"id":21,"type":12,"linkType":22},{"title":24314,"description":24314,"file":24722},{"url":24723,"details":24724,"fileName":24727,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2ddyElKsae6cNQY2abGbDw\u002F001c3d4ce87c65c886397aaf0b766052\u002Fmore-inference-less-infrastructure-5-v2.webp",{"size":24725,"image":24726},79406,{"width":55,"height":56},"more-inference-less-infrastructure-5-v2.webp",[],{"nodeType":940,"data":24730,"content":24731},{},[24732],{"nodeType":944,"value":24733,"marks":24734,"data":24737},"VAST’s parallel DASE architecture executes control logic on BlueField-4 DPUs, eliminating east-west coordination while allowing each host to access all SSDs in parallel. Each DPU sees the full storage namespace directly, delivering dedicated bandwidth per host without cross-talk as inference concurrency scales.",[24735],{"type":24736},"italic",{},{"nodeType":24461,"data":24739,"content":24740},{},[24741],{"nodeType":944,"value":24742,"marks":24743,"data":24744},"Performance Without Data Services Compromise",[],{},{"nodeType":940,"data":24746,"content":24747},{},[24748],{"nodeType":944,"value":24749,"marks":24750,"data":24751},"Since KV data is easily reproducible, we can provide AI labs the tools needed to both optimize for performance while also making it possible to enable a variety of tools for data management. As AI Inference moves out of its exploratory phase into serious application, some AI labs must build services to meet the requirements of heavily regulated industries. With VAST - they don’t need to choose only for maximum performance and can enable a set of optional data services that help them deliver safe and reproducible AI. We can envision a future collaboration with the NVIDIA Dynamo team where Dynamo APIs direct Inference Context Memory Storage systems to handle different data sets differently as they talk to shared services.",[],{},{"nodeType":24488,"data":24753,"content":24775},{"target":24754},{"metadata":24755,"sys":24758,"fields":24766},{"tags":24756,"concepts":24757},[],[],{"space":24759,"id":24761,"type":42,"createdAt":24762,"updatedAt":24763,"environment":24764,"publishedVersion":1170,"revision":163,"locale":29},{"sys":24760},{"type":12,"linkType":13,"id":14},"4vUBIjuD1YB6Lzj1UzoktA","2026-01-05T20:32:53.599Z","2026-01-06T11:33:17.588Z",{"sys":24765},{"id":21,"type":12,"linkType":22},{"title":24767,"description":24767,"file":24768},"The VAST AI Operating System",{"url":24769,"details":24770,"fileName":24774,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4vUBIjuD1YB6Lzj1UzoktA\u002Fa4a7c4766fbea320872fa65534a57ac2\u002Fmore-inference-less-infrastructure-vast-nvidia-2.webp",{"size":24771,"image":24772},67644,{"width":55,"height":24773},899,"more-inference-less-infrastructure-vast-nvidia-2.webp",[],{"nodeType":940,"data":24777,"content":24778},{},[24779],{"nodeType":944,"value":24780,"marks":24781,"data":24782},"Since VAST’s parallel DASE architecture can add additional CNodes out of band, the result is inference history that remains accessible and globally manageable as inference KV$ concurrency increases, while providing a set of tools to provide background services that do not reintroduce the data management overhead the Inference Context Memory Storage Platform is designed to eliminate.",[],{},{"nodeType":23519,"data":24784,"content":24785},{},[24786],{"nodeType":944,"value":24787,"marks":24788,"data":24789},"How BlueField-4 enables this shift",[],{},{"nodeType":940,"data":24791,"content":24792},{},[24793],{"nodeType":944,"value":24794,"marks":24795,"data":24796},"The Inference Context Memory Storage Platform architecture aligns directly with the NVIDIA BlueField‑4 DPU's operation.",[],{},{"nodeType":940,"data":24798,"content":24799},{},[24800],{"nodeType":944,"value":24801,"marks":24802,"data":24803},"DPUs create the opportunity for new storage service deployment paradigms. By adding accelerated processing cores directly onto a network adapter, this advanced data processor serves as the enforcement point for placement, access, and validation directly in the data path. By executing these functions on BlueField-4 at line rate, the host CPU is removed from the critical path as DPUs can now talk directly to NVMe devices in storage enclosures over a shared NVMe-oF fabric. Fewer components participate in each access, fewer transitions are required, and system behavior becomes more predictable under load.",[],{},{"nodeType":23519,"data":24805,"content":24806},{},[24807],{"nodeType":944,"value":24808,"marks":24809,"data":24810},"How NVIDIA Spectrum-X enables this shift",[],{},{"nodeType":940,"data":24812,"content":24813},{},[24814,24817,24825],{"nodeType":944,"value":401,"marks":24815,"data":24816},[],{},{"nodeType":6165,"data":24818,"content":24820},{"uri":24819},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-spectrum-x-and-vast-data-platform",[24821],{"nodeType":944,"value":24822,"marks":24823,"data":24824},"NVIDIA Spectrum‑X Ethernet ",[],{},{"nodeType":944,"value":24826,"marks":24827,"data":24828},"fabric plays a critical role by delivering low-latency, high-bandwidth transport with congestion control and performance isolation for large AI fabrics. The platform is designed to operate on the Spectrum-X Ethernet fabric as part of the broader Dynamo inference orchestration framework and enable predictable KV$ service under sustained cluster-wide load.",[],{},{"nodeType":23519,"data":24830,"content":24831},{},[24832],{"nodeType":944,"value":24833,"marks":24834,"data":24835},"Flexibility across deployment models",[],{},{"nodeType":940,"data":24837,"content":24838},{},[24839],{"nodeType":944,"value":24840,"marks":24841,"data":24842},"This behavior is not tied to a single hardware form factor.",[],{},{"nodeType":940,"data":24844,"content":24845},{},[24846],{"nodeType":944,"value":24847,"marks":24848,"data":24849},"While early deployments may showcase dense and fault-tolerant NVMe enclosures for efficiency and footprint, the NVIDIA Inference Context Memory Storage Platform + VAST architectural principles extend naturally to other configurations built from commodity storage servers. Because inference history is shared across the system and access is enforced close to the GPU, the loss of any individual server does not trigger a system-wide context rebuild in the case where customers choose to disable erasure encoding. Inference continues using the remaining distributed history, avoiding the large-scale recreation events that traditionally follow node failures.",[],{},{"nodeType":23519,"data":24851,"content":24852},{},[24853],{"nodeType":944,"value":24854,"marks":24855,"data":24856},"Functional outcomes that matter",[],{},{"nodeType":940,"data":24858,"content":24859},{},[24860],{"nodeType":944,"value":24861,"marks":24862,"data":24863},"The impact of this architectural shift is straightforward.",[],{},{"nodeType":940,"data":24865,"content":24866},{},[24867],{"nodeType":944,"value":24868,"marks":24869,"data":24870},"Inference history becomes accessible as a shared system resource without forcing every access through a centralized and limited storage server processing tier. GPU utilization improves because access decisions and context retrieval happen closer to execution. Concurrency scales because each GPU machine has its own dedicated and parallel data processing services. Power efficiency and infrastructure footprint improve because the whole solution has been fully optimized for end-to-end KV cache serving.",[],{},{"nodeType":940,"data":24872,"content":24873},{},[24874],{"nodeType":944,"value":24875,"marks":24876,"data":24877},"These outcomes align directly with the NVIDIA Inference Context Memory Storage Platform's focus on sustained throughput and power efficiency at scale.",[],{},{"nodeType":23519,"data":24879,"content":24880},{},[24881],{"nodeType":944,"value":24882,"marks":24883,"data":24884},"A leap in coherent inference",[],{},{"nodeType":940,"data":24886,"content":24887},{},[24888,24892,24900],{"nodeType":944,"value":24889,"marks":24890,"data":24891},"The platform defines context history as a first-class system memory resource. NVIDIA ",[],{},{"nodeType":6165,"data":24893,"content":24895},{"uri":24894},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fadvancing-the-state-of-the-art-with-nvidia-blue-field-4",[24896],{"nodeType":944,"value":24897,"marks":24898,"data":24899},"BlueField-4",[],{},{"nodeType":944,"value":24901,"marks":24902,"data":24903}," provides an efficient storage controller and the enforcement point for placement, access, and isolation at the boundary where inference runs. Spectrum-X Ethernet provides predictable transport. Dynamo provides orchestration.",[],{},{"nodeType":940,"data":24905,"content":24906},{},[24907],{"nodeType":944,"value":24908,"marks":24909,"data":24910},"VAST delivers the software core that allows these components to operate as a unified and coherent system, keeping inference history accessible, governable, and efficient as context sizes, concurrency and regulatory requirements continue to grow.",[],{},{"nodeType":940,"data":24912,"content":24913},{},[24914,24919,24928],{"nodeType":944,"value":24915,"marks":24916,"data":24918},"Want to see how this all works in practice? 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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":25168,"content":25169,"nodeType":24068},{},[25170],{"data":25171,"content":25172,"nodeType":940},{},[25173],{"data":25174,"marks":25175,"value":25176,"nodeType":944},{},[],"The NVIDIA Dynamo inference framework is bringing novel capabilities to market such as key-value cache (KV$ in short) offload",{"data":25178,"content":25179,"nodeType":24068},{},[25180],{"data":25181,"content":25182,"nodeType":940},{},[25183],{"data":25184,"marks":25185,"value":25186,"nodeType":944},{},[],"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":25188,"content":25189,"nodeType":24068},{},[25190],{"data":25191,"content":25192,"nodeType":940},{},[25193],{"data":25194,"marks":25195,"value":25196,"nodeType":944},{},[],"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":25198,"content":25199,"nodeType":24068},{},[25200],{"data":25201,"content":25202,"nodeType":940},{},[25203],{"data":25204,"marks":25205,"value":25206,"nodeType":944},{},[],"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":25208,"content":25209,"nodeType":24068},{},[25210],{"data":25211,"content":25212,"nodeType":940},{},[25213],{"data":25214,"marks":25215,"value":25216,"nodeType":944},{},[],"This blog post covers sizing and architecture considerations when building for large-scale inference infrastructure that is enabled for KV$ offload",{"data":25218,"content":25219,"nodeType":24068},{},[25220],{"data":25221,"content":25222,"nodeType":940},{},[25223,25227,25235,25239,25244,25248,25253],{"data":25224,"marks":25225,"value":25226,"nodeType":944},{},[],"VAST collaborated with CoreWeave to evaluate ",{"data":25228,"content":25230,"nodeType":6165},{"uri":25229},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-to-reduce-kv-cache-bottlenecks-with-nvidia-dynamo\u002F",[25231],{"data":25232,"marks":25233,"value":25234,"nodeType":944},{},[],"NVIDIA’s Dynamo KV$",{"data":25236,"marks":25237,"value":25238,"nodeType":944},{},[]," offload with the VAST AI OS and saw ",{"data":25240,"marks":25241,"value":25243,"nodeType":944},{},[25242],{"type":24041},"GPU efficiency improve by 90%",{"data":25245,"marks":25246,"value":25247,"nodeType":944},{},[]," and ",{"data":25249,"marks":25250,"value":25252,"nodeType":944},{},[25251],{"type":24041},"TTFT improve by 20x",{"data":25254,"marks":25255,"value":25256,"nodeType":944},{},[]," as compared to when GPUs need to prefill calculate every past conversation",{"data":25258,"content":25259,"nodeType":24068},{},[25260],{"data":25261,"content":25262,"nodeType":940},{},[25263],{"data":25264,"marks":25265,"value":25266,"nodeType":944},{},[],"Dynamo data persistence now relies on data batching for large asynchronous writes, and bulk synchronous reads - resulting in an IO profile focused on storage throughput, not IOPS and low latency",{"data":25268,"content":25269,"nodeType":24068},{},[25270],{"data":25271,"content":25272,"nodeType":940},{},[25273,25277,25284],{"data":25274,"marks":25275,"value":25276,"nodeType":944},{},[],"Ultimately, VAST’s testing identified that most KV$ 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":25278,"content":25279,"nodeType":6165},{"uri":24819},[25280],{"data":25281,"marks":25282,"value":25283,"nodeType":944},{},[],"here",{"data":25285,"marks":25286,"value":6169,"nodeType":944},{},[],{"data":25288,"content":25289,"nodeType":25290},{},[],"hr",{"data":25292,"content":25293,"nodeType":940},{},[25294],{"data":25295,"marks":25296,"value":25297,"nodeType":944},{},[],"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":25299,"content":25300,"nodeType":940},{},[25301,25305,25312],{"data":25302,"marks":25303,"value":25304,"nodeType":944},{},[],"Previously, we wrote about ",{"data":25306,"content":25307,"nodeType":6165},{"uri":24369},[25308],{"data":25309,"marks":25310,"value":25311,"nodeType":944},{},[],"accelerating inference and the AI data pipeline",{"data":25313,"marks":25314,"value":25315,"nodeType":944},{},[]," 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":25317,"content":25318,"nodeType":940},{},[25319,25323,25330,25333,25341,25345,25353],{"data":25320,"marks":25321,"value":25322,"nodeType":944},{},[],"Today, we're excited to share the next major step in this journey: the collaboration and integration between the ",{"data":25324,"content":25326,"nodeType":6165},{"uri":25325},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fai-os",[25327],{"data":25328,"marks":25329,"value":13912,"nodeType":944},{},[],{"data":25331,"marks":25332,"value":25247,"nodeType":944},{},[],{"data":25334,"content":25336,"nodeType":6165},{"uri":25335},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fdynamo\u002F",[25337],{"data":25338,"marks":25339,"value":25340,"nodeType":944},{},[],"NVIDIA Dynamo",{"data":25342,"marks":25343,"value":25344,"nodeType":944},{},[],". This article will detail Dynamo's sophisticated ",{"data":25346,"content":25348,"nodeType":6165},{"uri":25347},"https:\u002F\u002Fdocs.nvidia.com\u002Fdynamo\u002Farchive\u002F0.2.0\u002Farchitecture\u002Fkv_cache_manager.html",[25349],{"data":25350,"marks":25351,"value":25352,"nodeType":944},{},[],"architecture",{"data":25354,"marks":25355,"value":25356,"nodeType":944},{},[]," and show how the VAST AI OS is now integrated as a foundational, high-performance capacity solution to solve the GPU memory wall.",{"data":25358,"content":25359,"nodeType":23519},{},[25360],{"data":25361,"marks":25362,"value":25363,"nodeType":944},{},[],"What is NVIDIA Dynamo?",{"data":25365,"content":25366,"nodeType":940},{},[25367,25371,25378],{"data":25368,"marks":25369,"value":25370,"nodeType":944},{},[],"Before we dive into the problem, let's briefly introduce ",{"data":25372,"content":25374,"nodeType":6165},{"uri":25373},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fwhy-everyones-talking-about-nvidia-dynamo-and-why-it-actually-matters",[25375],{"data":25376,"marks":25377,"value":25340,"nodeType":944},{},[],{"data":25379,"marks":25380,"value":25381,"nodeType":944},{},[],". 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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":25436,"content":25437,"nodeType":23519},{},[25438],{"data":25439,"marks":25440,"value":25441,"nodeType":944},{},[],"The Solution: A New Caching Architecture",{"data":25443,"content":25444,"nodeType":940},{},[25445],{"data":25446,"marks":25447,"value":25448,"nodeType":944},{},[],"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":25450,"content":25451,"nodeType":940},{},[25452],{"data":25453,"marks":25454,"value":25455,"nodeType":944},{},[],"Here is the four-tiered architecture as defined by NVIDIA:",{"data":25457,"content":25458,"nodeType":24064},{},[25459,25474,25489,25504],{"data":25460,"content":25461,"nodeType":24068},{},[25462],{"data":25463,"content":25464,"nodeType":940},{},[25465,25470],{"data":25466,"marks":25467,"value":25469,"nodeType":944},{},[25468],{"type":24041},"G1: Device Memory (GPU HBM):",{"data":25471,"marks":25472,"value":25473,"nodeType":944},{},[]," 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":25505,"content":25506,"nodeType":24068},{},[25507],{"data":25508,"content":25509,"nodeType":940},{},[25510,25515],{"data":25511,"marks":25512,"value":25514,"nodeType":944},{},[25513],{"type":24041},"G4: Shared Network Storage:",{"data":25516,"marks":25517,"value":25518,"nodeType":944},{},[]," 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. Persistence is a key difference from the G3 tier, which focuses on extending capacity for live inference sessions, especially multi-turn agentic conversations. In the future, the VAST AI OS will also be integrated at the G4 tier to provide \"long-term memory\" capability.",{"data":25520,"content":25521,"nodeType":940},{},[25522],{"data":25523,"marks":25524,"value":25525,"nodeType":944},{},[],"This tiered design allows Dynamo to handle memory pressure gracefully. 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":25527,"content":25547,"nodeType":24488},{"target":25528},{"metadata":25529,"sys":25532,"fields":25539},{"tags":25530,"concepts":25531},[],[],{"space":25533,"id":25535,"type":42,"createdAt":25536,"updatedAt":25536,"environment":25537,"publishedVersion":140,"revision":47,"locale":29},{"sys":25534},{"type":12,"linkType":13,"id":14},"3rd6P1QQmnIepmPq1YkZd1","2026-01-14T12:20:42.908Z",{"sys":25538},{"id":21,"type":12,"linkType":22},{"title":25108,"description":25108,"file":25540},{"url":25541,"details":25542,"fileName":25546,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3rd6P1QQmnIepmPq1YkZd1\u002F5d441ebfa62d8cefe45cd048c47ddeb1\u002Fnvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",{"size":25543,"image":25544},46764,{"width":55,"height":25545},781,"nvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",[],{"data":25549,"content":25550,"nodeType":940},{},[25551,25556,25563],{"data":25552,"marks":25553,"value":25555,"nodeType":944},{},[25554],{"type":24736},"Dynamo architecture ",{"data":25557,"content":25558,"nodeType":6165},{"uri":25347},[25559],{"data":25560,"marks":25561,"value":25347,"nodeType":944},{},[25562],{"type":24736},{"data":25564,"marks":25565,"value":401,"nodeType":944},{},[],{"data":25567,"content":25568,"nodeType":25290},{},[],{"data":25570,"content":25571,"nodeType":23519},{},[25572],{"data":25573,"marks":25574,"value":25575,"nodeType":944},{},[],"Architecting For KV$ Offload",{"data":25577,"content":25578,"nodeType":24461},{},[25579],{"data":25580,"marks":25581,"value":25582,"nodeType":944},{},[],"General Architecture Considerations",{"data":25584,"content":25585,"nodeType":25626},{},[25586,25596,25606,25616],{"data":25587,"content":25588,"nodeType":24068},{},[25589],{"data":25590,"content":25591,"nodeType":940},{},[25592],{"data":25593,"marks":25594,"value":25595,"nodeType":944},{},[],"KV$ is stored in large blocks, making KV$ offload a workload that demands high throughput",{"data":25597,"content":25598,"nodeType":24068},{},[25599],{"data":25600,"content":25601,"nodeType":940},{},[25602],{"data":25603,"marks":25604,"value":25605,"nodeType":944},{},[],"The network and supporting storage subsystems must scale with\u002Fto the count of GPUs",{"data":25607,"content":25608,"nodeType":24068},{},[25609],{"data":25610,"content":25611,"nodeType":940},{},[25612],{"data":25613,"marks":25614,"value":25615,"nodeType":944},{},[],"Each host’s network access to storage will determine its TTFT performance",{"data":25617,"content":25618,"nodeType":24068},{},[25619],{"data":25620,"content":25621,"nodeType":940},{},[25622],{"data":25623,"marks":25624,"value":25625,"nodeType":944},{},[],"KV$ carries conversation information and should be encrypted using an encryption key per-customer, following the best practices of securing user information","ordered-list",{"data":25628,"content":25629,"nodeType":24461},{},[25630],{"data":25631,"marks":25632,"value":25633,"nodeType":944},{},[],"VAST Data Architecture Considerations",{"data":25635,"content":25636,"nodeType":25626},{},[25637,25658,25680],{"data":25638,"content":25639,"nodeType":24068},{},[25640],{"data":25641,"content":25642,"nodeType":940},{},[25643,25647,25655],{"data":25644,"marks":25645,"value":25646,"nodeType":944},{},[],"Any VAST cluster supports KV$ offload today via Dynamo’s transfer library (",{"data":25648,"content":25650,"nodeType":6165},{"uri":25649},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fnixl",[25651],{"data":25652,"marks":25653,"value":25654,"nodeType":944},{},[],"NIXL",{"data":25656,"marks":25657,"value":25401,"nodeType":944},{},[],{"data":25659,"content":25660,"nodeType":24068},{},[25661],{"data":25662,"content":25663,"nodeType":940},{},[25664,25668,25676],{"data":25665,"marks":25666,"value":25667,"nodeType":944},{},[],"Use the ",{"data":25669,"content":25671,"nodeType":6165},{"uri":25670},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002FIntro.html",[25672],{"data":25673,"marks":25674,"value":25675,"nodeType":944},{},[],"VAST NFS",{"data":25677,"marks":25678,"value":25679,"nodeType":944},{},[]," driver, to enable multipathing to saturate the IO needs of any GPU machine",{"data":25681,"content":25682,"nodeType":24068},{},[25683],{"data":25684,"content":25685,"nodeType":940},{},[25686],{"data":25687,"marks":25688,"value":25689,"nodeType":944},{},[],"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":25691,"content":25692,"nodeType":25290},{},[],{"data":25694,"content":25695,"nodeType":23519},{},[25696],{"data":25697,"marks":25698,"value":25699,"nodeType":944},{},[],"Calculating KV$ Footprint",{"data":25701,"content":25702,"nodeType":940},{},[25703],{"data":25704,"marks":25705,"value":25706,"nodeType":944},{},[],"KV$ creates gigabytes of data per prompt and is strongly dependent on model parameters:",{"data":25708,"content":25709,"nodeType":940},{},[25710],{"data":25711,"marks":25712,"value":25713,"nodeType":944},{},[],"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":25715,"content":25716,"nodeType":940},{},[25717],{"data":25718,"marks":25719,"value":25720,"nodeType":944},{},[],"Size per Token = 126 * 2 ( for Keys and Values)  * 8 * 128 * 2 (bytes) = 516,096 bytes",{"data":25722,"content":25723,"nodeType":940},{},[25724,25728,25733],{"data":25725,"marks":25726,"value":25727,"nodeType":944},{},[],"This results in approximately ",{"data":25729,"marks":25730,"value":25732,"nodeType":944},{},[25731],{"type":24041},"504 KiB per token",{"data":25734,"marks":25735,"value":25736,"nodeType":944},{},[],". For a single user utilizing a full 127,188-token context window, the cache becomes massive:",{"data":25738,"content":25739,"nodeType":940},{},[25740,25744,25748],{"data":25741,"marks":25742,"value":25743,"nodeType":944},{},[],"504KiB\u002Ftoken * 127,188 tokens ≈",{"data":25745,"marks":25746,"value":24053,"nodeType":944},{},[25747],{"type":24041},{"data":25749,"marks":25750,"value":25751,"nodeType":944},{},[]," 61 GB",{"data":25753,"content":25754,"nodeType":23519},{},[25755],{"data":25756,"marks":25757,"value":25759,"nodeType":944},{},[25758],{"type":24041},"Modeling the \"Data Dump\" Scenarios",{"data":25761,"content":25762,"nodeType":940},{},[25763],{"data":25764,"marks":25765,"value":25766,"nodeType":944},{},[],"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":25768,"content":25769,"nodeType":24064},{},[25770,25785],{"data":25771,"content":25772,"nodeType":24068},{},[25773],{"data":25774,"content":25775,"nodeType":940},{},[25776,25781],{"data":25777,"marks":25778,"value":25780,"nodeType":944},{},[25779],{"type":24041},"The 'Power User' Returns:",{"data":25782,"marks":25783,"value":25784,"nodeType":944},{},[]," 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":25786,"content":25787,"nodeType":24068},{},[25788],{"data":25789,"content":25790,"nodeType":940},{},[25791,25796],{"data":25792,"marks":25793,"value":25795,"nodeType":944},{},[25794],{"type":24041},"The RAG Workflow:",{"data":25797,"marks":25798,"value":25799,"nodeType":944},{},[]," 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":25801,"content":25802,"nodeType":25290},{},[],{"data":25804,"content":25805,"nodeType":24461},{},[25806],{"data":25807,"marks":25808,"value":25809,"nodeType":944},{},[],"How Much Capacity Do You Need?",{"data":25811,"content":25812,"nodeType":940},{},[25813],{"data":25814,"marks":25815,"value":25816,"nodeType":944},{},[],"The following is an example formula that can guide customers in sizing for KV$:",{"data":25818,"content":25819,"nodeType":940},{},[25820,25825],{"data":25821,"marks":25822,"value":25824,"nodeType":944},{},[25823],{"type":24041},"Total Capacity = ",{"data":25826,"marks":25827,"value":25828,"nodeType":944},{},[],"Number of Users * Average KV$ per User * Retention Multiplier",{"data":25830,"content":25831,"nodeType":940},{},[25832],{"data":25833,"marks":25834,"value":25835,"nodeType":944},{},[],"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":25837,"content":25838,"nodeType":24064},{},[25839,25863,25878],{"data":25840,"content":25841,"nodeType":24068},{},[25842],{"data":25843,"content":25844,"nodeType":940},{},[25845,25850,25854,25859],{"data":25846,"marks":25847,"value":25849,"nodeType":944},{},[25848],{"type":24041},"Average KV$ per Prompt:",{"data":25851,"marks":25852,"value":25853,"nodeType":944},{},[]," 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":25855,"marks":25856,"value":25858,"nodeType":944},{},[25857],{"type":24736},"average",{"data":25860,"marks":25861,"value":25862,"nodeType":944},{},[]," for a prompt is 64K tokens, roughly 30 GB",{"data":25864,"content":25865,"nodeType":24068},{},[25866],{"data":25867,"content":25868,"nodeType":940},{},[25869,25874],{"data":25870,"marks":25871,"value":25873,"nodeType":944},{},[25872],{"type":24041},"Retention Multiplier:",{"data":25875,"marks":25876,"value":25877,"nodeType":944},{},[]," We'll use 15 to provide a good user experience",{"data":25879,"content":25880,"nodeType":24068},{},[25881],{"data":25882,"content":25883,"nodeType":940},{},[25884,25889,25893],{"data":25885,"marks":25886,"value":25888,"nodeType":944},{},[25887],{"type":24041},"Calculation:",{"data":25890,"marks":25891,"value":25892,"nodeType":944},{},[]," 100,000 users * 30 GB * 15 = 45,000,000 GB = ",{"data":25894,"marks":25895,"value":25897,"nodeType":944},{},[25896],{"type":24041},"45 PB",{"data":25899,"content":25900,"nodeType":940},{},[25901],{"data":25902,"marks":25903,"value":25904,"nodeType":944},{},[],"To support 100,000 users\u002Fagents on a large-context model with a robust conversation history, you need 45 petabytes of storage. ",{"data":25906,"content":25907,"nodeType":25290},{},[],{"data":25909,"content":25910,"nodeType":23519},{},[25911],{"data":25912,"marks":25913,"value":25914,"nodeType":944},{},[],"Understanding the KV$ I\u002FO Profile",{"data":25916,"content":25917,"nodeType":940},{},[25918,25923],{"data":25919,"marks":25920,"value":25922,"nodeType":944},{},[25921],{"type":24041},"Asynchronous Writes (Eviction):",{"data":25924,"marks":25925,"value":25926,"nodeType":944},{},[]," 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":25928,"content":25929,"nodeType":940},{},[25930],{"data":25931,"marks":25932,"value":25933,"nodeType":944},{},[],"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":25935,"content":25936,"nodeType":940},{},[25937,25942],{"data":25938,"marks":25939,"value":25941,"nodeType":944},{},[25940],{"type":24041},"Total Cache per Token (all 126 layers):",{"data":25943,"marks":25944,"value":25945,"nodeType":944},{},[]," 504 KiB",{"data":25947,"content":25948,"nodeType":940},{},[25949,25954],{"data":25950,"marks":25951,"value":25953,"nodeType":944},{},[25952],{"type":24041},"Total Cache per Block (16 tokens):",{"data":25955,"marks":25956,"value":25957,"nodeType":944},{},[]," 504 KiB\u002Ftoken * 16 tokens ≈ 8 MB",{"data":25959,"content":25960,"nodeType":940},{},[25961,25966],{"data":25962,"marks":25963,"value":25965,"nodeType":944},{},[25964],{"type":24041},"Data Written per GPU per Evicted Block (8 ways):",{"data":25967,"marks":25968,"value":25969,"nodeType":944},{},[]," 8064 KiB \u002F 8 GPUs ≈ 1 MB ",{"data":25971,"content":25972,"nodeType":940},{},[25973,25978,25982,25987,25991,25996],{"data":25974,"marks":25975,"value":25977,"nodeType":944},{},[25976],{"type":24041},"Latency-Critical Reads (Retrieval)",{"data":25979,"marks":25980,"value":25981,"nodeType":944},{},[],": When a user session\u002Fconversation is reactivated, the system must fetch the offloaded KV$. While modern inference engines utilize pipeline parallelism and batching to keep the GPU compute units active with ",{"data":25983,"marks":25984,"value":25986,"nodeType":944},{},[25985],{"type":24736},"other",{"data":25988,"marks":25989,"value":25990,"nodeType":944},{},[]," concurrent requests, the TTFT for the ",{"data":25992,"marks":25993,"value":25995,"nodeType":944},{},[25994],{"type":24736},"specific",{"data":25997,"marks":25998,"value":25999,"nodeType":944},{},[]," re-activated user\u002Fconversation is entirely bound by how fast this data can be retrieved.",{"data":26001,"content":26002,"nodeType":940},{},[26003],{"data":26004,"marks":26005,"value":26006,"nodeType":944},{},[],"Therefore, the goal is not to maximize IOPS (processing many small requests) and overly focus on latency, but to maximize throughput (delivering a massive stream of data instantly). AI inference systems need to stream GBs of data linearly into HBM to unblock that specific sequence. This is a bandwidth-intensive workload where the storage tier must deliver the entire KV$ at line-rate to minimize the response time perceived by the user.",{"data":26008,"content":26009,"nodeType":940},{},[26010],{"data":26011,"marks":26012,"value":26014,"nodeType":944},{},[26013],{"type":24736},"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":26016,"content":26017,"nodeType":25290},{},[],{"data":26019,"content":26020,"nodeType":23519},{},[26021],{"data":26022,"marks":26023,"value":26024,"nodeType":944},{},[],"Real-World Performance: VAST and Dynamo in Action",{"data":26026,"content":26027,"nodeType":940},{},[26028],{"data":26029,"marks":26030,"value":26031,"nodeType":944},{},[],"Hardware:",{"data":26033,"content":26034,"nodeType":24064},{},[26035,26050,26065,26080,26095,26110,26125,26140],{"data":26036,"content":26037,"nodeType":24068},{},[26038],{"data":26039,"content":26040,"nodeType":940},{},[26041,26046],{"data":26042,"marks":26043,"value":26045,"nodeType":944},{},[26044],{"type":24041},"Client system:",{"data":26047,"marks":26048,"value":26049,"nodeType":944},{},[]," NVIDIA HGX™ H100",{"data":26051,"content":26052,"nodeType":24068},{},[26053],{"data":26054,"content":26055,"nodeType":940},{},[26056,26061],{"data":26057,"marks":26058,"value":26060,"nodeType":944},{},[26059],{"type":24041},"GPUs:",{"data":26062,"marks":26063,"value":26064,"nodeType":944},{},[]," 8x NVIDIA HGX H100 80GB HBM3",{"data":26066,"content":26067,"nodeType":24068},{},[26068],{"data":26069,"content":26070,"nodeType":940},{},[26071,26076],{"data":26072,"marks":26073,"value":26075,"nodeType":944},{},[26074],{"type":24041},"Network link",{"data":26077,"marks":26078,"value":26079,"nodeType":944},{},[],": 2x100Gbit\u002Fsec Ethernet, Bluefied-3 integrated connectX",{"data":26081,"content":26082,"nodeType":24068},{},[26083],{"data":26084,"content":26085,"nodeType":940},{},[26086,26091],{"data":26087,"marks":26088,"value":26090,"nodeType":944},{},[26089],{"type":24041},"Software:",{"data":26092,"marks":26093,"value":26094,"nodeType":944},{},[]," NVIDIA Dynamo pre-release 0.7.0 (Nov 21, 2025, modified) + vLLM v0.11.0 (modified) + NIXL 0.7.1",{"data":26096,"content":26097,"nodeType":24068},{},[26098],{"data":26099,"content":26100,"nodeType":940},{},[26101,26106],{"data":26102,"marks":26103,"value":26105,"nodeType":944},{},[26104],{"type":24041},"Model:",{"data":26107,"marks":26108,"value":26109,"nodeType":944},{},[]," clowman\u002FLlama-3.1-405B-Instruct-AWQ-Int4",{"data":26111,"content":26112,"nodeType":24068},{},[26113],{"data":26114,"content":26115,"nodeType":940},{},[26116,26121],{"data":26117,"marks":26118,"value":26120,"nodeType":944},{},[26119],{"type":24041},"Run mode:",{"data":26122,"marks":26123,"value":26124,"nodeType":944},{},[]," Tensor parallelism across 8 GPUs",{"data":26126,"content":26127,"nodeType":24068},{},[26128],{"data":26129,"content":26130,"nodeType":940},{},[26131,26136],{"data":26132,"marks":26133,"value":26135,"nodeType":944},{},[26134],{"type":24041},"Storage:",{"data":26137,"marks":26138,"value":26139,"nodeType":944},{},[]," NFS (TCP, VAST NFS multipath, VAST server)",{"data":26141,"content":26142,"nodeType":24068},{},[26143],{"data":26144,"content":26145,"nodeType":940},{},[26146,26151],{"data":26147,"marks":26148,"value":26150,"nodeType":944},{},[26149],{"type":24041},"Prompt:",{"data":26152,"marks":26153,"value":26154,"nodeType":944},{},[]," 127,188 tokens",{"data":26156,"content":26157,"nodeType":24461},{},[26158],{"data":26159,"marks":26160,"value":26161,"nodeType":944},{},[],"Results",{"data":26163,"content":26164,"nodeType":940},{},[26165,26169,26176,26180,26185,26189,26194],{"data":26166,"marks":26167,"value":26168,"nodeType":944},{},[],"Collaborating on this configuration with our partner ",{"data":26170,"content":26172,"nodeType":6165},{"uri":26171},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Fcoreweave",[26173],{"data":26174,"marks":26175,"value":20694,"nodeType":944},{},[],{"data":26177,"marks":26178,"value":26179,"nodeType":944},{},[],", VAST consistently saturates the network during KV$ retrieval, sustaining ",{"data":26181,"marks":26182,"value":26184,"nodeType":944},{},[26183],{"type":24041},"over 90% of line rate (181 Gbps)",{"data":26186,"marks":26187,"value":26188,"nodeType":944},{},[]," and peaking at ",{"data":26190,"marks":26191,"value":26193,"nodeType":944},{},[26192],{"type":24041},"~99% (198 Gbps)",{"data":26195,"marks":26196,"value":26197,"nodeType":944},{},[]," on a 200 Gbps link. ",{"data":26199,"content":26218,"nodeType":24488},{"target":26200},{"metadata":26201,"sys":26204,"fields":26211},{"tags":26202,"concepts":26203},[],[],{"space":26205,"id":26207,"type":42,"createdAt":26208,"updatedAt":26208,"environment":26209,"publishedVersion":140,"revision":47,"locale":29},{"sys":26206},{"type":12,"linkType":13,"id":14},"6DWr3VdK0ZzHJfYC0Wh4a6","2025-12-16T20:19:33.551Z",{"sys":26210},{"id":21,"type":12,"linkType":22},{"title":25108,"description":25108,"file":26212},{"url":26213,"details":26214,"fileName":26217,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWr3VdK0ZzHJfYC0Wh4a6\u002Ff41d8b35caefaba50dcc1245b279fb39\u002Fnvidia-dynamo-vast-scalable-optimized-inference-2.webp",{"size":26215,"image":26216},120100,{"width":55,"height":56},"nvidia-dynamo-vast-scalable-optimized-inference-2.webp",[],{"data":26220,"content":26221,"nodeType":940},{},[26222,26227],{"data":26223,"marks":26224,"value":26226,"nodeType":944},{},[26225],{"type":24041},"Note:",{"data":26228,"marks":26229,"value":26230,"nodeType":944},{},[]," 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":26232,"content":26233,"nodeType":940},{},[26234],{"data":26235,"marks":26236,"value":26237,"nodeType":944},{},[],"As relates to the inference user experience, we observed the following performance: ",{"data":26239,"content":26240,"nodeType":24064},{},[26241,26251,26261,26271],{"data":26242,"content":26243,"nodeType":24068},{},[26244],{"data":26245,"content":26246,"nodeType":940},{},[26247],{"data":26248,"marks":26249,"value":26250,"nodeType":944},{},[],"TTFT with prefill compute: 62 seconds",{"data":26252,"content":26253,"nodeType":24068},{},[26254],{"data":26255,"content":26256,"nodeType":940},{},[26257],{"data":26258,"marks":26259,"value":26260,"nodeType":944},{},[],"TTFT with KV$ load from VAST: 3 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Abstraction",[],{},{"nodeType":940,"data":26636,"content":26637},{},[26638],{"nodeType":944,"value":26639,"marks":26640,"data":26641},"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":940,"data":26643,"content":26644},{},[26645,26650],{"nodeType":944,"value":26646,"marks":26647,"data":26649},"VAST Multi-Protocol Efficiency:",[26648],{"type":24041},{},{"nodeType":944,"value":26651,"marks":26652,"data":26653}," 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. 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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":23519,"data":26697,"content":26698},{},[26699],{"nodeType":944,"value":26700,"marks":26701,"data":26702},"Consistency and Advanced Feature Utilization",[],{},{"nodeType":940,"data":26704,"content":26705},{},[26706],{"nodeType":944,"value":26707,"marks":26708,"data":26709},"Beyond simplifying provisioning, the integration also ensures that advanced VAST features are consistently available through Kubernetes without adding operational overhead.",[],{},{"nodeType":24064,"data":26711,"content":26712},{},[26713,26753],{"nodeType":24068,"data":26714,"content":26715},{},[26716],{"nodeType":940,"data":26717,"content":26718},{},[26719,26724,26728,26737,26740,26749],{"nodeType":944,"value":26720,"marks":26721,"data":26723},"Snapshots and Clones:",[26722],{"type":24041},{},{"nodeType":944,"value":26725,"marks":26726,"data":26727}," VAST’s native storage efficiency and performance features, such as high-performance ",[],{},{"nodeType":6165,"data":26729,"content":26731},{"uri":26730},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-snapshots-to-vasts-element-store",[26732],{"nodeType":944,"value":26733,"marks":26734,"data":26736},"snapshots",[26735],{"type":23577},{},{"nodeType":944,"value":25247,"marks":26738,"data":26739},[],{},{"nodeType":6165,"data":26741,"content":26743},{"uri":26742},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fglobal-snapshot-clones-intelligent-multi-site-data-availability",[26744],{"nodeType":944,"value":26745,"marks":26746,"data":26748},"clones",[26747],{"type":23577},{},{"nodeType":944,"value":26750,"marks":26751,"data":26752},", are exposed directly through the CSI interface. 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Tenants are defined within VAST as isolated domains, each having its own file system hierarchy, unique security policies and performance controls (QoS). ",[],{},{"nodeType":940,"data":26803,"content":26804},{},[26805],{"nodeType":944,"value":26806,"marks":26807,"data":26808},"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":940,"data":26810,"content":26811},{},[26812],{"nodeType":944,"value":26813,"marks":26814,"data":26815},"First, we configure two tenants, differentiated by vippool, and create two corresponding storage classes, each assigned to one of the tenants.",[],{},{"nodeType":940,"data":26817,"content":26818},{},[26819],{"nodeType":944,"value":26820,"marks":26821,"data":26822},"Subsequently, we assign two projects in NVIDIA Run:AI, one for each storage class. This configuration ensures definitive data isolation between the projects.",[],{},{"nodeType":24488,"data":26824,"content":26845},{"target":26825},{"metadata":26826,"sys":26829,"fields":26837},{"tags":26827,"concepts":26828},[],[],{"space":26830,"id":26832,"type":42,"createdAt":26833,"updatedAt":26834,"environment":26835,"publishedVersion":345,"revision":346,"locale":29},{"sys":26831},{"type":12,"linkType":13,"id":14},"6yc2QsKV9sGw5SVVSci6Vs","2025-12-16T14:00:19.048Z","2025-12-17T08:24:05.430Z",{"sys":26836},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":26838},{"url":26839,"details":26840,"fileName":26844,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6yc2QsKV9sGw5SVVSci6Vs\u002F8445177ac0cbc88c3ba92b94d977cbe8\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",{"size":26841,"image":26842},34318,{"width":55,"height":26843},734,"simplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",[],{"nodeType":940,"data":26847,"content":26848},{},[26849],{"nodeType":944,"value":26850,"marks":26851,"data":26853},"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.",[26852],{"type":24736},{},{"nodeType":24488,"data":26855,"content":26875},{"target":26856},{"metadata":26857,"sys":26860,"fields":26868},{"tags":26858,"concepts":26859},[],[],{"space":26861,"id":26863,"type":42,"createdAt":26864,"updatedAt":26865,"environment":26866,"publishedVersion":345,"revision":346,"locale":29},{"sys":26862},{"type":12,"linkType":13,"id":14},"kdNFeI1EdTQVWw3l6H2HJ","2025-12-16T14:01:52.688Z","2025-12-17T08:23:52.260Z",{"sys":26867},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":26869},{"url":26870,"details":26871,"fileName":26874,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FkdNFeI1EdTQVWw3l6H2HJ\u002F5bc2cbf1b6c6a1c9da29b8fcf30a3269\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",{"size":26872,"image":26873},36150,{"width":55,"height":56},"simplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",[],{"nodeType":23519,"data":26877,"content":26878},{},[26879],{"nodeType":944,"value":26880,"marks":26881,"data":26882},"Controlling Tenants’ QoS",[],{},{"nodeType":940,"data":26884,"content":26885},{},[26886],{"nodeType":944,"value":26887,"marks":26888,"data":26889},"Once tenants are defined, the next step is ensuring each project gets the right performance. ",[],{},{"nodeType":940,"data":26891,"content":26892},{},[26893,26896,26905],{"nodeType":944,"value":401,"marks":26894,"data":26895},[],{},{"nodeType":6165,"data":26897,"content":26899},{"uri":26898},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fenforcing-io-discipline-dynamic-qos-model-training",[26900],{"nodeType":944,"value":26901,"marks":26902,"data":26904},"VAST’s QoS policies",[26903],{"type":23577},{},{"nodeType":944,"value":26906,"marks":26907,"data":26908}," 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":940,"data":26910,"content":26911},{},[26912],{"nodeType":944,"value":26913,"marks":26914,"data":26915},"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":940,"data":26917,"content":26918},{},[26919],{"nodeType":944,"value":26920,"marks":26921,"data":26922},"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":23519,"data":26924,"content":26925},{},[26926],{"nodeType":944,"value":26927,"marks":26928,"data":26929},"Multiple Sites \u002F Global Namespace Integration",[],{},{"nodeType":940,"data":26931,"content":26932},{},[26933,26937,26944],{"nodeType":944,"value":26934,"marks":26935,"data":26936},"While we’ve concentrated on local PVC workloads with NVIDIA Run:AI, VAST’s global namespace (",[],{},{"nodeType":6165,"data":26938,"content":26939},{"uri":23666},[26940],{"nodeType":944,"value":16405,"marks":26941,"data":26943},[26942],{"type":23577},{},{"nodeType":944,"value":26945,"marks":26946,"data":26947},") is vital for extending resource utilization to remote sites and enabling advanced Run:AI scenarios. Let’s discuss two scenarios: ",[],{},{"nodeType":24064,"data":26949,"content":26950},{},[26951,26961],{"nodeType":24068,"data":26952,"content":26953},{},[26954],{"nodeType":940,"data":26955,"content":26956},{},[26957],{"nodeType":944,"value":26958,"marks":26959,"data":26960},"Seamless multi-site burst compute for both training and inference",[],{},{"nodeType":24068,"data":26962,"content":26963},{},[26964],{"nodeType":940,"data":26965,"content":26966},{},[26967],{"nodeType":944,"value":26968,"marks":26969,"data":26970},"Resilient LLM Training",[],{},{"nodeType":24488,"data":26972,"content":26992},{"target":26973},{"metadata":26974,"sys":26977,"fields":26984},{"tags":26975,"concepts":26976},[],[],{"space":26978,"id":26980,"type":42,"createdAt":26981,"updatedAt":26981,"environment":26982,"publishedVersion":140,"revision":47,"locale":29},{"sys":26979},{"type":12,"linkType":13,"id":14},"4VJFjVdk0kDHrqMAd5QX8L","2025-12-17T08:23:40.138Z",{"sys":26983},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":26985},{"url":26986,"details":26987,"fileName":26991,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4VJFjVdk0kDHrqMAd5QX8L\u002Fb0e52250dfd10cb5363a5fc25784bb1e\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",{"size":26988,"image":26989},117848,{"width":55,"height":26990},808,"simplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",[],{"nodeType":940,"data":26994,"content":26995},{},[26996,27001,27007,27012,27018],{"nodeType":944,"value":26997,"marks":26998,"data":27000},"Run:AI can dynamically burst the job queue to a ",[26999],{"type":24736},{},{"nodeType":944,"value":27002,"marks":27003,"data":27006},"secondary site (Site B)",[27004,27005],{"type":24736},{"type":24041},{},{"nodeType":944,"value":27008,"marks":27009,"data":27011}," or a ",[27010],{"type":24736},{},{"nodeType":944,"value":27013,"marks":27014,"data":27017},"cloud environment",[27015,27016],{"type":24736},{"type":24041},{},{"nodeType":944,"value":27019,"marks":27020,"data":27022}," where more compute resources are available.",[27021],{"type":24736},{},{"nodeType":23519,"data":27024,"content":27025},{},[27026],{"nodeType":944,"value":27027,"marks":27028,"data":27029},"Burst Compute for Training and Inference",[],{},{"nodeType":940,"data":27031,"content":27032},{},[27033,27037,27045],{"nodeType":944,"value":27034,"marks":27035,"data":27036},"This is a classic use case enabled by the ",[],{},{"nodeType":6165,"data":27038,"content":27040},{"uri":27039},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fglobal-namespace-demo",[27041],{"nodeType":944,"value":27042,"marks":27043,"data":27044},"global, persistent nature",[],{},{"nodeType":944,"value":27046,"marks":27047,"data":27048}," of the VAST DataSpace.",[],{},{"nodeType":940,"data":27050,"content":27051},{},[27052,27057],{"nodeType":944,"value":27053,"marks":27054,"data":27056},"Scenario:",[27055],{"type":24041},{},{"nodeType":944,"value":27058,"marks":27059,"data":27060}," 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":940,"data":27062,"content":27063},{},[27064],{"nodeType":944,"value":27065,"marks":27066,"data":27068},"Solution:",[27067],{"type":24041},{},{"nodeType":25626,"data":27070,"content":27071},{},[27072,27082,27092],{"nodeType":24068,"data":27073,"content":27074},{},[27075],{"nodeType":940,"data":27076,"content":27077},{},[27078],{"nodeType":944,"value":27079,"marks":27080,"data":27081},"A job on Site A writes its training checkpoints to the VAST file system mounted via NFS. ",[],{},{"nodeType":24068,"data":27083,"content":27084},{},[27085],{"nodeType":940,"data":27086,"content":27087},{},[27088],{"nodeType":944,"value":27089,"marks":27090,"data":27091},"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":24068,"data":27093,"content":27094},{},[27095],{"nodeType":940,"data":27096,"content":27097},{},[27098],{"nodeType":944,"value":27099,"marks":27100,"data":27101},"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":24488,"data":27103,"content":27122},{"target":27104},{"metadata":27105,"sys":27108,"fields":27115},{"tags":27106,"concepts":27107},[],[],{"space":27109,"id":27111,"type":42,"createdAt":27112,"updatedAt":27112,"environment":27113,"publishedVersion":140,"revision":47,"locale":29},{"sys":27110},{"type":12,"linkType":13,"id":14},"68urmPYKyxmQ5RH1ZwDewH","2025-12-17T08:26:53.179Z",{"sys":27114},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":27116},{"url":27117,"details":27118,"fileName":27121,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F68urmPYKyxmQ5RH1ZwDewH\u002Fdc8556b00c19ac8043e5539c5a41fe85\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",{"size":27119,"image":27120},41180,{"width":55,"height":56},"simplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",[],{"nodeType":940,"data":27124,"content":27125},{},[27126,27131],{"nodeType":944,"value":27127,"marks":27128,"data":27130},"Benefit:",[27129],{"type":24041},{},{"nodeType":944,"value":27132,"marks":27133,"data":27134}," 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":23519,"data":27136,"content":27137},{},[27138],{"nodeType":944,"value":27139,"marks":27140,"data":27141},"Resilient LLM Training: Checkpoint and Recover in Seconds",[],{},{"nodeType":940,"data":27143,"content":27144},{},[27145],{"nodeType":944,"value":27146,"marks":27147,"data":27148},"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":940,"data":27150,"content":27151},{},[27152,27156,27164],{"nodeType":944,"value":27153,"marks":27154,"data":27155},"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":6165,"data":27157,"content":27159},{"uri":27158},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency",[27160],{"nodeType":944,"value":26368,"marks":27161,"data":27163},[27162],{"type":23577},{},{"nodeType":944,"value":27165,"marks":27166,"data":27167}," to reproduce it.",[],{},{"nodeType":24461,"data":27169,"content":27170},{},[27171],{"nodeType":944,"value":27172,"marks":27173,"data":27174},"The High Cost of Unplanned Interruptions",[],{},{"nodeType":940,"data":27176,"content":27177},{},[27178,27182,27202],{"nodeType":944,"value":27179,"marks":27180,"data":27181},"Failures during training aren’t reserved for hyperscale clusters - they can strike any environment, from a handful of GPUs to multi-region deployments. ",[],{},{"nodeType":6165,"data":27183,"content":27185},{"uri":27184},"https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fthe-llama-3-herd-of-models\u002F",[27186,27191,27197],{"nodeType":944,"value":27187,"marks":27188,"data":27190},"In Meta’s ",[27189],{"type":23577},{},{"nodeType":944,"value":27192,"marks":27193,"data":27196},"Llama 3",[27194,27195],{"type":23577},{"type":24736},{},{"nodeType":944,"value":27198,"marks":27199,"data":27201}," pretraining report",[27200],{"type":23577},{},{"nodeType":944,"value":27203,"marks":27204,"data":27205},", we get a glimpse into just how common these interruptions are:",[],{},{"nodeType":940,"data":27207,"content":27208},{},[27209,27214,27220,27225,27231],{"nodeType":944,"value":27210,"marks":27211,"data":27213},"Over a 54-day window, Meta’s 16,384-GPU cluster recorded ",[27212],{"type":24736},{},{"nodeType":944,"value":27215,"marks":27216,"data":27219},"466 job interruptions",[27217,27218],{"type":24736},{"type":24041},{},{"nodeType":944,"value":27221,"marks":27222,"data":27224},", 90% of which were unplanned. GPU hardware alone caused ",[27223],{"type":24736},{},{"nodeType":944,"value":27226,"marks":27227,"data":27230},"59%",[27228,27229],{"type":24736},{"type":24041},{},{"nodeType":944,"value":27232,"marks":27233,"data":27235}," of those failures.",[27234],{"type":24736},{},{"nodeType":940,"data":27237,"content":27238},{},[27239],{"nodeType":944,"value":27240,"marks":27241,"data":27242},"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":24461,"data":27244,"content":27245},{},[27246],{"nodeType":944,"value":27247,"marks":27248,"data":27249},"Why Checkpointing Matters",[],{},{"nodeType":940,"data":27251,"content":27252},{},[27253,27256,27265],{"nodeType":944,"value":401,"marks":27254,"data":27255},[],{},{"nodeType":6165,"data":27257,"content":27259},{"uri":27258},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fa-checkpoint-on-checkpoints-in-llms",[27260],{"nodeType":944,"value":27261,"marks":27262,"data":27264},"Checkpointing",[27263],{"type":23577},{},{"nodeType":944,"value":27266,"marks":27267,"data":27268}," - 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":940,"data":27270,"content":27271},{},[27272,27275,27284],{"nodeType":944,"value":401,"marks":27273,"data":27274},[],{},{"nodeType":6165,"data":27276,"content":27278},{"uri":27277},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Foptimizing-checkpoint-bandwidth-for-llm-training",[27279],{"nodeType":944,"value":27280,"marks":27281,"data":27283},"Checkpoint strategies",[27282],{"type":23577},{},{"nodeType":944,"value":27285,"marks":27286,"data":27287}," 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":24461,"data":27289,"content":27290},{},[27291],{"nodeType":944,"value":27292,"marks":27293,"data":27294},"The Storage Dilemma",[],{},{"nodeType":940,"data":27296,"content":27297},{},[27298],{"nodeType":944,"value":27299,"marks":27300,"data":27301},"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":940,"data":27303,"content":27304},{},[27305,27309,27317,27321,27330],{"nodeType":944,"value":27306,"marks":27307,"data":27308},"VAST bridges these gaps. VAST’s ",[],{},{"nodeType":6165,"data":27310,"content":27311},{"uri":23633},[27312],{"nodeType":944,"value":27313,"marks":27314,"data":27316},"disaggregated architecture",[27315],{"type":23577},{},{"nodeType":944,"value":27318,"marks":27319,"data":27320}," aggregates NVMe drives across an entire cluster into a single global namespace, providing parallel I\u002FO at tens of gigabytes per second plus ",[],{},{"nodeType":6165,"data":27322,"content":27324},{"uri":27323},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[27325],{"nodeType":944,"value":27326,"marks":27327,"data":27329},"built‑in erasure coding",[27328],{"type":23577},{},{"nodeType":944,"value":27331,"marks":27332,"data":27333}," for data durability.",[],{},{"nodeType":24461,"data":27335,"content":27336},{},[27337],{"nodeType":944,"value":27338,"marks":27339,"data":27340},"Orchestration with NVIDIA Run:AI",[],{},{"nodeType":940,"data":27342,"content":27343},{},[27344],{"nodeType":944,"value":27345,"marks":27346,"data":27347},"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":940,"data":27349,"content":27350},{},[27351],{"nodeType":944,"value":27352,"marks":27353,"data":27354},"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":23519,"data":27356,"content":27357},{},[27358],{"nodeType":944,"value":27359,"marks":27360,"data":27361},"Fast, Cross‑Region Recovery in Action",[],{},{"nodeType":940,"data":27363,"content":27364},{},[27365],{"nodeType":944,"value":27366,"marks":27367,"data":27368},"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":940,"data":27370,"content":27371},{},[27372],{"nodeType":944,"value":27373,"marks":27374,"data":27375},"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":940,"data":27377,"content":27378},{},[27379],{"nodeType":944,"value":27380,"marks":27381,"data":27382},"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":940,"data":27384,"content":27385},{},[27386],{"nodeType":944,"value":27387,"marks":27388,"data":27389},"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":940,"data":27391,"content":27392},{},[27393],{"nodeType":944,"value":27394,"marks":27395,"data":27397},"Putting it into Practice: A Step-by-Step Job Submission",[27396],{"type":24041},{},{"nodeType":940,"data":27399,"content":27400},{},[27401,27405,27412],{"nodeType":944,"value":27402,"marks":27403,"data":27404},"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":6165,"data":27406,"content":27407},{"uri":27158},[27408],{"nodeType":944,"value":26368,"marks":27409,"data":27411},[27410],{"type":23577},{},{"nodeType":944,"value":6169,"marks":27413,"data":27414},[],{},{"nodeType":940,"data":27416,"content":27417},{},[27418],{"nodeType":944,"value":27419,"marks":27420,"data":27422},"Step 1: Creating a PVC with VAST CSI filesystem",[27421],{"type":24041},{},{"nodeType":940,"data":27424,"content":27425},{},[27426],{"nodeType":944,"value":27427,"marks":27428,"data":27429},"Let’s create a PVC with VAST CSI filesystem and mount it to \u002Fmodel. ",[],{},{"nodeType":24488,"data":27431,"content":27450},{"target":27432},{"metadata":27433,"sys":27436,"fields":27443},{"tags":27434,"concepts":27435},[],[],{"space":27437,"id":27439,"type":42,"createdAt":27440,"updatedAt":27440,"environment":27441,"publishedVersion":140,"revision":47,"locale":29},{"sys":27438},{"type":12,"linkType":13,"id":14},"77QPXz8kJ5suw2HsXf7PIO","2025-12-17T08:28:42.070Z",{"sys":27442},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":27444},{"url":27445,"details":27446,"fileName":27449,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F77QPXz8kJ5suw2HsXf7PIO\u002F87bbdebb1642d12787d37b08e1feff92\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",{"size":27447,"image":27448},51050,{"width":55,"height":56},"simplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",[],{"nodeType":940,"data":27452,"content":27453},{},[27454],{"nodeType":944,"value":27455,"marks":27456,"data":27457},"Create a new training workload job with pytorch-one-gpu:",[],{},{"nodeType":24488,"data":27459,"content":27479},{"target":27460},{"metadata":27461,"sys":27464,"fields":27471},{"tags":27462,"concepts":27463},[],[],{"space":27465,"id":27467,"type":42,"createdAt":27468,"updatedAt":27468,"environment":27469,"publishedVersion":140,"revision":47,"locale":29},{"sys":27466},{"type":12,"linkType":13,"id":14},"41lMeyZPrg1hwFMqvUlEdp","2025-12-17T08:30:29.308Z",{"sys":27470},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":27472},{"url":27473,"details":27474,"fileName":27478,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F41lMeyZPrg1hwFMqvUlEdp\u002F8ed7d59c7fd81eff3525165efcfe2e2d\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",{"size":27475,"image":27476},12608,{"width":55,"height":27477},521,"simplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",[],{"nodeType":940,"data":27481,"content":27482},{},[27483,27487,27496],{"nodeType":944,"value":27484,"marks":27485,"data":27486},"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":6165,"data":27488,"content":27490},{"uri":27489},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency\u002Fblob\u002Fmain\u002Fmodel-loading\u002Fclone.sh",[27491],{"nodeType":944,"value":27492,"marks":27493,"data":27495},"in this repository",[27494],{"type":23577},{},{"nodeType":944,"value":27497,"marks":27498,"data":27499}," 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":940,"data":27501,"content":27502},{},[27503],{"nodeType":944,"value":27504,"marks":27505,"data":27506},"After this step completes, your VAST volume contains:",[],{},{"nodeType":24064,"data":27508,"content":27509},{},[27510,27520],{"nodeType":24068,"data":27511,"content":27512},{},[27513],{"nodeType":940,"data":27514,"content":27515},{},[27516],{"nodeType":944,"value":27517,"marks":27518,"data":27519},"\u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct - The base model (one-time download)",[],{},{"nodeType":24068,"data":27521,"content":27522},{},[27523],{"nodeType":940,"data":27524,"content":27525},{},[27526],{"nodeType":944,"value":27527,"marks":27528,"data":27529},"\u002Fmodel\u002Fcheckpoints\u002F - Empty directory ready for checkpoints",[],{},{"nodeType":940,"data":27531,"content":27532},{},[27533],{"nodeType":944,"value":27534,"marks":27535,"data":27537},"Step 2: Creating Training Script ",[27536],{"type":24041},{},{"nodeType":940,"data":27539,"content":27540},{},[27541],{"nodeType":944,"value":27542,"marks":27543,"data":27544},"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":24064,"data":27546,"content":27547},{},[27548,27563,27578,27593],{"nodeType":24068,"data":27549,"content":27550},{},[27551],{"nodeType":940,"data":27552,"content":27553},{},[27554,27559],{"nodeType":944,"value":27555,"marks":27556,"data":27558},"Checkpoint frequency: ",[27557],{"type":24041},{},{"nodeType":944,"value":27560,"marks":27561,"data":27562},"Saves every 5 training steps to \u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F",[],{},{"nodeType":24068,"data":27564,"content":27565},{},[27566],{"nodeType":940,"data":27567,"content":27568},{},[27569,27574],{"nodeType":944,"value":27570,"marks":27571,"data":27573},"Automatic resume logic: ",[27572],{"type":24041},{},{"nodeType":944,"value":27575,"marks":27576,"data":27577},"On startup, the script searches for existing checkpoints and automatically resumes from the latest one",[],{},{"nodeType":24068,"data":27579,"content":27580},{},[27581],{"nodeType":940,"data":27582,"content":27583},{},[27584,27589],{"nodeType":944,"value":27585,"marks":27586,"data":27588},"LoRa fine-tuning:",[27587],{"type":24041},{},{"nodeType":944,"value":27590,"marks":27591,"data":27592}," Uses parameter-efficient LoRa adapters to fine-tune Llama 3.1 with minimal memory overhead",[],{},{"nodeType":24068,"data":27594,"content":27595},{},[27596],{"nodeType":940,"data":27597,"content":27598},{},[27599,27604],{"nodeType":944,"value":27600,"marks":27601,"data":27603},"Distributed training ready:",[27602],{"type":24041},{},{"nodeType":944,"value":27605,"marks":27606,"data":27607}," Leverages torchrun for multi-GPU training via the launch.sh entrypoint",[],{},{"nodeType":940,"data":27609,"content":27610},{},[27611],{"nodeType":944,"value":27612,"marks":27613,"data":27615},"Step 3: Build the Training Image (or Use Ours)",[27614],{"type":24041},{},{"nodeType":940,"data":27617,"content":27618},{},[27619],{"nodeType":944,"value":27620,"marks":27621,"data":27622},"The training\u002F directory includes a Dockerfile that packages everything needed. ",[],{},{"nodeType":940,"data":27624,"content":27625},{},[27626],{"nodeType":944,"value":27627,"marks":27628,"data":27629},"If you want to build your own image:",[],{},{"nodeType":940,"data":27631,"content":27632},{},[27633],{"nodeType":944,"value":27634,"marks":27635,"data":27638},"cd training \ndocker build --platform linux\u002Famd64 -t \u003CYOUR_REGISTRY>\u002Frunai-vast-training\ndocker push \u003CYOUR_REGISTRY>\u002Frunai-vast-training",[27636],{"type":27637},"code",{},{"nodeType":940,"data":27640,"content":27641},{},[27642,27646,27655],{"nodeType":944,"value":27643,"marks":27644,"data":27645},"Alternatively, you can use ",[],{},{"nodeType":6165,"data":27647,"content":27649},{"uri":27648},"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fekarabulut844\u002Frunai-vast-training",[27650],{"nodeType":944,"value":27651,"marks":27652,"data":27654},"our pre-built Docker image",[27653],{"type":23577},{},{"nodeType":944,"value":27656,"marks":27657,"data":27658}," that’s ready to go. ",[],{},{"nodeType":940,"data":27660,"content":27661},{},[27662],{"nodeType":944,"value":27663,"marks":27664,"data":27666},"Step 4: Launch the Training Job",[27665],{"type":24041},{},{"nodeType":940,"data":27668,"content":27669},{},[27670],{"nodeType":944,"value":27671,"marks":27672,"data":27673},"With the model loaded on VAST and the training image ready, submit your training job via Run:AI:",[],{},{"nodeType":940,"data":27675,"content":27676},{},[27677],{"nodeType":944,"value":27678,"marks":27679,"data":27681},"runai training pytorch submit lora-fine-tune-model --existing-pvc \"claimname=$DATA,path=\u002Fmodel\" -i docker.io\u002F\u003CREGISTRY>\u002Frunai-vast-training -g 1",[27680],{"type":27637},{},{"nodeType":940,"data":27683,"content":27684},{},[27685],{"nodeType":944,"value":27686,"marks":27687,"data":27688},"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":24488,"data":27690,"content":27710},{"target":27691},{"metadata":27692,"sys":27695,"fields":27702},{"tags":27693,"concepts":27694},[],[],{"space":27696,"id":27698,"type":42,"createdAt":27699,"updatedAt":27699,"environment":27700,"publishedVersion":140,"revision":47,"locale":29},{"sys":27697},{"type":12,"linkType":13,"id":14},"qqhA5Y8rP0XN94LDN7MeA","2025-12-17T08:38:12.304Z",{"sys":27701},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":27703},{"url":27704,"details":27705,"fileName":27709,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FqqhA5Y8rP0XN94LDN7MeA\u002F5f56d596378a69b3341bfeea28ae4fbe\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",{"size":27706,"image":27707},204520,{"width":55,"height":27708},972,"simplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",[],{"nodeType":940,"data":27712,"content":27713},{},[27714],{"nodeType":944,"value":27715,"marks":27716,"data":27717},"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":940,"data":27719,"content":27720},{},[27721,27726,27732],{"nodeType":944,"value":27722,"marks":27723,"data":27725},"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",[27724],{"type":27637},{},{"nodeType":944,"value":27727,"marks":27728,"data":27731},"drwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:18 checkpoint-40\n",[27729,27730],{"type":24041},{"type":27637},{},{"nodeType":944,"value":27733,"marks":27734,"data":27736},"-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",[27735],{"type":27637},{},{"nodeType":940,"data":27738,"content":27739},{},[27740],{"nodeType":944,"value":27741,"marks":27742,"data":27743},"… by creating an inference workload with the following arguments:",[],{},{"nodeType":940,"data":27745,"content":27746},{},[27747],{"nodeType":944,"value":27748,"marks":27749,"data":27751},"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",[27750],{"type":27637},{},{"nodeType":24488,"data":27753,"content":27773},{"target":27754},{"metadata":27755,"sys":27758,"fields":27765},{"tags":27756,"concepts":27757},[],[],{"space":27759,"id":27761,"type":42,"createdAt":27762,"updatedAt":27762,"environment":27763,"publishedVersion":140,"revision":47,"locale":29},{"sys":27760},{"type":12,"linkType":13,"id":14},"38SHUNOFYtJfSLk5DvLWJm","2025-12-17T08:41:04.447Z",{"sys":27764},{"id":21,"type":12,"linkType":22},{"title":26562,"description":26562,"file":27766},{"url":27767,"details":27768,"fileName":27772,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F38SHUNOFYtJfSLk5DvLWJm\u002F145deaae37713e46c143c7bc48b78aba\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",{"size":27769,"image":27770},18250,{"width":55,"height":27771},341,"simplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",[],{"nodeType":23519,"data":27775,"content":27776},{},[27777],{"nodeType":944,"value":27778,"marks":27779,"data":27780},"Final Thoughts",[],{},{"nodeType":940,"data":27782,"content":27783},{},[27784,27787,27796],{"nodeType":944,"value":401,"marks":27785,"data":27786},[],{},{"nodeType":6165,"data":27788,"content":27790},{"uri":27789},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-rack-level-resilience-to-vast-part-one",[27791],{"nodeType":944,"value":27792,"marks":27793,"data":27795},"Resilience isn’t optional",[27794],{"type":23577},{},{"nodeType":944,"value":27797,"marks":27798,"data":27799}," 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":940,"data":27801,"content":27802},{},[27803],{"nodeType":944,"value":27804,"marks":27805,"data":27806},"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.",[28062],{"type":24736},{},{"nodeType":25290,"data":28065,"content":28066},{},[],{"nodeType":940,"data":28068,"content":28069},{},[28070],{"nodeType":944,"value":28071,"marks":28072,"data":28073},"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":940,"data":28075,"content":28076},{},[28077,28081],{"nodeType":944,"value":28078,"marks":28079,"data":28080},"The future isn’t one hyper-intelligent agent; it’s a swarm of millions. Why? ",[],{},{"nodeType":944,"value":28082,"marks":28083,"data":28085},"Specialization.",[28084],{"type":24041},{},{"nodeType":940,"data":28087,"content":28088},{},[28089,28093,28098],{"nodeType":944,"value":28090,"marks":28091,"data":28092},"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":944,"value":28094,"marks":28095,"data":28097},"team of specialized agents",[28096],{"type":24736},{},{"nodeType":944,"value":28099,"marks":28100,"data":28101}," —a PlannerAgent, a ResearchAgent, a DataAnalystAgent, a SecurityMonitorAgent, and a NotificationAgent — all working in concert.",[],{},{"nodeType":940,"data":28103,"content":28104},{},[28105],{"nodeType":944,"value":28106,"marks":28107,"data":28108},"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":940,"data":28110,"content":28111},{},[28112,28116,28125,28129],{"nodeType":944,"value":28113,"marks":28114,"data":28115},"However, this also creates an unprecedented infrastructure problem. A swarm of millions of autonomous agents, all reasoning, communicating, and accessing data simultaneously, ",[],{},{"nodeType":6165,"data":28117,"content":28119},{"uri":28118},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbuilding-enterprise-infrastructure-for-ai-agents",[28120],{"nodeType":944,"value":28121,"marks":28122,"data":28124},"cannot run on today’s siloed architecture",[28123],{"type":24041},{},{"nodeType":944,"value":6169,"marks":28126,"data":28128},[28127],{"type":24041},{},{"nodeType":944,"value":28130,"marks":28131,"data":28132}," It’s an infrastructure nightmare, creating problems of cost, security, and complexity.",[],{},{"nodeType":23519,"data":28134,"content":28135},{},[28136],{"nodeType":944,"value":28137,"marks":28138,"data":28139},"The Bottleneck: The Environment vs. The Swarm",[],{},{"nodeType":940,"data":28141,"content":28142},{},[28143],{"nodeType":944,"value":28144,"marks":28145,"data":28146},"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":940,"data":28148,"content":28149},{},[28150],{"nodeType":944,"value":28151,"marks":28152,"data":28153},"This creates critical bottlenecks that make swarm deployment challenging:",[],{},{"nodeType":24064,"data":28155,"content":28156},{},[28157,28172,28187,28202],{"nodeType":24068,"data":28158,"content":28159},{},[28160],{"nodeType":940,"data":28161,"content":28162},{},[28163,28168],{"nodeType":944,"value":28164,"marks":28165,"data":28167},"The coupled scaling problem:",[28166],{"type":24041},{},{"nodeType":944,"value":28169,"marks":28170,"data":28171}," 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":24068,"data":28173,"content":28174},{},[28175],{"nodeType":940,"data":28176,"content":28177},{},[28178,28183],{"nodeType":944,"value":28179,"marks":28180,"data":28182},"The state and amnesia problem:",[28181],{"type":24041},{},{"nodeType":944,"value":28184,"marks":28185,"data":28186}," 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":24068,"data":28188,"content":28189},{},[28190],{"nodeType":940,"data":28191,"content":28192},{},[28193,28198],{"nodeType":944,"value":28194,"marks":28195,"data":28197},"The communication bottleneck:",[28196],{"type":24041},{},{"nodeType":944,"value":28199,"marks":28200,"data":28201}," 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":24068,"data":28203,"content":28204},{},[28205],{"nodeType":940,"data":28206,"content":28207},{},[28208,28213,28217,28222],{"nodeType":944,"value":28209,"marks":28210,"data":28212},"The security and identity silo:",[28211],{"type":24041},{},{"nodeType":944,"value":28214,"marks":28215,"data":28216}," In a monolithic setup, each agent is an island. It has to manage its ",[],{},{"nodeType":944,"value":28218,"marks":28219,"data":28221},"own",[28220],{"type":24736},{},{"nodeType":944,"value":28223,"marks":28224,"data":28225}," 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":940,"data":28227,"content":28228},{},[28229],{"nodeType":944,"value":28230,"marks":28231,"data":28232},"These aren’t agent failures; they’re platform gaps - the absence of infrastructure primitives purpose-built for multi-agent systems.",[],{},{"nodeType":23519,"data":28234,"content":28235},{},[28236],{"nodeType":944,"value":28237,"marks":28238,"data":28239},"A Shared, Decoupled Foundation for Running AI Agents",[],{},{"nodeType":940,"data":28241,"content":28242},{},[28243],{"nodeType":944,"value":28244,"marks":28245,"data":28246},"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":940,"data":28248,"content":28249},{},[28250,28254,28259],{"nodeType":944,"value":28251,"marks":28252,"data":28253},"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":944,"value":28255,"marks":28256,"data":28258},"separate functions",[28257],{"type":24736},{},{"nodeType":944,"value":28260,"marks":28261,"data":28262}," 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":940,"data":28264,"content":28265},{},[28266,28270,28275],{"nodeType":944,"value":28267,"marks":28268,"data":28269},"The primary advantage of this disaggregated architecture is the ability to scale ",[],{},{"nodeType":944,"value":28271,"marks":28272,"data":28274},"only",[28273],{"type":24736},{},{"nodeType":944,"value":28276,"marks":28277,"data":28278}," the component that is the bottleneck.",[],{},{"nodeType":24064,"data":28280,"content":28281},{},[28282,28297,28320,28335],{"nodeType":24068,"data":28283,"content":28284},{},[28285],{"nodeType":940,"data":28286,"content":28287},{},[28288,28293],{"nodeType":944,"value":28289,"marks":28290,"data":28292},"Scaling CPU (kernels)",[28291],{"type":24041},{},{"nodeType":944,"value":28294,"marks":28295,"data":28296},": If 10,000 tasks arrive, the platform spins up 10,000 cheap, stateless Agent Kernels (on VAST C-Nodes).",[],{},{"nodeType":24068,"data":28298,"content":28299},{},[28300],{"nodeType":940,"data":28301,"content":28302},{},[28303,28308,28312,28316],{"nodeType":944,"value":28304,"marks":28305,"data":28307},"Scaling GPU (reasoning)",[28306],{"type":24041},{},{"nodeType":944,"value":28309,"marks":28310,"data":28311},": If those agents create 100,000 reasoning requests, ",[],{},{"nodeType":944,"value":28271,"marks":28313,"data":28315},[28314],{"type":24736},{},{"nodeType":944,"value":28317,"marks":28318,"data":28319}," the GPU servers are scaled, backed by the VAST KVCache to maximize GPU utility.",[],{},{"nodeType":24068,"data":28321,"content":28322},{},[28323],{"nodeType":940,"data":28324,"content":28325},{},[28326,28331],{"nodeType":944,"value":28327,"marks":28328,"data":28330},"Scaling storage (memory)",[28329],{"type":24041},{},{"nodeType":944,"value":28332,"marks":28333,"data":28334},": 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":24068,"data":28336,"content":28337},{},[28338],{"nodeType":940,"data":28339,"content":28340},{},[28341,28346],{"nodeType":944,"value":28342,"marks":28343,"data":28345},"Scaling I\u002FO (comms)",[28344],{"type":24041},{},{"nodeType":944,"value":28347,"marks":28348,"data":28349},": 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":24488,"data":28351,"content":28369},{"target":28352},{"metadata":28353,"sys":28356,"fields":28363},{"tags":28354,"concepts":28355},[],[],{"space":28357,"id":28359,"type":42,"createdAt":28360,"updatedAt":28360,"environment":28361,"publishedVersion":140,"revision":47,"locale":29},{"sys":28358},{"type":12,"linkType":13,"id":14},"1O2NCk6JSuSs1UXnguRpiS","2025-11-19T14:14:44.267Z",{"sys":28362},{"id":21,"type":12,"linkType":22},{"title":28016,"description":28016,"file":28364},{"url":28365,"details":28366,"fileName":28052,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1O2NCk6JSuSs1UXnguRpiS\u002Fa2a722a7966a4271d9fc7ff17f7fa313\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"size":28367,"image":28368},159002,{"width":55,"height":56},[],{"nodeType":23519,"data":28371,"content":28372},{},[28373],{"nodeType":944,"value":28374,"marks":28375,"data":28376},"Architectural and Engineering Deep Dive: Pillars of a Scalable Agentic Platform",[],{},{"nodeType":940,"data":28378,"content":28379},{},[28380],{"nodeType":944,"value":28381,"marks":28382,"data":28383},"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":24461,"data":28385,"content":28386},{},[28387],{"nodeType":944,"value":28388,"marks":28389,"data":28390},"1. The Execution Model: Stateless Compute with Shared State Access",[],{},{"nodeType":940,"data":28392,"content":28393},{},[28394],{"nodeType":944,"value":28395,"marks":28396,"data":28397},"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":940,"data":28399,"content":28400},{},[28401],{"nodeType":944,"value":28402,"marks":28403,"data":28405},"Agent Kernels as Ephemeral Functions",[28404],{"type":24041},{},{"nodeType":940,"data":28407,"content":28408},{},[28409],{"nodeType":944,"value":28410,"marks":28411,"data":28412},"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":24064,"data":28414,"content":28415},{},[28416,28426,28436,28446],{"nodeType":24068,"data":28417,"content":28418},{},[28419],{"nodeType":940,"data":28420,"content":28421},{},[28422],{"nodeType":944,"value":28423,"marks":28424,"data":28425},"Execute in response to events (e.g., file writes, object creation, table mutations)",[],{},{"nodeType":24068,"data":28427,"content":28428},{},[28429],{"nodeType":940,"data":28430,"content":28431},{},[28432],{"nodeType":944,"value":28433,"marks":28434,"data":28435},"Have direct NVMe access to the underlying storage fabric",[],{},{"nodeType":24068,"data":28437,"content":28438},{},[28439],{"nodeType":940,"data":28440,"content":28441},{},[28442],{"nodeType":944,"value":28443,"marks":28444,"data":28445},"Scale horizontally without coordination, so spawning 10,000 agent kernels doesn’t require inter-kernel communication",[],{},{"nodeType":24068,"data":28447,"content":28448},{},[28449],{"nodeType":940,"data":28450,"content":28451},{},[28452],{"nodeType":944,"value":28453,"marks":28454,"data":28455},"Die after execution; state persistence is the storage layer’s responsibility, not the kernel’s",[],{},{"nodeType":940,"data":28457,"content":28458},{},[28459],{"nodeType":944,"value":28460,"marks":28461,"data":28462},"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":940,"data":28464,"content":28465},{},[28466],{"nodeType":944,"value":28467,"marks":28468,"data":28470},"The Data Locality Advantage",[28469],{"type":24041},{},{"nodeType":940,"data":28472,"content":28473},{},[28474],{"nodeType":944,"value":28475,"marks":28476,"data":28477},"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":940,"data":28479,"content":28480},{},[28481],{"nodeType":944,"value":28482,"marks":28483,"data":28485},"Execution Triggers and Lifecycle",[28484],{"type":24041},{},{"nodeType":940,"data":28487,"content":28488},{},[28489],{"nodeType":944,"value":28490,"marks":28491,"data":28492},"Agent kernel triggers can be:",[],{},{"nodeType":25626,"data":28494,"content":28495},{},[28496,28511,28526],{"nodeType":24068,"data":28497,"content":28498},{},[28499],{"nodeType":940,"data":28500,"content":28501},{},[28502,28507],{"nodeType":944,"value":28503,"marks":28504,"data":28506},"Event-driven",[28505],{"type":24041},{},{"nodeType":944,"value":28508,"marks":28509,"data":28510},": File write, object creation, table insert → kernel spawns automatically",[],{},{"nodeType":24068,"data":28512,"content":28513},{},[28514],{"nodeType":940,"data":28515,"content":28516},{},[28517,28522],{"nodeType":944,"value":28518,"marks":28519,"data":28521},"API-driven",[28520],{"type":24041},{},{"nodeType":944,"value":28523,"marks":28524,"data":28525},": External system calls an endpoint → kernel executes on-demand",[],{},{"nodeType":24068,"data":28527,"content":28528},{},[28529],{"nodeType":940,"data":28530,"content":28531},{},[28532,28537],{"nodeType":944,"value":28533,"marks":28534,"data":28536},"Scheduled",[28535],{"type":24041},{},{"nodeType":944,"value":28538,"marks":28539,"data":28540},": Cron-like triggers for periodic agent tasks",[],{},{"nodeType":940,"data":28542,"content":28543},{},[28544],{"nodeType":944,"value":28545,"marks":28546,"data":28548},"When is it Important to Write Agents Output Storage:",[28547],{"type":24041},{},{"nodeType":940,"data":28550,"content":28551},{},[28552],{"nodeType":944,"value":28553,"marks":28554,"data":28555},"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":940,"data":28557,"content":28558},{},[28559],{"nodeType":944,"value":28560,"marks":28561,"data":28562},"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":940,"data":28564,"content":28565},{},[28566],{"nodeType":944,"value":28567,"marks":28568,"data":28569},"Write everything to persistent storage when:",[],{},{"nodeType":24064,"data":28571,"content":28572},{},[28573,28583,28593,28603],{"nodeType":24068,"data":28574,"content":28575},{},[28576],{"nodeType":940,"data":28577,"content":28578},{},[28579],{"nodeType":944,"value":28580,"marks":28581,"data":28582},"Regulatory or compliance requirements mandate traceability (finance, healthcare)",[],{},{"nodeType":24068,"data":28584,"content":28585},{},[28586],{"nodeType":940,"data":28587,"content":28588},{},[28589],{"nodeType":944,"value":28590,"marks":28591,"data":28592},"System reliability is paramount",[],{},{"nodeType":24068,"data":28594,"content":28595},{},[28596],{"nodeType":940,"data":28597,"content":28598},{},[28599],{"nodeType":944,"value":28600,"marks":28601,"data":28602},"Agents work on long-horizon tasks",[],{},{"nodeType":24068,"data":28604,"content":28605},{},[28606],{"nodeType":940,"data":28607,"content":28608},{},[28609],{"nodeType":944,"value":28610,"marks":28611,"data":28612},"Agent interactions should be inspectable, replayable, or learning-enabled for future deployments.",[],{},{"nodeType":24461,"data":28614,"content":28615},{},[28616],{"nodeType":944,"value":28617,"marks":28618,"data":28619},"2. Event-Driven Coordination: Communication Through the Environment",[],{},{"nodeType":940,"data":28621,"content":28622},{},[28623],{"nodeType":944,"value":28624,"marks":28625,"data":28626},"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":940,"data":28628,"content":28629},{},[28630],{"nodeType":944,"value":28631,"marks":28632,"data":28633},"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":940,"data":28635,"content":28636},{},[28637],{"nodeType":944,"value":28638,"marks":28639,"data":28641},"Environment-Driven Agent Triggers",[28640],{"type":24041},{},{"nodeType":940,"data":28643,"content":28644},{},[28645],{"nodeType":944,"value":28646,"marks":28647,"data":28648},"Agents register themselves to be triggered by specific environmental changes:",[],{},{"nodeType":24064,"data":28650,"content":28651},{},[28652,28667,28682,28697],{"nodeType":24068,"data":28653,"content":28654},{},[28655],{"nodeType":940,"data":28656,"content":28657},{},[28658,28663],{"nodeType":944,"value":28659,"marks":28660,"data":28662},"File system events",[28661],{"type":24041},{},{"nodeType":944,"value":28664,"marks":28665,"data":28666},": An agent triggers when a file matching \u002Fvast\u002Fdatasets\u002Fsales_*.parquet is created",[],{},{"nodeType":24068,"data":28668,"content":28669},{},[28670],{"nodeType":940,"data":28671,"content":28672},{},[28673,28678],{"nodeType":944,"value":28674,"marks":28675,"data":28677},"Database mutations",[28676],{"type":24041},{},{"nodeType":944,"value":28679,"marks":28680,"data":28681},": An agent triggers when a new row is inserted into the pending_tasks table",[],{},{"nodeType":24068,"data":28683,"content":28684},{},[28685],{"nodeType":940,"data":28686,"content":28687},{},[28688,28693],{"nodeType":944,"value":28689,"marks":28690,"data":28692},"Vector updates",[28691],{"type":24041},{},{"nodeType":944,"value":28694,"marks":28695,"data":28696},": An agent triggers when new embeddings are added to the knowledge base",[],{},{"nodeType":24068,"data":28698,"content":28699},{},[28700],{"nodeType":940,"data":28701,"content":28702},{},[28703,28708],{"nodeType":944,"value":28704,"marks":28705,"data":28707},"Time-based events",[28706],{"type":24041},{},{"nodeType":944,"value":28709,"marks":28710,"data":28711},": An agent triggers on a schedule to perform periodic analysis",[],{},{"nodeType":940,"data":28713,"content":28714},{},[28715],{"nodeType":944,"value":28716,"marks":28717,"data":28718},"Here’s an example workflow:",[],{},{"nodeType":25626,"data":28720,"content":28721},{},[28722,28732,28742,28752,28762,28772],{"nodeType":24068,"data":28723,"content":28724},{},[28725],{"nodeType":940,"data":28726,"content":28727},{},[28728],{"nodeType":944,"value":28729,"marks":28730,"data":28731},"DataCollectorAgent writes a new file: \u002Fvast\u002Fdatasets\u002Fsales_2025_q3.parquet",[],{},{"nodeType":24068,"data":28733,"content":28734},{},[28735],{"nodeType":940,"data":28736,"content":28737},{},[28738],{"nodeType":944,"value":28739,"marks":28740,"data":28741},"The file write generates a file.created event on the Event Broker",[],{},{"nodeType":24068,"data":28743,"content":28744},{},[28745],{"nodeType":940,"data":28746,"content":28747},{},[28748],{"nodeType":944,"value":28749,"marks":28750,"data":28751},"DataValidatorAgent, registered to trigger on file.created events in \u002Fvast\u002Fdatasets\u002F, automatically spawns",[],{},{"nodeType":24068,"data":28753,"content":28754},{},[28755],{"nodeType":940,"data":28756,"content":28757},{},[28758],{"nodeType":944,"value":28759,"marks":28760,"data":28761},"DataValidatorAgent reads the file, validates it, and updates a database table with status = “validated”",[],{},{"nodeType":24068,"data":28763,"content":28764},{},[28765],{"nodeType":940,"data":28766,"content":28767},{},[28768],{"nodeType":944,"value":28769,"marks":28770,"data":28771},"The table update generates a table.updated event",[],{},{"nodeType":24068,"data":28773,"content":28774},{},[28775],{"nodeType":940,"data":28776,"content":28777},{},[28778],{"nodeType":944,"value":28779,"marks":28780,"data":28781},"AnalysisPipelineAgent, registered to trigger on validation status changes, automatically spawns",[],{},{"nodeType":940,"data":28783,"content":28784},{},[28785],{"nodeType":944,"value":28786,"marks":28787,"data":28788},"No agent explicitly called another agent. They communicated through changes to the shared environment, with the Event Broker mediating the triggers.",[],{},{"nodeType":940,"data":28790,"content":28791},{},[28792],{"nodeType":944,"value":28793,"marks":28794,"data":28796},"Why This Model Scales",[28795],{"type":24041},{},{"nodeType":940,"data":28798,"content":28799},{},[28800],{"nodeType":944,"value":28801,"marks":28802,"data":28803},"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":940,"data":28805,"content":28806},{},[28807],{"nodeType":944,"value":28808,"marks":28809,"data":28810},"Environment-driven communication decouples agents completely:",[],{},{"nodeType":24064,"data":28812,"content":28813},{},[28814,28824,28834],{"nodeType":24068,"data":28815,"content":28816},{},[28817],{"nodeType":940,"data":28818,"content":28819},{},[28820],{"nodeType":944,"value":28821,"marks":28822,"data":28823},"Agents don’t need to know which other agents exist",[],{},{"nodeType":24068,"data":28825,"content":28826},{},[28827],{"nodeType":940,"data":28828,"content":28829},{},[28830],{"nodeType":944,"value":28831,"marks":28832,"data":28833},"New agents can be added without modifying existing agents",[],{},{"nodeType":24068,"data":28835,"content":28836},{},[28837],{"nodeType":940,"data":28838,"content":28839},{},[28840],{"nodeType":944,"value":28841,"marks":28842,"data":28843},"Failed agents don’t cause cascading failures—their work simply remains in the environment until they recover",[],{},{"nodeType":940,"data":28845,"content":28846},{},[28847,28850,28857],{"nodeType":944,"value":401,"marks":28848,"data":28849},[],{},{"nodeType":6165,"data":28851,"content":28852},{"uri":23712},[28853],{"nodeType":944,"value":28854,"marks":28855,"data":28856},"VAST’s Event Broker",[],{},{"nodeType":944,"value":28858,"marks":28859,"data":28860}," 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":24064,"data":28862,"content":28863},{},[28864,28879,28894],{"nodeType":24068,"data":28865,"content":28866},{},[28867],{"nodeType":940,"data":28868,"content":28869},{},[28870,28875],{"nodeType":944,"value":28871,"marks":28872,"data":28874},"No broker bottleneck",[28873],{"type":24041},{},{"nodeType":944,"value":28876,"marks":28877,"data":28878},": Event capacity scales with storage capacity, not broker node count",[],{},{"nodeType":24068,"data":28880,"content":28881},{},[28882],{"nodeType":940,"data":28883,"content":28884},{},[28885,28890],{"nodeType":944,"value":28886,"marks":28887,"data":28889},"Unlimited retention",[28888],{"type":24041},{},{"nodeType":944,"value":28891,"marks":28892,"data":28893},": Events can persist indefinitely because storage is cheap and abundant",[],{},{"nodeType":24068,"data":28895,"content":28896},{},[28897],{"nodeType":940,"data":28898,"content":28899},{},[28900,28905],{"nodeType":944,"value":28901,"marks":28902,"data":28904},"Parallel consumption",[28903],{"type":24041},{},{"nodeType":944,"value":28906,"marks":28907,"data":28908},": Thousands of agents can consume events concurrently without overwhelming broker nodes—they’re reading directly from distributed storage",[],{},{"nodeType":940,"data":28910,"content":28911},{},[28912],{"nodeType":944,"value":28913,"marks":28914,"data":28915},"Because events are part of the storage fabric:",[],{},{"nodeType":24064,"data":28917,"content":28918},{},[28919,28934,28949],{"nodeType":24068,"data":28920,"content":28921},{},[28922],{"nodeType":940,"data":28923,"content":28924},{},[28925,28930],{"nodeType":944,"value":28926,"marks":28927,"data":28929},"Sub-millisecond publish latency",[28928],{"type":24041},{},{"nodeType":944,"value":28931,"marks":28932,"data":28933},": Writing an event is a storage write, leveraging NVMe and RDMA",[],{},{"nodeType":24068,"data":28935,"content":28936},{},[28937],{"nodeType":940,"data":28938,"content":28939},{},[28940,28945],{"nodeType":944,"value":28941,"marks":28942,"data":28944},"Massive parallel throughput",[28943],{"type":24041},{},{"nodeType":944,"value":28946,"marks":28947,"data":28948},": The storage fabric can handle millions of events per second across the cluster",[],{},{"nodeType":24068,"data":28950,"content":28951},{},[28952],{"nodeType":940,"data":28953,"content":28954},{},[28955,28960],{"nodeType":944,"value":28956,"marks":28957,"data":28959},"No data movement for triggers",[28958],{"type":24041},{},{"nodeType":944,"value":28961,"marks":28962,"data":28963},": When an event triggers a DataEngine function, the function executes on the same C-Node where the event is stored—zero data movement",[],{},{"nodeType":24461,"data":28965,"content":28966},{},[28967],{"nodeType":944,"value":28968,"marks":28969,"data":28970},"3. State Management: Transactional Tables, Vectors, and Consistency",[],{},{"nodeType":940,"data":28972,"content":28973},{},[28974],{"nodeType":944,"value":28975,"marks":28976,"data":28977},"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":940,"data":28979,"content":28980},{},[28981],{"nodeType":944,"value":28982,"marks":28983,"data":28984},"VAST DataBase unifies three capabilities in a single storage layer:",[],{},{"nodeType":25626,"data":28986,"content":28987},{},[28988,29003,29018],{"nodeType":24068,"data":28989,"content":28990},{},[28991],{"nodeType":940,"data":28992,"content":28993},{},[28994,28999],{"nodeType":944,"value":28995,"marks":28996,"data":28998},"OLTP: ",[28997],{"type":24041},{},{"nodeType":944,"value":29000,"marks":29001,"data":29002},"Transactional state (agent metadata, task status, workflow state)",[],{},{"nodeType":24068,"data":29004,"content":29005},{},[29006],{"nodeType":940,"data":29007,"content":29008},{},[29009,29014],{"nodeType":944,"value":29010,"marks":29011,"data":29013},"OLAP:",[29012],{"type":24041},{},{"nodeType":944,"value":29015,"marks":29016,"data":29017}," Analytical queries (aggregate agent performance, system metrics)",[],{},{"nodeType":24068,"data":29019,"content":29020},{},[29021],{"nodeType":940,"data":29022,"content":29023},{},[29024,29029],{"nodeType":944,"value":29025,"marks":29026,"data":29028},"Vector storage:",[29027],{"type":24041},{},{"nodeType":944,"value":29030,"marks":29031,"data":29032}," Embeddings for RAG, semantic search, and memory retrieval",[],{},{"nodeType":940,"data":29034,"content":29035},{},[29036],{"nodeType":944,"value":29037,"marks":29038,"data":29039},"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":24064,"data":29041,"content":29042},{},[29043,29058,29073],{"nodeType":24068,"data":29044,"content":29045},{},[29046],{"nodeType":940,"data":29047,"content":29048},{},[29049,29054],{"nodeType":944,"value":29050,"marks":29051,"data":29053},"Row-based index: ",[29052],{"type":24041},{},{"nodeType":944,"value":29055,"marks":29056,"data":29057},"For transactional lookups (e.g., “fetch state for agent_id=123”)",[],{},{"nodeType":24068,"data":29059,"content":29060},{},[29061],{"nodeType":940,"data":29062,"content":29063},{},[29064,29069],{"nodeType":944,"value":29065,"marks":29066,"data":29068},"Column-based index:",[29067],{"type":24041},{},{"nodeType":944,"value":29070,"marks":29071,"data":29072}," For analytical scans (e.g., “count all agents with status=failed”)",[],{},{"nodeType":24068,"data":29074,"content":29075},{},[29076],{"nodeType":940,"data":29077,"content":29078},{},[29079,29084],{"nodeType":944,"value":29080,"marks":29081,"data":29083},"Vector index:",[29082],{"type":24041},{},{"nodeType":944,"value":29085,"marks":29086,"data":29087}," approximate nearest neighbor search for embeddings",[],{},{"nodeType":940,"data":29089,"content":29090},{},[29091],{"nodeType":944,"value":29092,"marks":29093,"data":29095},"Vector Operations at Scale",[29094],{"type":24041},{},{"nodeType":940,"data":29097,"content":29098},{},[29099],{"nodeType":944,"value":29100,"marks":29101,"data":29102},"The vector storage subsystem is designed for trillion-scale vector search. Key characteristics include:",[],{},{"nodeType":24064,"data":29104,"content":29105},{},[29106,29121,29136],{"nodeType":24068,"data":29107,"content":29108},{},[29109],{"nodeType":940,"data":29110,"content":29111},{},[29112,29117],{"nodeType":944,"value":29113,"marks":29114,"data":29116},"Parallel updates",[29115],{"type":24041},{},{"nodeType":944,"value":29118,"marks":29119,"data":29120},": Unlike traditional vector DBs that require full re-indexing, VAST’s parallel architecture allows concurrent inserts without locking the entire index",[],{},{"nodeType":24068,"data":29122,"content":29123},{},[29124],{"nodeType":940,"data":29125,"content":29126},{},[29127,29132],{"nodeType":944,"value":29128,"marks":29129,"data":29131},"Freshness of data: ",[29130],{"type":24041},{},{"nodeType":944,"value":29133,"marks":29134,"data":29135},"All of the data is searchable, including data that was not already converted to the vectorDB",[],{},{"nodeType":24068,"data":29137,"content":29138},{},[29139],{"nodeType":940,"data":29140,"content":29141},{},[29142,29147],{"nodeType":944,"value":29143,"marks":29144,"data":29146},"Trillion-vector scale",[29145],{"type":24041},{},{"nodeType":944,"value":29148,"marks":29149,"data":29150},": No sharding and no performance degradation at scale",[],{},{"nodeType":24461,"data":29152,"content":29153},{},[29154],{"nodeType":944,"value":29155,"marks":29156,"data":29157},"4. The Memory Hierarchy: KV Cache Offloading for GPU Efficiency and Endless Context",[],{},{"nodeType":940,"data":29159,"content":29160},{},[29161],{"nodeType":944,"value":29162,"marks":29163,"data":29164},"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":940,"data":29166,"content":29167},{},[29168,29171,29178],{"nodeType":944,"value":24542,"marks":29169,"data":29170},[],{},{"nodeType":6165,"data":29172,"content":29173},{"uri":24369},[29174],{"nodeType":944,"value":29175,"marks":29176,"data":29177},"KV cache is the key to unlocking high GPU utilization,",[],{},{"nodeType":944,"value":29179,"marks":29180,"data":29181}," reducing latency of response, and the ability to support endless context conversations that multi-agent systems require.",[],{},{"nodeType":940,"data":29183,"content":29184},{},[29185],{"nodeType":944,"value":29186,"marks":29187,"data":29189},"The GPU Utilization Problem",[29188],{"type":24041},{},{"nodeType":940,"data":29191,"content":29192},{},[29193],{"nodeType":944,"value":29194,"marks":29195,"data":29196},"A GPU’s compute capacity is wasted if its memory (HBM) fills up before its processors are fully utilized. ",[],{},{"nodeType":940,"data":29198,"content":29199},{},[29200],{"nodeType":944,"value":29201,"marks":29202,"data":29203},"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":940,"data":29205,"content":29206},{},[29207],{"nodeType":944,"value":29208,"marks":29209,"data":29210},"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":940,"data":29212,"content":29213},{},[29214],{"nodeType":944,"value":29215,"marks":29216,"data":29218},"The Solution: KV Cache Offloading",[29217],{"type":24041},{},{"nodeType":940,"data":29220,"content":29221},{},[29222],{"nodeType":944,"value":29223,"marks":29224,"data":29225},"By offloading the KV cache from GPU HBM to VAST’s high-speed storage fabric, you break the memory bottleneck:",[],{},{"nodeType":24064,"data":29227,"content":29228},{},[29229,29239,29249],{"nodeType":24068,"data":29230,"content":29231},{},[29232],{"nodeType":940,"data":29233,"content":29234},{},[29235],{"nodeType":944,"value":29236,"marks":29237,"data":29238},"Hot cache blocks (actively being used for generation) stay in GPU HBM",[],{},{"nodeType":24068,"data":29240,"content":29241},{},[29242],{"nodeType":940,"data":29243,"content":29244},{},[29245],{"nodeType":944,"value":29246,"marks":29247,"data":29248},"Cold cache blocks (common prefixes, older context, inactive requests) are moved to VAST storage",[],{},{"nodeType":24068,"data":29250,"content":29251},{},[29252],{"nodeType":940,"data":29253,"content":29254},{},[29255],{"nodeType":944,"value":29256,"marks":29257,"data":29258},"When needed, cold blocks are fetched back to HBM in under 1ms",[],{},{"nodeType":940,"data":29260,"content":29261},{},[29262],{"nodeType":944,"value":29263,"marks":29264,"data":29265},"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":940,"data":29267,"content":29268},{},[29269],{"nodeType":944,"value":29270,"marks":29271,"data":29273},"Enabling Endless Context for Multi-Agent Systems",[29272],{"type":24041},{},{"nodeType":940,"data":29275,"content":29276},{},[29277],{"nodeType":944,"value":29278,"marks":29279,"data":29280},"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":940,"data":29282,"content":29283},{},[29284],{"nodeType":944,"value":29285,"marks":29286,"data":29287},"With KV cache offloading:",[],{},{"nodeType":24064,"data":29289,"content":29290},{},[29291,29301,29311],{"nodeType":24068,"data":29292,"content":29293},{},[29294],{"nodeType":940,"data":29295,"content":29296},{},[29297],{"nodeType":944,"value":29298,"marks":29299,"data":29300},"Agents can maintain effectively unlimited context length—bounded only by storage, not GPU memory",[],{},{"nodeType":24068,"data":29302,"content":29303},{},[29304],{"nodeType":940,"data":29305,"content":29306},{},[29307],{"nodeType":944,"value":29308,"marks":29309,"data":29310},"Long-running agents don’t need to truncate or summarize their context to fit memory constraints",[],{},{"nodeType":24068,"data":29312,"content":29313},{},[29314],{"nodeType":940,"data":29315,"content":29316},{},[29317],{"nodeType":944,"value":29318,"marks":29319,"data":29320},"Multi-turn reasoning tasks can operate over massive context windows without performance degradation",[],{},{"nodeType":940,"data":29322,"content":29323},{},[29324],{"nodeType":944,"value":29325,"marks":29326,"data":29328},"The Technical Mechanism",[29327],{"type":24041},{},{"nodeType":940,"data":29330,"content":29331},{},[29332],{"nodeType":944,"value":29333,"marks":29334,"data":29335},"VAST achieves KV cache offloading by integrating with inference frameworks (e.g., NVIDIA Dynamo, LMCache) at the KV cache layer:",[],{},{"nodeType":24064,"data":29337,"content":29338},{},[29339,29349,29359,29369,29379],{"nodeType":24068,"data":29340,"content":29341},{},[29342],{"nodeType":940,"data":29343,"content":29344},{},[29345],{"nodeType":944,"value":29346,"marks":29347,"data":29348},"The inference engine generates KV cache blocks during forward passes",[],{},{"nodeType":24068,"data":29350,"content":29351},{},[29352],{"nodeType":940,"data":29353,"content":29354},{},[29355],{"nodeType":944,"value":29356,"marks":29357,"data":29358},"The cache manager (modified to use VAST as backing storage) keeps frequently-accessed blocks in GPU HBM",[],{},{"nodeType":24068,"data":29360,"content":29361},{},[29362],{"nodeType":940,"data":29363,"content":29364},{},[29365],{"nodeType":944,"value":29366,"marks":29367,"data":29368},"When HBM fills up, cold blocks are evicted to VAST storage via high-speed NVMe and RDMA",[],{},{"nodeType":24068,"data":29370,"content":29371},{},[29372],{"nodeType":940,"data":29373,"content":29374},{},[29375],{"nodeType":944,"value":29376,"marks":29377,"data":29378},"On a cache miss, the block is fetched from VAST storage with sub-millisecond latency",[],{},{"nodeType":24068,"data":29380,"content":29381},{},[29382],{"nodeType":940,"data":29383,"content":29384},{},[29385],{"nodeType":944,"value":29386,"marks":29387,"data":29388},"Prefetch hints and access patterns allow proactive loading of likely-needed blocks",[],{},{"nodeType":940,"data":29390,"content":29391},{},[29392],{"nodeType":944,"value":29393,"marks":29394,"data":29395},"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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Analytics",{"metadata":33207,"sys":33210,"fields":33220},{"tags":33208,"concepts":33209},[],[],{"space":33211,"id":33213,"type":16,"createdAt":33214,"updatedAt":33215,"environment":33216,"publishedVersion":345,"revision":163,"contentType":33218,"locale":29},{"sys":33212},{"type":12,"linkType":13,"id":14},"4SH4MM2kDCqA3s5kKwTn3l","2025-11-12T12:14:52.895Z","2025-11-13T10:44:34.826Z",{"sys":33217},{"id":21,"type":12,"linkType":22},{"sys":33219},{"type":12,"linkType":27,"id":792},{"entryTitle":33202,"image":33221},{"metadata":33222,"sys":33225,"fields":33233},{"tags":33223,"concepts":33224},[],[],{"space":33226,"id":33228,"type":42,"createdAt":33229,"updatedAt":33230,"environment":33231,"publishedVersion":185,"revision":140,"locale":29},{"sys":33227},{"type":12,"linkType":13,"id":14},"1wjqKGi3An73ZbZ9RuelZA","2025-11-12T12:14:43.667Z","2025-11-13T12:25:54.232Z",{"sys":33232},{"id":21,"type":12,"linkType":22},{"title":33202,"description":33234,"file":33235},"Streaming 136M Messages Per Second on VAST\n",{"url":33236,"details":33237,"fileName":33240,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1wjqKGi3An73ZbZ9RuelZA\u002Ff82867fcfa0ca02a04bf184180b5bfea\u002Fstreaming-136m-messages-per-second-on-vast.webp",{"size":33238,"image":33239},116602,{"width":55,"height":56},"streaming-136m-messages-per-second-on-vast.webp",{"nodeType":936,"data":33242,"content":33243},{},[33244,33251,33267,33274,33292,33299,33306,33370,33377,33410,33417,33424,33445,33452,33459,33466,33483,33502,33523,33530,33551,33558,33602,33609,33628,33635,33673,33700,33765,33786,33793,33800,33823,33830,33837,33856,33863,33906,33913,34019,34035,34042,34049,34056,34077,34111,34118,34125,34143],{"nodeType":940,"data":33245,"content":33246},{},[33247],{"nodeType":944,"value":33248,"marks":33249,"data":33250},"Data streaming presents a lot of challenges. At scale these challenges compound, requiring heavy engineering effort to deal with each specific use for that data. ",[],{},{"nodeType":940,"data":33252,"content":33253},{},[33254,33258,33263],{"nodeType":944,"value":33255,"marks":33256,"data":33257},"After you’ve stitched together the requisite software and infrastructure to accomplish your goals, you hope the goals don’t change and that the selected components continue to perform. This can be challenging because each component added to the system introduces compounding operational effort ",[],{},{"nodeType":944,"value":33259,"marks":33260,"data":33262},"and",[33261],{"type":24736},{},{"nodeType":944,"value":33264,"marks":33265,"data":33266}," fragility.",[],{},{"nodeType":940,"data":33268,"content":33269},{},[33270],{"nodeType":944,"value":33271,"marks":33272,"data":33273},"We’re solving this problem by building out the VAST AI OS to include the core components for modern data-intensive applications, from real-time packet analysis to multi-agent AI pipelines. We’ve added multiple data stores, serverless functions, and more, all running at extremely high performance thanks to our underlying storage architecture.",[],{},{"nodeType":940,"data":33275,"content":33276},{},[33277,33281,33289],{"nodeType":944,"value":33278,"marks":33279,"data":33280},"In this post, we’re focusing on one component that’s an integral part of any data pipeline: the event broker. Specifically, we’re digging into the performance of the Kafka-compatible ",[],{},{"nodeType":6165,"data":33282,"content":33284},{"uri":33283},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fsolution-briefs\u002Fvast-event-broker",[33285],{"nodeType":944,"value":33286,"marks":33287,"data":33288},"VAST Event Broker",[],{},{"nodeType":944,"value":6169,"marks":33290,"data":33291},[],{},{"nodeType":23519,"data":33293,"content":33294},{},[33295],{"nodeType":944,"value":33296,"marks":33297,"data":33298},"VAST Event Broker vs. Apache Kafka and Redpanda",[],{},{"nodeType":940,"data":33300,"content":33301},{},[33302],{"nodeType":944,"value":33303,"marks":33304,"data":33305},"Because we’re talking about benchmarks, let’s start with some numbers:",[],{},{"nodeType":24064,"data":33307,"content":33308},{},[33309,33324,33343],{"nodeType":24068,"data":33310,"content":33311},{},[33312],{"nodeType":940,"data":33313,"content":33314},{},[33315,33319],{"nodeType":944,"value":33316,"marks":33317,"data":33318},"The VAST Event Broker can sustain ",[],{},{"nodeType":944,"value":33320,"marks":33321,"data":33323},"604% more throughput than Apache Kafka.",[33322],{"type":24041},{},{"nodeType":24068,"data":33325,"content":33326},{},[33327],{"nodeType":940,"data":33328,"content":33329},{},[33330,33334,33339],{"nodeType":944,"value":33331,"marks":33332,"data":33333},"A single VAST Event Broker node is ",[],{},{"nodeType":944,"value":33335,"marks":33336,"data":33338},"156% faster than Redpanda ",[33337],{"type":24041},{},{"nodeType":944,"value":33340,"marks":33341,"data":33342},"(according to their own published figures on similar infrastructure).",[],{},{"nodeType":24068,"data":33344,"content":33345},{},[33346],{"nodeType":940,"data":33347,"content":33348},{},[33349,33353,33358,33361,33366],{"nodeType":944,"value":33350,"marks":33351,"data":33352},"The VAST Event Broker was able to ",[],{},{"nodeType":944,"value":33354,"marks":33355,"data":33357},"ingest",[33356],{"type":24041},{},{"nodeType":944,"value":24053,"marks":33359,"data":33360},[],{},{"nodeType":944,"value":33362,"marks":33363,"data":33365},"136 million messages per second",[33364],{"type":24041},{},{"nodeType":944,"value":33367,"marks":33368,"data":33369}," at 99% efficiency across 88 nodes.",[],{},{"nodeType":940,"data":33371,"content":33372},{},[33373],{"nodeType":944,"value":33374,"marks":33375,"data":33376},"Of course, these abstract numbers don’t tell you much. So here’s some more context: We ran an OpenMessaging benchmark using a \"throughput\"-oriented Kafka driver on 3-node broker setups for VAST and Apache Kafka. Some workload details:",[],{},{"nodeType":24064,"data":33378,"content":33379},{},[33380,33390,33400],{"nodeType":24068,"data":33381,"content":33382},{},[33383],{"nodeType":940,"data":33384,"content":33385},{},[33386],{"nodeType":944,"value":33387,"marks":33388,"data":33389},"1 topic",[],{},{"nodeType":24068,"data":33391,"content":33392},{},[33393],{"nodeType":940,"data":33394,"content":33395},{},[33396],{"nodeType":944,"value":33397,"marks":33398,"data":33399},"1 KB messages",[],{},{"nodeType":24068,"data":33401,"content":33402},{},[33403],{"nodeType":940,"data":33404,"content":33405},{},[33406],{"nodeType":944,"value":33407,"marks":33408,"data":33409},"128 partitions",[],{},{"nodeType":940,"data":33411,"content":33412},{},[33413],{"nodeType":944,"value":33414,"marks":33415,"data":33416},"This test supplied us with the maximum ingress figures - on identical infrastructure - for Kafka and VAST. We tested Redpanda in-house on the same gear and did not achieve their published figures for this workload. We elected to use their own figures anyway.",[],{},{"nodeType":940,"data":33418,"content":33419},{},[33420],{"nodeType":944,"value":33421,"marks":33422,"data":33423},"As noted above, the results of this 3-broker benchmark showed VAST significantly outperforming both other options:",[],{},{"nodeType":24488,"data":33425,"content":33444},{"target":33426},{"metadata":33427,"sys":33430,"fields":33437},{"tags":33428,"concepts":33429},[],[],{"space":33431,"id":33433,"type":42,"createdAt":33434,"updatedAt":33434,"environment":33435,"publishedVersion":140,"revision":47,"locale":29},{"sys":33432},{"type":12,"linkType":13,"id":14},"U6WtjyFvmEh7IgZyY2mMs","2025-11-13T22:03:01.726Z",{"sys":33436},{"id":21,"type":12,"linkType":22},{"title":33202,"description":33202,"file":33438},{"url":33439,"details":33440,"fileName":33443,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FU6WtjyFvmEh7IgZyY2mMs\u002Ffd0e9776039f7a1d3401b04ceb9c2885\u002Fstreaming-136m-messages-per-second-on-vast-1.webp",{"size":33441,"image":33442},20978,{"width":55,"height":56},"streaming-136m-messages-per-second-on-vast-1.webp",[],{"nodeType":940,"data":33446,"content":33447},{},[33448],{"nodeType":944,"value":33449,"marks":33450,"data":33451},"However, this is a 1:1 production\u002Fconsumption ratio test, which is a bit misleading because, in the real world, that ratio is very likely to be higher on the consumption side. Increasing the read pull on a cluster will put downward pressure on ingress performance. So if you’re operating on the margins of your pub\u002Fsub system (as in this benchmark), things can get scary.",[],{},{"nodeType":940,"data":33453,"content":33454},{},[33455],{"nodeType":944,"value":33456,"marks":33457,"data":33458},"The story ends there for Redpanda and Kafka - size accordingly. But our Event Broker has more to say.",[],{},{"nodeType":23519,"data":33460,"content":33461},{},[33462],{"nodeType":944,"value":33463,"marks":33464,"data":33465},"The DASE Architecture Fuels Performance",[],{},{"nodeType":940,"data":33467,"content":33468},{},[33469,33473,33480],{"nodeType":944,"value":33470,"marks":33471,"data":33472},"Redpanda and Apache Kafka versions take a shared-nothing approach to storing data. They focus on front-end software and use traditional replication and consensus algorithms to maintain resilience and availability. VAST leverages a sophisticated but highly streamlined approach to consensus and resilience: ",[],{},{"nodeType":6165,"data":33474,"content":33475},{"uri":23633},[33476],{"nodeType":944,"value":33477,"marks":33478,"data":33479},"Disaggregated and Shared Everything (DASE)",[],{},{"nodeType":944,"value":6169,"marks":33481,"data":33482},[],{},{"nodeType":940,"data":33484,"content":33485},{},[33486,33490,33498],{"nodeType":944,"value":33487,"marks":33488,"data":33489},"This figure depicts a simplified view of the shared-nothing, ",[],{},{"nodeType":6165,"data":33491,"content":33493},{"uri":33492},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRaft_(algorithm)",[33494],{"nodeType":944,"value":33495,"marks":33496,"data":33497},"Raft",[],{},{"nodeType":944,"value":33499,"marks":33500,"data":33501},"-based replication approach used by Redpanda. Apache Kafka uses Raft only for metadata management and Kafka’s In-Sync Replicas (ISR) model for data. It’s not depicted here because Redpanda’s is faster.",[],{},{"nodeType":24488,"data":33503,"content":33522},{"target":33504},{"metadata":33505,"sys":33508,"fields":33515},{"tags":33506,"concepts":33507},[],[],{"space":33509,"id":33511,"type":42,"createdAt":33512,"updatedAt":33512,"environment":33513,"publishedVersion":140,"revision":47,"locale":29},{"sys":33510},{"type":12,"linkType":13,"id":14},"1g9T808Kv4jFL4LTy0ZTQA","2025-11-13T22:06:00.885Z",{"sys":33514},{"id":21,"type":12,"linkType":22},{"title":33202,"description":33202,"file":33516},{"url":33517,"details":33518,"fileName":33521,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1g9T808Kv4jFL4LTy0ZTQA\u002F9c9f58f3516350964d641c766f3e6238\u002Fstreaming-136m-messages-per-second-on-vast-2.webp",{"size":33519,"image":33520},33330,{"width":55,"height":56},"streaming-136m-messages-per-second-on-vast-2.webp",[],{"nodeType":940,"data":33524,"content":33525},{},[33526],{"nodeType":944,"value":33527,"marks":33528,"data":33529},"Writes are directed to the Kafka partition’s Raft leader (there is a Raft session per-partition) where that broker writes (at minimum) two additional copies of a data chunk to two other nodes. Fully replicated write amplification is part of the deal. Acknowledgements are sent back to the leader on successful writes and, once a minimum number of chunks are stored, the data is considered safe and coherent. Since this is a streaming platform, reads are happening very close in time to the writes, which incur checks of the data’s status over the Raft protocols, where misses are included in consensus traffic. ",[],{},{"nodeType":24488,"data":33531,"content":33550},{"target":33532},{"metadata":33533,"sys":33536,"fields":33543},{"tags":33534,"concepts":33535},[],[],{"space":33537,"id":33539,"type":42,"createdAt":33540,"updatedAt":33540,"environment":33541,"publishedVersion":140,"revision":47,"locale":29},{"sys":33538},{"type":12,"linkType":13,"id":14},"67aGH3Sg4F6mbaAE4AANku","2025-11-13T23:34:00.101Z",{"sys":33542},{"id":21,"type":12,"linkType":22},{"title":33202,"description":33202,"file":33544},{"url":33545,"details":33546,"fileName":33549,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F67aGH3Sg4F6mbaAE4AANku\u002F6469e26d13588a8a4407d7ac98eec869\u002Fstreaming-136m-messages-per-second-on-vast-3.webp",{"size":33547,"image":33548},49204,{"width":55,"height":56},"streaming-136m-messages-per-second-on-vast-3.webp",[],{"nodeType":940,"data":33552,"content":33553},{},[33554],{"nodeType":944,"value":33555,"marks":33556,"data":33557},"VAST is fundamentally different. Our approach is deceptively simple when visualized, but it is the result of sophisticated software design that merges modern networking and hardware capabilities to supply an end-to-end distributed system that “works” exactly like a single, infinitely scalable entity:",[],{},{"nodeType":24064,"data":33559,"content":33560},{},[33561,33582,33592],{"nodeType":24068,"data":33562,"content":33563},{},[33564],{"nodeType":940,"data":33565,"content":33566},{},[33567,33571,33579],{"nodeType":944,"value":33568,"marks":33569,"data":33570},"Erasure coding reduces write amplification from +200% to +3% while maintaining ",[],{},{"nodeType":6165,"data":33572,"content":33574},{"uri":33573},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fthe-rule-of-nines",[33575],{"nodeType":944,"value":33576,"marks":33577,"data":33578},"five nines of availability",[],{},{"nodeType":944,"value":6169,"marks":33580,"data":33581},[],{},{"nodeType":24068,"data":33583,"content":33584},{},[33585],{"nodeType":940,"data":33586,"content":33587},{},[33588],{"nodeType":944,"value":33589,"marks":33590,"data":33591},"Consensus is handled on-disk in the metadata layer, eliminating the need for external, complex, and often fragile consensus algorithms. Ultra low-latency RDMA networking and storage-class memory (SCM) make this possible.",[],{},{"nodeType":24068,"data":33593,"content":33594},{},[33595],{"nodeType":940,"data":33596,"content":33597},{},[33598],{"nodeType":944,"value":33599,"marks":33600,"data":33601},"VAST operates without siloing data for safe replication arrangements, making capacity and performance expansions trivial.",[],{},{"nodeType":940,"data":33603,"content":33604},{},[33605],{"nodeType":944,"value":33606,"marks":33607,"data":33608},"What all of this means is that throughput isn’t constrained by anything but the aggregate of available CPUs in the system and networking bandwidth, and latency is reduced through the elimination of consensus traffic. Fundamentally, almost everything VAST is grounded in DASE. ",[],{},{"nodeType":940,"data":33610,"content":33611},{},[33612,33616,33624],{"nodeType":944,"value":33613,"marks":33614,"data":33615},"I encourage you to read about it to understand the ",[],{},{"nodeType":6165,"data":33617,"content":33619},{"uri":33618},"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper#the-disaggregated-shared-everything-architecture",[33620],{"nodeType":944,"value":33621,"marks":33622,"data":33623},"details of the architecture",[],{},{"nodeType":944,"value":33625,"marks":33626,"data":33627}," and how it has revolutionized infrastructure over the past five years.",[],{},{"nodeType":23519,"data":33629,"content":33630},{},[33631],{"nodeType":944,"value":33632,"marks":33633,"data":33634},"Tabular Access to Topics",[],{},{"nodeType":940,"data":33636,"content":33637},{},[33638,33642,33648,33652,33658,33662,33670],{"nodeType":944,"value":33639,"marks":33640,"data":33641},"You can read more about the VAST Event Broker features ",[],{},{"nodeType":6165,"data":33643,"content":33644},{"uri":23712},[33645],{"nodeType":944,"value":25283,"marks":33646,"data":33647},[],{},{"nodeType":944,"value":33649,"marks":33650,"data":33651},", but the thing we’re focusing on now is how Event Broker topics are stored as ",[],{},{"nodeType":6165,"data":33653,"content":33654},{"uri":23700},[33655],{"nodeType":944,"value":16679,"marks":33656,"data":33657},[],{},{"nodeType":944,"value":33659,"marks":33660,"data":33661}," tables. Using a tabular backend for Kafka topics gives us a structured data interface to those topics. In this case, that interface can be Trino, Spark, and\u002For the greater Python ecosystem via the ",[],{},{"nodeType":6165,"data":33663,"content":33665},{"uri":33664},"https:\u002F\u002Fgithub.com\u002Fvast-data\u002Fvastdb_sdk",[33666],{"nodeType":944,"value":33667,"marks":33668,"data":33669},"VAST DataBase SDK",[],{},{"nodeType":944,"value":6169,"marks":33671,"data":33672},[],{},{"nodeType":940,"data":33674,"content":33675},{},[33676,33680,33688,33692,33696],{"nodeType":944,"value":33677,"marks":33678,"data":33679},"Rather than applications subscribing to topics over Kafka APIs and sorting through entire streams to down-select, or implementing complex pipelines with additional software like ",[],{},{"nodeType":6165,"data":33681,"content":33683},{"uri":33682},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Funleash-real-time-data-introducing-vast-database-apache-flink-connector",[33684],{"nodeType":944,"value":33685,"marks":33686,"data":33687},"Flink",[],{},{"nodeType":944,"value":33689,"marks":33690,"data":33691}," or ksqlDB, clients can choose to consume real-time ",[],{},{"nodeType":944,"value":33259,"marks":33693,"data":33695},[33694],{"type":24736},{},{"nodeType":944,"value":33697,"marks":33698,"data":33699}," historical data with a data-processing engine table-topics. This has a lot of advantages:",[],{},{"nodeType":24064,"data":33701,"content":33702},{},[33703,33735,33745,33755],{"nodeType":24068,"data":33704,"content":33705},{},[33706],{"nodeType":940,"data":33707,"content":33708},{},[33709,33713,33720,33724,33731],{"nodeType":944,"value":33710,"marks":33711,"data":33712},"No limitations are imposed by the Kafka protocol itself; partition count is not a factor in consumer access. Even if the topic has only 1 partition, the entire cluster can be brought to bear on the topic through processing engines (",[],{},{"nodeType":6165,"data":33714,"content":33716},{"uri":33715},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fwhite-papers\u002Ftrino-with-vast",[33717],{"nodeType":944,"value":20573,"marks":33718,"data":33719},[],{},{"nodeType":944,"value":33721,"marks":33722,"data":33723},", ",[],{},{"nodeType":6165,"data":33725,"content":33727},{"uri":33726},"https:\u002F\u002Fwww.vastdata.com\u002Ftechnology\u002Fspark",[33728],{"nodeType":944,"value":12088,"marks":33729,"data":33730},[],{},{"nodeType":944,"value":33732,"marks":33733,"data":33734},", Python SDK).",[],{},{"nodeType":24068,"data":33736,"content":33737},{},[33738],{"nodeType":940,"data":33739,"content":33740},{},[33741],{"nodeType":944,"value":33742,"marks":33743,"data":33744},"Current and historical data can be efficiently accessed and processed in real-time.",[],{},{"nodeType":24068,"data":33746,"content":33747},{},[33748],{"nodeType":940,"data":33749,"content":33750},{},[33751],{"nodeType":944,"value":33752,"marks":33753,"data":33754},"The tables themselves ensure high-availability, data resilience, and add the entire catalog of VAST’s governance, visibility, tenancy, and data protection features to the topics.",[],{},{"nodeType":24068,"data":33756,"content":33757},{},[33758],{"nodeType":940,"data":33759,"content":33760},{},[33761],{"nodeType":944,"value":33762,"marks":33763,"data":33764},"The DASE architecture handles low-latency consensus, avoiding discrete Zookeeper or KRaft instances.",[],{},{"nodeType":24488,"data":33766,"content":33785},{"target":33767},{"metadata":33768,"sys":33771,"fields":33778},{"tags":33769,"concepts":33770},[],[],{"space":33772,"id":33774,"type":42,"createdAt":33775,"updatedAt":33775,"environment":33776,"publishedVersion":140,"revision":47,"locale":29},{"sys":33773},{"type":12,"linkType":13,"id":14},"3HdNDEL4JqLpOeFsSu7o9o","2025-11-13T23:35:21.290Z",{"sys":33777},{"id":21,"type":12,"linkType":22},{"title":33202,"description":33202,"file":33779},{"url":33780,"details":33781,"fileName":33784,"contentType":58},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3HdNDEL4JqLpOeFsSu7o9o\u002F1c6160f0b3d4038347596963b636d974\u002Fstreaming-136m-messages-per-second-on-vast-4.webp",{"size":33782,"image":33783},39634,{"width":55,"height":56},"streaming-136m-messages-per-second-on-vast-4.webp",[],{"nodeType":940,"data":33787,"content":33788},{},[33789],{"nodeType":944,"value":33790,"marks":33791,"data":33792},"This capability obviates the need for split architectures for housing wide time windows and separating real-time and historical data. 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