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Setting it up requires zero complex cluster state management - just a clean config file, a standard ",{"data":12266,"marks":12267,"value":12270,"nodeType":1906},{},[12268],{"type":12269},"italic","vllm serve launch",{"data":12272,"marks":12273,"value":12274,"nodeType":1906},{},[],", and ",{"data":12276,"marks":12277,"value":12279,"nodeType":1906},{},[12278],{"type":12269},"lmcache bench",{"data":12281,"marks":12282,"value":12283,"nodeType":1906},{},[]," to watch the performance scale.",{"data":12285,"content":12286,"nodeType":1920},{},[12287],{"data":12288,"marks":12289,"value":2030,"nodeType":1906},{},[],{"data":12291,"content":12292,"nodeType":1920},{},[12293],{"data":12294,"marks":12295,"value":12297,"nodeType":1906},{},[12296],{"type":12205},"Here are the steps we took and the detailed results we achieved",{"data":12299,"content":12300,"nodeType":12305},{},[12301],{"data":12302,"marks":12303,"value":12304,"nodeType":1906},{},[],"Step1: Bucket Creation ","heading-5",{"data":12307,"content":12308,"nodeType":1920},{},[12309],{"data":12310,"marks":12311,"value":12312,"nodeType":1906},{},[],"Follow the following guide to create a S3 bucket on VAST via VMS",{"data":12314,"content":12315,"nodeType":1920},{},[12316,12319,12328],{"data":12317,"marks":12318,"value":2030,"nodeType":1906},{},[],{"data":12320,"content":12322,"nodeType":1915},{"uri":12321},"https:\u002F\u002Fkb.vastdata.com\u002Fdocumentation\u002Fdocs\u002Fcreating-s3-buckets-via-vms-6",[12323],{"data":12324,"marks":12325,"value":12321,"nodeType":1906},{},[12326],{"type":12327},"underline",{"data":12329,"marks":12330,"value":2915,"nodeType":1906},{},[],{"data":12332,"content":12333,"nodeType":1920},{},[12334],{"data":12335,"marks":12336,"value":12337,"nodeType":1906},{},[],"For example: create a bucket named kvc. ",{"data":12339,"content":12340,"nodeType":1920},{},[12341],{"data":12342,"marks":12343,"value":12344,"nodeType":1906},{},[],"Assign an owner, group, and identity policy, then generate access and secret keys for the owner.",{"data":12346,"content":12347,"nodeType":1920},{},[12348],{"data":12349,"marks":12350,"value":12351,"nodeType":1906},{},[],"Configure the access and secret keys in ~\u002F.aws\u002Fcredentials",{"data":12353,"content":12373,"nodeType":12374},{"target":12354},{"metadata":12355,"sys":12358,"fields":12368},{"tags":12356,"concepts":12357},[],[],{"space":12359,"id":12361,"type":14,"createdAt":12362,"updatedAt":12362,"environment":12363,"publishedVersion":939,"revision":1135,"contentType":12365,"locale":672},{"sys":12360},{"type":656,"linkType":657,"id":658},"vE3OhHnHFsGUzBUTfkxnE","2026-07-08T00:14:02.636Z",{"sys":12364},{"id":664,"type":656,"linkType":665},{"sys":12366},{"type":656,"linkType":670,"id":12367},"codeBlock",{"entryTitle":12369,"code":12370,"language":12371,"theme":12372},"Code: ~\u002F.aws\u002Fcredentials","[default]\nendpoint_url = http:\u002F\u002Fvast3115-var.VastENG.lab\naws_access_key_id = xxx\naws_secret_access_key = yyy\nresponse_checksum_validation = when_required\npreferred_transfer_client = crt","python","vast-data-dark",[],"embedded-entry-block",{"data":12376,"content":12377,"nodeType":1920},{},[12378],{"data":12379,"marks":12380,"value":12381,"nodeType":1906},{},[],"\nIn the example above, the S3 endpoint is configured to use the VAST DNS endpoint defined in the backend configuration.",{"data":12383,"content":12384,"nodeType":1920},{},[12385],{"data":12386,"marks":12387,"value":12388,"nodeType":1906},{},[],"Verify S3 connectivity from the GPU server using the AWS CLI. The bucket should initially be empty. For example:",{"data":12390,"content":12407,"nodeType":12374},{"target":12391},{"metadata":12392,"sys":12395,"fields":12404},{"tags":12393,"concepts":12394},[],[],{"space":12396,"id":12398,"type":14,"createdAt":12399,"updatedAt":12399,"environment":12400,"publishedVersion":846,"revision":1135,"contentType":12402,"locale":672},{"sys":12397},{"type":656,"linkType":657,"id":658},"22dVxXeVpC51Emk6wJz2W2","2026-07-08T00:13:22.419Z",{"sys":12401},{"id":664,"type":656,"linkType":665},{"sys":12403},{"type":656,"linkType":670,"id":12367},{"entryTitle":12405,"code":12406,"language":12371,"theme":12372},"Verify S3 ","kvc@rtx6k02-var:~$ aws s3 ls\n2026-05-14 15:20:59 kvc\n",[],{"data":12409,"content":12410,"nodeType":12305},{},[12411],{"data":12412,"marks":12413,"value":12414,"nodeType":1906},{},[],"\n\nStep 2: Create a Python Virtual Environment and Install LMCache with vLLM",{"data":12416,"content":12417,"nodeType":1920},{},[12418,12425,12435,12441,12451],{"data":12419,"marks":12420,"value":12424,"nodeType":1906},{},[12421,12422],{"type":12269},{"type":12423},"code","Note: LMCache now has a ",{"data":12426,"content":12428,"nodeType":1915},{"uri":12427},"https:\u002F\u002Fblog.lmcache.ai\u002Fen\u002F2026\u002F06\u002F15\u002Funderstanding-lmcache-mp-mode-transfer-paths-a-beginners-guide\u002F",[12429],{"data":12430,"marks":12431,"value":12434,"nodeType":1906},{},[12432,12433],{"type":12269},{"type":12423},"new deployment model called “MP Mode”",{"data":12436,"marks":12437,"value":12440,"nodeType":1906},{},[12438,12439],{"type":12269},{"type":12423}," for Multi Process mode, which comes with quite a few advantages in terms of performance, maintenance and fault tolerance. We’ll check it out soon. If you want to check it out for yourself, follow ",{"data":12442,"content":12444,"nodeType":1915},{"uri":12443},"https:\u002F\u002Fdocs.lmcache.ai\u002Fgetting_started\u002Fquickstart.html",[12445],{"data":12446,"marks":12447,"value":12450,"nodeType":1906},{},[12448,12449],{"type":12269},{"type":12423},"these steps",{"data":12452,"marks":12453,"value":12456,"nodeType":1906},{},[12454,12455],{"type":12269},{"type":12423}," instead.",{"data":12458,"content":12475,"nodeType":12374},{"target":12459},{"metadata":12460,"sys":12463,"fields":12472},{"tags":12461,"concepts":12462},[],[],{"space":12464,"id":12466,"type":14,"createdAt":12467,"updatedAt":12467,"environment":12468,"publishedVersion":939,"revision":1135,"contentType":12470,"locale":672},{"sys":12465},{"type":656,"linkType":657,"id":658},"GVOqRDWXHrnG8W2yNhrcM","2026-07-08T00:12:12.051Z",{"sys":12469},{"id":664,"type":656,"linkType":665},{"sys":12471},{"type":656,"linkType":670,"id":12367},{"entryTitle":12473,"code":12474,"language":12371,"theme":12372},"Code: Create a Python Virtual Environment and Install LMCache with vLLM","curl -LsSf https:\u002F\u002Fastral.sh\u002Fuv\u002Finstall.sh | sh\nuv venv lmcache_vast  --python 3.12 --seed\nsource ~\u002Flmcache_vast\u002Fbin\u002Factivate\nuv pip install vllm nixl lmcache\n",[],{"data":12477,"content":12478,"nodeType":12305},{},[12479],{"data":12480,"marks":12481,"value":12482,"nodeType":1906},{},[],"\n\nStep 3: Configure LMCache Backend with NIXL + S3",{"data":12484,"content":12485,"nodeType":1920},{},[12486,12489,12497],{"data":12487,"marks":12488,"value":2030,"nodeType":1906},{},[],{"data":12490,"content":12492,"nodeType":1915},{"uri":12491},"http:\u002F\u002Flmcache.ai",[12493],{"data":12494,"marks":12495,"value":12496,"nodeType":1906},{},[],"LMCache",{"data":12498,"marks":12499,"value":12500,"nodeType":1906},{},[]," supports multiple storage backends for offloading and sharing KV cache data across processes and nodes.",{"data":12502,"content":12503,"nodeType":1920},{},[12504],{"data":12505,"marks":12506,"value":12507,"nodeType":1906},{},[],"In this example, we configure the NIXL dynamic mode backend using an S3 bucket as the remote storage layer.",{"data":12509,"content":12510,"nodeType":1920},{},[12511],{"data":12512,"marks":12513,"value":12514,"nodeType":1906},{},[],"The NIXL storage backend supports a dynamic mode, which creates NIXL storage descriptors on demand instead of preallocating them during initialization. To enable dynamic mode, set: \n\nextra_config:\n nixl_pool_size: 0",{"data":12516,"content":12517,"nodeType":1920},{},[12518],{"data":12519,"marks":12520,"value":12521,"nodeType":1906},{},[],"This configuration is recommended for:",{"data":12523,"content":12524,"nodeType":12184},{},[12525,12535,12545,12555],{"data":12526,"content":12527,"nodeType":12173},{},[12528],{"data":12529,"content":12530,"nodeType":1920},{},[12531],{"data":12532,"marks":12533,"value":12534,"nodeType":1906},{},[],"dynamic workloads",{"data":12536,"content":12537,"nodeType":12173},{},[12538],{"data":12539,"content":12540,"nodeType":1920},{},[12541],{"data":12542,"marks":12543,"value":12544,"nodeType":1906},{},[],"large-scale KV sharing",{"data":12546,"content":12547,"nodeType":12173},{},[12548],{"data":12549,"content":12550,"nodeType":1920},{},[12551],{"data":12552,"marks":12553,"value":12554,"nodeType":1906},{},[],"reducing initialization overhead",{"data":12556,"content":12557,"nodeType":12173},{},[12558],{"data":12559,"content":12560,"nodeType":1920},{},[12561],{"data":12562,"marks":12563,"value":12564,"nodeType":1906},{},[],"improving storage flexibility",{"data":12566,"content":12567,"nodeType":1920},{},[12568],{"data":12569,"marks":12570,"value":12571,"nodeType":1906},{},[],"Configure the NIXL + S3 backend YAML as follows (e.g \u002Fpath\u002Fto\u002Fnixl_s3.yaml):",{"data":12573,"content":12590,"nodeType":12374},{"target":12574},{"metadata":12575,"sys":12578,"fields":12587},{"tags":12576,"concepts":12577},[],[],{"space":12579,"id":12581,"type":14,"createdAt":12582,"updatedAt":12582,"environment":12583,"publishedVersion":939,"revision":1135,"contentType":12585,"locale":672},{"sys":12580},{"type":656,"linkType":657,"id":658},"4BahCMbmx7C2cjHG1Y3nq5","2026-07-08T00:11:20.094Z",{"sys":12584},{"id":664,"type":656,"linkType":665},{"sys":12586},{"type":656,"linkType":670,"id":12367},{"entryTitle":12588,"code":12589,"language":12371,"theme":12372},"Configure the NIXL + S3 backend YAML","chunk_size: 1024\nlocal_cpu: False\nsave_unfull_chunk: False\nenable_async_loading: False\nnixl_buffer_size: 4294967296\nnixl_buffer_device: cpu\nextra_config:\n  s3_num_io_threads: 64\n  enable_nixl_storage: true\n  nixl_backend: OBJ\n  nixl_pool_size: 0\n  nixl_presence_cache: False\n  nixl_async_put: False\n  save_chunk_meta: true\n  nixl_backend_params:\n    access_key: xxx\n    secret_key: yyy\n    bucket: kvc\n    region: us-east-1\n    endpoint_override: http:\u002F\u002Fvast3115-var.VastENG.lab\n",[],{"data":12592,"content":12593,"nodeType":12305},{},[12594],{"data":12595,"marks":12596,"value":12597,"nodeType":1906},{},[],"\nStep 4: Export the Required Environment Variables",{"data":12599,"content":12600,"nodeType":1920},{},[12601],{"data":12602,"marks":12603,"value":12604,"nodeType":1906},{},[],"Configure the following environment variables before starting the vLLM server:\n",{"data":12606,"content":12623,"nodeType":12374},{"target":12607},{"metadata":12608,"sys":12611,"fields":12620},{"tags":12609,"concepts":12610},[],[],{"space":12612,"id":12614,"type":14,"createdAt":12615,"updatedAt":12615,"environment":12616,"publishedVersion":939,"revision":1135,"contentType":12618,"locale":672},{"sys":12613},{"type":656,"linkType":657,"id":658},"gyR7XW51HLcsFmFMOfZkh","2026-07-08T00:10:32.462Z",{"sys":12617},{"id":664,"type":656,"linkType":665},{"sys":12619},{"type":656,"linkType":670,"id":12367},{"entryTitle":12621,"code":12622,"language":12371,"theme":12372},"Code: Export the Required Environment Variables","export LMCACHE_CONFIG_FILE=\u002Fhome\u002Fkvc\u002Fkvc\u002Flmcache\u002Fs3\u002Fnixl_s3.yaml\nexport CUDA_VISIBLE_DEVICES=0,1\nexport PYTHONHASHSEED=0\n",[],{"data":12625,"content":12626,"nodeType":1920},{},[12627],{"data":12628,"marks":12629,"value":12632,"nodeType":1906},{},[12630,12631],{"type":12205},{"type":12327},"Variable breakdown:",{"data":12634,"content":12635,"nodeType":12184},{},[12636,12651,12666,12681],{"data":12637,"content":12638,"nodeType":12173},{},[12639],{"data":12640,"content":12641,"nodeType":1920},{},[12642,12647],{"data":12643,"marks":12644,"value":12646,"nodeType":1906},{},[12645],{"type":12205},"LMCACHE_CONFIG_FILE",{"data":12648,"marks":12649,"value":12650,"nodeType":1906},{},[],"\n Points to the LMCache backend configuration file.\n In this example, the configuration enables the NIXL + S3 storage backend.",{"data":12652,"content":12653,"nodeType":12173},{},[12654],{"data":12655,"content":12656,"nodeType":1920},{},[12657,12662],{"data":12658,"marks":12659,"value":12661,"nodeType":1906},{},[12660],{"type":12205},"CUDA_VISIBLE_DEVICES=0,1",{"data":12663,"marks":12664,"value":12665,"nodeType":1906},{},[],"\n Restricts the server to GPUs 0 and 1 only.\n vLLM will use these GPUs for tensor parallel execution.",{"data":12667,"content":12668,"nodeType":12173},{},[12669],{"data":12670,"content":12671,"nodeType":1920},{},[12672,12677],{"data":12673,"marks":12674,"value":12676,"nodeType":1906},{},[12675],{"type":12205},"PYTHONHASHSEED=0",{"data":12678,"marks":12679,"value":12680,"nodeType":1906},{},[],"\n Enables deterministic Python hash generation, helping ensure stable cache key generation and consistent prefix hashing behavior across workers and restarts.",{"data":12682,"content":12683,"nodeType":12173},{},[12684],{"data":12685,"content":12686,"nodeType":1920},{},[12687,12692],{"data":12688,"marks":12689,"value":12691,"nodeType":1906},{},[12690],{"type":12205},"VLLM_ATTENTION_BACKEND=FLASH_ATTN\n",{"data":12693,"marks":12694,"value":12695,"nodeType":1906},{},[]," Configures vLLM to use the FlashAttention backend for optimized attention computation, improving throughput and reducing memory overhead during inference.",{"data":12697,"content":12698,"nodeType":12305},{},[12699],{"data":12700,"marks":12701,"value":12702,"nodeType":1906},{},[],"\nStep 5: Serve vLLM",{"data":12704,"content":12721,"nodeType":12374},{"target":12705},{"metadata":12706,"sys":12709,"fields":12718},{"tags":12707,"concepts":12708},[],[],{"space":12710,"id":12712,"type":14,"createdAt":12713,"updatedAt":12713,"environment":12714,"publishedVersion":846,"revision":1135,"contentType":12716,"locale":672},{"sys":12711},{"type":656,"linkType":657,"id":658},"2Ox46unYUEjkmthM5d2yBK","2026-07-08T00:09:37.975Z",{"sys":12715},{"id":664,"type":656,"linkType":665},{"sys":12717},{"type":656,"linkType":670,"id":12367},{"entryTitle":12719,"code":12720,"language":12371,"theme":12372},"Code: Serve vLLM","model=\u002Fmnt\u002Fkvcache\u002Fmodels\u002FLlama-3.1-8B-Instruct\u002F\nvllm serve $model \\\n --kv-transfer-config '{\"kv_connector\":\"LMCacheConnectorV1\",\"kv_role\":\"kv_both\"}' --gpu-memory-utilization 0.85 --enable-prefix-caching --tensor-parallel-size 2",[],{"data":12723,"content":12724,"nodeType":12305},{},[12725],{"data":12726,"marks":12727,"value":12728,"nodeType":1906},{},[],"\n\nStep 6: LMCache Benchmarking",{"data":12730,"content":12731,"nodeType":1920},{},[12732],{"data":12733,"marks":12734,"value":12735,"nodeType":1906},{},[],"Create the following YAML configuration file named long-doc-qa.yaml for the LMCache Bench workload test:",{"data":12737,"content":12754,"nodeType":12374},{"target":12738},{"metadata":12739,"sys":12742,"fields":12751},{"tags":12740,"concepts":12741},[],[],{"space":12743,"id":12745,"type":14,"createdAt":12746,"updatedAt":12746,"environment":12747,"publishedVersion":846,"revision":1135,"contentType":12749,"locale":672},{"sys":12744},{"type":656,"linkType":657,"id":658},"3zKier4iSVbTLXowEvekMy","2026-07-08T00:08:19.554Z",{"sys":12748},{"id":664,"type":656,"linkType":665},{"sys":12750},{"type":656,"linkType":670,"id":12367},{"entryTitle":12752,"code":12753,"language":12371,"theme":12372},"Code:  LMCache Benchmarking","{\n  \"workload\": \"long-doc-qa\",\n  \"tokens_per_gb_kvcache\": 16384,\n  \"model\": \"\u002Fmnt\u002Fkvcache\u002Fmodels\u002FLlama-3.1-8B-Instruct\u002F\",\n  \"kv_cache_volume\": 100.0,\n  \"ldqa_document_length\": 40000,\n  \"ldqa_query_per_document\": 2,\n  \"ldqa_shuffle_policy\": \"tile\",\n  \"ldqa_num_inflight_requests\": 12\n} ",[],{"data":12756,"content":12757,"nodeType":1920},{},[12758],{"data":12759,"marks":12760,"value":12761,"nodeType":1906},{},[],"Run the LMCache benchmark using the long-doc-qa workload.",{"data":12763,"content":12764,"nodeType":1920},{},[12765],{"data":12766,"marks":12767,"value":12768,"nodeType":1906},{},[],"This workload simulates repeated question-and-answer requests over large synthetic documents:",{"data":12770,"content":12771,"nodeType":12184},{},[12772,12782],{"data":12773,"content":12774,"nodeType":12173},{},[12775],{"data":12776,"content":12777,"nodeType":1920},{},[12778],{"data":12779,"marks":12780,"value":12781,"nodeType":1906},{},[],"An initial warmup phase populates the KV cache for each document.",{"data":12783,"content":12784,"nodeType":12173},{},[12785],{"data":12786,"content":12787,"nodeType":1920},{},[12788],{"data":12789,"marks":12790,"value":12791,"nodeType":1906},{},[],"The benchmark phase then issues additional queries against the cached prefixes to measure KV reuse efficiency.",{"data":12793,"content":12794,"nodeType":1920},{},[12795],{"data":12796,"marks":12797,"value":12798,"nodeType":1906},{},[],"The total number of generated documents is automatically derived from:",{"data":12800,"content":12801,"nodeType":12184},{},[12802,12812,12822],{"data":12803,"content":12804,"nodeType":12173},{},[12805],{"data":12806,"content":12807,"nodeType":1920},{},[12808],{"data":12809,"marks":12810,"value":12811,"nodeType":1906},{},[],"kv_cache_volume",{"data":12813,"content":12814,"nodeType":12173},{},[12815],{"data":12816,"content":12817,"nodeType":1920},{},[12818],{"data":12819,"marks":12820,"value":12821,"nodeType":1906},{},[],"tokens_per_gb_kvcache",{"data":12823,"content":12824,"nodeType":12173},{},[12825],{"data":12826,"content":12827,"nodeType":1920},{},[12828],{"data":12829,"marks":12830,"value":12831,"nodeType":1906},{},[],"ldqa_document_length",{"data":12833,"content":12834,"nodeType":1920},{},[12835],{"data":12836,"marks":12837,"value":12838,"nodeType":1906},{},[],"rather than being configured explicitly.",{"data":12840,"content":12841,"nodeType":1920},{},[12842],{"data":12843,"marks":12844,"value":12845,"nodeType":1906},{},[],"In this example:",{"data":12847,"content":12848,"nodeType":12184},{},[12849,12859,12869],{"data":12850,"content":12851,"nodeType":12173},{},[12852],{"data":12853,"content":12854,"nodeType":1920},{},[12855],{"data":12856,"marks":12857,"value":12858,"nodeType":1906},{},[],"kv_cache_volume: 100 represents a logical 100GB KV cache working set.",{"data":12860,"content":12861,"nodeType":12173},{},[12862],{"data":12863,"content":12864,"nodeType":1920},{},[12865],{"data":12866,"marks":12867,"value":12868,"nodeType":1906},{},[],"tokens_per_gb_kvcache: 16384 estimates how many tokens fit into 1GB of KV cache for this model. (\"tokens_per_gb_kvcache\": 16384 = GPU KV cache size tokens: 928,128 \u002F Available KV cache memory: 56.65 GiB)",{"data":12870,"content":12871,"nodeType":12173},{},[12872],{"data":12873,"content":12874,"nodeType":1920},{},[12875],{"data":12876,"marks":12877,"value":12878,"nodeType":1906},{},[],"With 40k-token documents, the benchmark automatically generates approximately 40 documents and issues 2 queries per 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Without caching, request latency continuously grows under large 40k-token prefills, quickly saturating the system leading to longer wait times for end user and agent queries. With LMCache and the VAST AI OS ",{"data":13272,"content":13274,"nodeType":1915},{"uri":13273},"https:\u002F\u002Fkb.vastdata.com\u002Fdocumentation\u002Fdocs\u002Foverview-of-vast-cluster-s3-implementation-3",[13275],{"data":13276,"marks":13277,"value":13278,"nodeType":1906},{},[],"high performance S3 backend",{"data":13280,"marks":13281,"value":13282,"nodeType":1906},{},[]," enabled, repeated prefixes are served directly from cache, collapsing latency from ~37s average down to ~9s and maintaining consistently fast response times. ",{"data":13284,"content":13285,"nodeType":1920},{},[13286],{"data":13287,"marks":13288,"value":13289,"nodeType":1906},{},[],"Once the cache is warm, the workload shifts from expensive prefill computation to lightweight decode execution, delivering dramatically higher throughput and near-interactive responsiveness.",{"data":13291,"content":13311,"nodeType":12909},{"target":13292},{"metadata":13293,"sys":13296,"fields":13303},{"tags":13294,"concepts":13295},[],[],{"space":13297,"id":13299,"type":933,"createdAt":13300,"updatedAt":13300,"environment":13301,"publishedVersion":846,"revision":1135,"locale":672},{"sys":13298},{"type":656,"linkType":657,"id":658},"6PsRZHnJHMImPCw3N3yFqf","2026-07-07T23:55:33.683Z",{"sys":13302},{"id":664,"type":656,"linkType":665},{"title":13304,"description":2030,"file":13305},"Inline 2",{"url":13306,"details":13307,"fileName":13310,"contentType":4469},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6PsRZHnJHMImPCw3N3yFqf\u002Fee8bc0507b226176e198ed6f295e8d66\u002Fimg2.png",{"size":13308,"image":13309},48599,{"width":12906,"height":6477},"img2.png",[],{"data":13313,"content":13314,"nodeType":1920},{},[13315],{"data":13316,"marks":13317,"value":13318,"nodeType":1906},{},[],"We are using a 1MB block size for the S3-over-TCP workload and achieving a peak throughput of 9.79 GB\u002Fs with only two RTX 6000 GPUs, reaching approximately 80% of line rate.",{"data":13320,"content":13321,"nodeType":12207},{},[13322],{"data":13323,"marks":13324,"value":13326,"nodeType":1906},{},[13325],{"type":12205},"Key Takeaways from the S3 + NIXL Architecture",{"data":13328,"content":13329,"nodeType":12184},{},[13330,13345,13360],{"data":13331,"content":13332,"nodeType":12173},{},[13333],{"data":13334,"content":13335,"nodeType":1920},{},[13336,13341],{"data":13337,"marks":13338,"value":13340,"nodeType":1906},{},[13339],{"type":12205},"S3 Latency is Dead:",{"data":13342,"marks":13343,"value":13344,"nodeType":1906},{},[]," Hitting a 0.79 minimum TTFT proves that VAST's high-performance object storage acts like local memory, completely bypassing the typical HTTP\u002FS3 handshake and retrieval penalties.",{"data":13346,"content":13347,"nodeType":12173},{},[13348],{"data":13349,"content":13350,"nodeType":1920},{},[13351,13356],{"data":13352,"marks":13353,"value":13355,"nodeType":1906},{},[13354],{"type":12205},"Massive Concurrency Protection:",{"data":13357,"marks":13358,"value":13359,"nodeType":1906},{},[]," Under heavy prefill pressure, dropping peak TTFT from 36.78 down to 8.85 prevents the engine from cascading into an unrecoverable request queue backlog.",{"data":13361,"content":13362,"nodeType":12173},{},[13363],{"data":13364,"content":13365,"nodeType":1920},{},[13366,13371],{"data":13367,"marks":13368,"value":13370,"nodeType":1906},{},[13369],{"type":12205},"620% more tokens at full throttle",{"data":13372,"marks":13373,"value":13374,"nodeType":1906},{},[]," - Because the engine skips the heavy lifting of prefix evaluation, the GPUs get a 6.2x decode boost that immediately magnifies token output.",{"data":13376,"content":13377,"nodeType":1920},{},[13378],{"data":13379,"marks":13380,"value":13381,"nodeType":1906},{},[],"During warmup write bandwidth holds steady at about 1 GB\u002Fs as the system populates the cache. Each document's KV state is computed once and persisted to S3. Once the cache is warm, the workload flips to read-dominated: as queries hit the cached prefixes, KV state is pulled back from S3 in a series of high-throughput bursts, peaking at 9.79 GB\u002Fs (9,794 MB\u002Fs) read bandwidth. ",{"data":13383,"content":13384,"nodeType":1920},{},[13385],{"data":13386,"marks":13387,"value":13388,"nodeType":1906},{},[],"At that peak, writes have dropped to under 1 MB\u002Fs, as the system is doing almost pure reads. With a 1 MB transfer block size over S3-over-TCP, this reaches roughly 80% of 100 GbE line rate using only two RTX 6000 GPUs.",{"data":13390,"content":13391,"nodeType":1920},{},[13392],{"data":13393,"marks":13394,"value":13395,"nodeType":1906},{},[],"This read\u002Fwrite asymmetry is exactly the profile a KV cache should have - written once, read many times - and the bursty read pattern maps directly to batches of queries arriving against the cache. Between bursts, the GPUs are decoding rather than waiting on storage. ",{"data":13397,"content":13398,"nodeType":1920},{},[13399],{"data":13400,"marks":13401,"value":13402,"nodeType":1906},{},[],"The headline takeaway is efficiency: a single modest two-GPU node drives the storage and network layer to near line rate - meaning the compute, not the storage backend, sets the ceiling. As GPUs and NICs are added, the same architecture has obvious headroom to scale.",{"data":13404,"content":13405,"nodeType":1920},{},[13406],{"data":13407,"marks":13408,"value":13409,"nodeType":1906},{},[],"A similar data structure to this will be created:",{"data":13411,"content":13428,"nodeType":12374},{"target":13412},{"metadata":13413,"sys":13416,"fields":13425},{"tags":13414,"concepts":13415},[],[],{"space":13417,"id":13419,"type":14,"createdAt":13420,"updatedAt":13420,"environment":13421,"publishedVersion":846,"revision":1135,"contentType":13423,"locale":672},{"sys":13418},{"type":656,"linkType":657,"id":658},"6Xmvne6M35AP2qrLxPOgy3","2026-07-08T00:06:23.911Z",{"sys":13422},{"id":664,"type":656,"linkType":665},{"sys":13424},{"type":656,"linkType":670,"id":12367},{"entryTitle":13426,"code":13427,"language":12371,"theme":12372},"Code - data structure ","...\n2026-05-18 15:24:23   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_f59976a188d5671_bfloat16\n2026-05-18 15:26:38   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_f7f22757bdd1700_bfloat16\n2026-05-18 15:26:25   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_f91e3cd732b8302_bfloat16\n2026-05-18 15:26:53   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_fa70bc4b8db540b_bfloat16\n2026-05-18 15:25:43   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_fb9217af340d519_bfloat16\n2026-05-18 15:25:47   64.0 MiB _mnt_kvcache_models_Llama-3.1-8B-Instruct__2_1_fdf85844cf436b1_bfloat16\n\nTotal Objects: 2934\n   Total Size: 183.4 GiB\n",[],{"data":13430,"content":13431,"nodeType":1920},{},[13432],{"data":13433,"marks":13434,"value":13435,"nodeType":1906},{},[],"And can be removed with parallel cmd:",{"data":13437,"content":13438,"nodeType":1920},{},[13439],{"data":13440,"marks":13441,"value":13443,"nodeType":1906},{},[13442],{"type":12423},"S3cmd ls s3:\u002F\u002Fkvc | awk '{print $4}' | parallel -j 96 s3cmd del {}",{"data":13445,"content":13446,"nodeType":1920},{},[13447],{"data":13448,"marks":13449,"value":2030,"nodeType":1906},{},[],{"data":13451,"content":13452,"nodeType":12207},{},[13453],{"data":13454,"marks":13455,"value":13456,"nodeType":1906},{},[],"Why It Matters",{"data":13458,"content":13459,"nodeType":1920},{},[13460,13464,13472],{"data":13461,"marks":13462,"value":13463,"nodeType":1906},{},[],"At ",{"data":13465,"content":13467,"nodeType":1915},{"uri":13466},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fa-new-memory-tier-for-ai-at-scale",[13468],{"data":13469,"marks":13470,"value":13471,"nodeType":1906},{},[],"inference scale",{"data":13473,"marks":13474,"value":13475,"nodeType":1906},{},[],", latency and GPU count are two sides of the same coin. Every second a GPU spends recomputing a prefix it has already seen is a second it isn't serving new tokens.",{"data":13477,"content":13478,"nodeType":1920},{},[13479],{"data":13480,"marks":13481,"value":13482,"nodeType":1906},{},[],"Redundant prefill doesn't just slow individual requests, it inflates the number of GPUs you need to hold a given latency SLA. By serving shared prefixes from cache instead of recomputing them, LMCache on the VAST AI OS S3 backend cut average request latency from ~37s to ~9s - a roughly 3.9x improvement. ",{"data":13484,"content":13485,"nodeType":1920},{},[13486],{"data":13487,"marks":13488,"value":13489,"nodeType":1906},{},[],"At a fixed SLA, that reclaimed GPU time translates fairly directly into capacity: the same node sustains far more concurrent requests, or the same workload runs on a fraction of the GPUs. For RAG, agentic, and multi-turn workloads - where prefix reuse is the rule rather than the exception - that's the difference between scaling out hardware and scaling up efficiency.",{"data":13491,"content":13492,"nodeType":12207},{},[13493],{"data":13494,"marks":13495,"value":13497,"nodeType":1906},{},[13496],{"type":12205},"\nIn summary:",{"data":13499,"content":13500,"nodeType":1920},{},[13501,13505,13511],{"data":13502,"marks":13503,"value":13504,"nodeType":1906},{},[],"These results demonstrate how combining vLLM with LMCache and the ",{"data":13506,"content":13507,"nodeType":1915},{"uri":11214},[13508],{"data":13509,"marks":13510,"value":1188,"nodeType":1906},{},[],{"data":13512,"marks":13513,"value":13514,"nodeType":1906},{},[]," on a high-performance S3 backend can significantly accelerate inference performance for long-context workloads. By leveraging LMCache for KV-cache acceleration over cost-optimized S3\u002FTCP, the system avoids redundant GPU prefill computation, reducing GPU time per request with 3.9x reduction in end-to-end response latency and increasing average decode speed by 6.2X. ",{"data":13516,"content":13517,"nodeType":1920},{},[13518],{"data":13519,"marks":13520,"value":13521,"nodeType":1906},{},[],"For long-context, high-reuse inference patterns - common in RAG systems, agentic workflows, and multi-turn question answering - this architecture frees up GPU capacity allowing AI cloud providers and large enterprise AI infrastructure to host more concurrent user sessions per GPU, shorten the wait time for end users, and ultimately provides a compelling approach to improving both performance and infrastructure efficiency for large inference workloads. ",{"data":13523,"content":13524,"nodeType":1920},{},[13525],{"data":13526,"marks":13527,"value":13528,"nodeType":1906},{},[],"Performance and cost efficiency are critical priorities for large scale production inference providers. VAST provides options to optimize both, but we also go beyond lowering TTFT and maximizing infrastructure efficiency. The VAST AI OS provides KV cache data enterprise data services such as life cycle management, encryption, and data reduction. These features aren’t turned off to speed up performance; they are always on and always providing critical data services to our customers.",{"data":13530,"content":13531,"nodeType":12207},{},[13532],{"data":13533,"marks":13534,"value":13535,"nodeType":1906},{},[],"\nWhat’s Next",{"data":13537,"content":13538,"nodeType":1920},{},[13539],{"data":13540,"marks":13541,"value":13542,"nodeType":1906},{},[],"Keep an eye on our upcoming blogs for further insights and practical guides on optimizing KV Cache performance with LMCache, GPUDirect Storage, and other emerging architectural solutions in the market. ",{"data":13544,"content":13545,"nodeType":1920},{},[13546,13550,13558],{"data":13547,"marks":13548,"value":13549,"nodeType":1906},{},[],"VAST is set to introduce support for 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",{"data":13581,"content":13582,"nodeType":1915},{"uri":12491},[13583],{"data":13584,"marks":13585,"value":12496,"nodeType":1906},{},[],{"data":13587,"marks":13588,"value":13589,"nodeType":1906},{},[]," and on a more advanced product named Tensormesh Operator that will potentially benefit any VAST customers.  We’ll keep you informed once they release their product in the coming 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Cache-Feature_VAST-Data.jpg",{"data":13857,"content":13858,"nodeType":1921},{},[13859,13867,13874,13882,13899,13906,13913,13920,13927,13999,14010,14018,14025,14037,14049,14061,14073,14080,14104,14112,14131,14138,14161,14168,14176,14183,14190,14202,14214,14226,14234,14241,14248,14260,14272,14284,14296,14308,14320,14328,14347,14354,14361,14383,14391,14450,14462,14474,14486,14498,14506,14513,14525,14537,14549,14561,14573,14580,14592,14604,14627,14635,14654,14672,14680],{"data":13860,"content":13861,"nodeType":12207},{},[13862],{"data":13863,"marks":13864,"value":13866,"nodeType":1906},{},[13865],{"type":12205},"The Infrastructure Challenge of the Agentic Era",{"data":13868,"content":13869,"nodeType":1920},{},[13870],{"data":13871,"marks":13872,"value":13873,"nodeType":1906},{},[],"As the use of LLMs evolves from simple prompts into complex, multi-turn \"agentic\" workflows, the underlying infrastructure must adapt. AI-native organizations and neoclouds now face a daunting task: supporting context windows that extend to millions of tokens while maintaining strict SLAs, as well as provide important data services including secure multi-tenancy, data reduction, and encryption both at rest and on transit.The primary barrier to this scale is the GPU HBM (high bandwidth memory), which is expensive and limited to holding the large, dynamic KV cache required for long context agentic processing. Every new turn, tool-call, and promptrequires the model to re-process the entire conversation history, wasting critical GPU cycles on \"prefill\" for data it has already seen. This recomputation slows Time-to-First-Token (TTFT), degrading user experience and impacting revenue streams.",{"data":13875,"content":13876,"nodeType":12207},{},[13877],{"data":13878,"marks":13879,"value":13881,"nodeType":1906},{},[13880],{"type":12205},"Why the KV Cache is Critical",{"data":13883,"content":13884,"nodeType":1920},{},[13885,13888,13895],{"data":13886,"marks":13887,"value":2030,"nodeType":1906},{},[],{"data":13889,"content":13890,"nodeType":1915},{"uri":12217},[13891],{"data":13892,"marks":13893,"value":13894,"nodeType":1906},{},[],"Building the KV cache is compute-heavy",{"data":13896,"marks":13897,"value":13898,"nodeType":1906},{},[],", and it gets heavier as context grows - prefill scales with input length, and attention cost grows faster still. Being able to cache that work once and reuse it is enormously valuable: across the turns of a single conversation, across an agent’s tool-calling loop, or across users who share a common system prompt or retrieved document.",{"data":13900,"content":13901,"nodeType":1920},{},[13902],{"data":13903,"marks":13904,"value":13905,"nodeType":1906},{},[],"The catch is where it lives. GPU HBM is fast but small and expensive, so it can’t hold long histories for many concurrent sessions. When a session pauses, the cache is typically evicted and the next request pays a full cold-start prefill. To support long-context workloads like AI-assisted coding or large-document analysis, you need a way to park that memory cheaply and retrieve it at speed.",{"data":13907,"content":13908,"nodeType":1920},{},[13909],{"data":13910,"marks":13911,"value":13912,"nodeType":1906},{},[],"One point worth keeping in mind: KV reuse only ever helps the prefill stage (and therefore TTFT). Prefill processes the input tokens, which can recur across requests, so their KV can be computed once, stored, and retrieved later. Decode is fundamentally different: it generates new tokens one at a time, and each token’s K\u002FV has never existed before this request. It is new data, so there is nothing in any cache to retrieve - and decode is bound by memory bandwidth, not compute. ",{"data":13914,"content":13915,"nodeType":1920},{},[13916],{"data":13917,"marks":13918,"value":13919,"nodeType":1906},{},[],"Benefits of KV cache offload are therefore about reaching the first token faster and reclaiming prefill compute, not about speeding up the token-by-token generation that follows.",{"data":13921,"content":13922,"nodeType":1920},{},[13923],{"data":13924,"marks":13925,"value":13926,"nodeType":1906},{},[],"Solving this requires a coordinated strategy across three pillars:",{"data":13928,"content":13929,"nodeType":13998},{},[13930,13957,13983],{"data":13931,"content":13932,"nodeType":12173},{},[13933],{"data":13934,"content":13935,"nodeType":1920},{},[13936,13941,13945,13953],{"data":13937,"marks":13938,"value":13940,"nodeType":1906},{},[13939],{"type":12205},"Software frameworks",{"data":13942,"marks":13943,"value":13944,"nodeType":1906},{},[]," - KV cache managers like ",{"data":13946,"content":13948,"nodeType":1915},{"uri":13947},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-dynamo-vast-scalable-optimized-inference",[13949],{"data":13950,"marks":13951,"value":13952,"nodeType":1906},{},[],"NVIDIA Dynamo KVBM",{"data":13954,"marks":13955,"value":13956,"nodeType":1906},{},[],", LMCache, or SGLang (with HiCache) that decide what to keep, when to load it, and how to match it.",{"data":13958,"content":13959,"nodeType":12173},{},[13960],{"data":13961,"content":13962,"nodeType":1920},{},[13963,13968,13972,13979],{"data":13964,"marks":13965,"value":13967,"nodeType":1906},{},[13966],{"type":12205},"Hardware architecture ",{"data":13969,"marks":13970,"value":13971,"nodeType":1906},{},[],"- a fast fabric (RDMA \u002F RoCE, ",{"data":13973,"content":13974,"nodeType":1915},{"uri":12131},[13975],{"data":13976,"marks":13977,"value":13978,"nodeType":1906},{},[],"GPUDirect Storage",{"data":13980,"marks":13981,"value":13982,"nodeType":1906},{},[],") so any GPU can reach any KV block at line rate, plus the right single-node vs. multi-node and disaggregated-serving topology.",{"data":13984,"content":13985,"nodeType":12173},{},[13986],{"data":13987,"content":13988,"nodeType":1920},{},[13989,13994],{"data":13990,"marks":13991,"value":13993,"nodeType":1906},{},[13992],{"type":12205},"A persistent, shared data platform",{"data":13995,"marks":13996,"value":13997,"nodeType":1906},{},[]," - the tier that actually stores the cache so it survives evictions and is reachable across the whole cluster. This is the pillar most teams under-plan, and it’s where VAST fits.","ordered-list",{"data":14000,"content":14001,"nodeType":14009},{},[14002],{"data":14003,"content":14004,"nodeType":1920},{},[14005],{"data":14006,"marks":14007,"value":14008,"nodeType":1906},{},[],"Increasingly, these layers are becoming more modular: cache managers such as LMCache or Dynamo KVBM decide what KV state should be stored or retrieved, while data transport frameworks such as NVIDIA NIXL optimize how that data moves between GPUs, memory tiers, and shared storage.","blockquote",{"data":14011,"content":14012,"nodeType":12207},{},[14013],{"data":14014,"marks":14015,"value":14017,"nodeType":1906},{},[14016],{"type":12205},"Deciding on Software Frameworks",{"data":14019,"content":14020,"nodeType":1920},{},[14021],{"data":14022,"marks":14023,"value":14024,"nodeType":1906},{},[],"The right framework depends on various considerations, including:",{"data":14026,"content":14027,"nodeType":1920},{},[14028,14033],{"data":14029,"marks":14030,"value":14032,"nodeType":1906},{},[14031],{"type":12205},"Existing and projected hardware",{"data":14034,"marks":14035,"value":14036,"nodeType":1906},{},[]," - including heterogeneous fleets: mixing vendors or new and old GPUs across prefill\u002Fdecode stages)",{"data":14038,"content":14039,"nodeType":1920},{},[14040,14045],{"data":14041,"marks":14042,"value":14044,"nodeType":1906},{},[14043],{"type":12205},"Scale and topology",{"data":14046,"marks":14047,"value":14048,"nodeType":1906},{},[]," - single vs. multi-node and aggregated vs. disaggregated serving",{"data":14050,"content":14051,"nodeType":1920},{},[14052,14057],{"data":14053,"marks":14054,"value":14056,"nodeType":1906},{},[14055],{"type":12205},"Workload pattern",{"data":14058,"marks":14059,"value":14060,"nodeType":1906},{},[]," - are inputs\u002Foutputs long and reuse-heavy? Do sessions resume hours or days later?",{"data":14062,"content":14063,"nodeType":1920},{},[14064,14069],{"data":14065,"marks":14066,"value":14068,"nodeType":1906},{},[14067],{"type":12205},"Model-coverage needs",{"data":14070,"marks":14071,"value":14072,"nodeType":1906},{},[]," - Do you adopt new models the day they drop, or prioritize validated, stable workloads?",{"data":14074,"content":14075,"nodeType":1920},{},[14076],{"data":14077,"marks":14078,"value":14079,"nodeType":1906},{},[],"All of these frameworks converge on the same memory hierarchy and NVIDIA developed names for each layer: GPU HBM at the top (G1 layer), then host DRAM (G2), then local SSD (G3), then networked storage (G4). The top tiers are private to an instance and the G4 tier is shared across the entire cluster. At the G1 layer, GPU memory is the most expensive and offers the highest in performance. At the G4 layer, shared storage is the least expensive and relatively slowest in performance. 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More on that in the section on NVIDIA CMX.",{"data":14081,"content":14103,"nodeType":12909},{"target":14082},{"metadata":14083,"sys":14086,"fields":14093},{"tags":14084,"concepts":14085},[],[],{"space":14087,"id":14089,"type":933,"createdAt":14090,"updatedAt":14090,"environment":14091,"publishedVersion":691,"revision":1135,"locale":672},{"sys":14088},{"type":656,"linkType":657,"id":658},"3rcB7mBcBTWw0gpVV19xKd","2026-07-06T16:42:01.764Z",{"sys":14092},{"id":664,"type":656,"linkType":665},{"title":14094,"description":2030,"file":14095},"Inline 1: Right-Sizing KV Cache",{"url":14096,"details":14097,"fileName":14102,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3rcB7mBcBTWw0gpVV19xKd\u002Fc72b9258c3b9872cf2a7f0f4bf67d606\u002FCR-11908-Inline_Graphic-BLOG-Practical_Guide-05.jpg",{"size":14098,"image":14099},1407754,{"width":14100,"height":14101},8001,2653,"CR-11908-Inline Graphic-BLOG-Practical Guide-05.jpg",[],{"data":14105,"content":14106,"nodeType":12305},{},[14107],{"data":14108,"marks":14109,"value":14111,"nodeType":1906},{},[14110],{"type":12205},"Hardware Architecture Options",{"data":14113,"content":14114,"nodeType":1920},{},[14115,14119,14127],{"data":14116,"marks":14117,"value":14118,"nodeType":1906},{},[],"Key variables include bandwidth and scale. What GPU-to-GPU and GPU-to-storage bandwidth do you have? VAST testing has found that most KV offload operations are network-limited, not storage-limited - so the fabric (RDMA\u002FRoCE, GPUDirect Storage, and accelerators like ",{"data":14120,"content":14122,"nodeType":1915},{"uri":14121},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-spectrum-x-and-vast-data-platform",[14123],{"data":14124,"marks":14125,"value":14126,"nodeType":1906},{},[],"NVIDIA Spectrum-X",{"data":14128,"marks":14129,"value":14130,"nodeType":1906},{},[],") matters more than raw disk speed. For mass-scale deployments, dedicated context-memory infrastructure (e.g., NVIDIA’s upcoming CMX reference design) becomes relevant.",{"data":14132,"content":14133,"nodeType":1920},{},[14134],{"data":14135,"marks":14136,"value":14137,"nodeType":1906},{},[],"Which infrastructure do you actually need? Walk this from the top - each “no” is the lightest setup that works for that case, and following the “yes” path adds what cross-node reuse and scale demand:",{"data":14139,"content":14160,"nodeType":12909},{"target":14140},{"metadata":14141,"sys":14144,"fields":14151},{"tags":14142,"concepts":14143},[],[],{"space":14145,"id":14147,"type":933,"createdAt":14148,"updatedAt":14148,"environment":14149,"publishedVersion":846,"revision":1135,"locale":672},{"sys":14146},{"type":656,"linkType":657,"id":658},"3OzTa7fg0gAPOH7vfhe2JT","2026-07-06T16:42:46.474Z",{"sys":14150},{"id":664,"type":656,"linkType":665},{"title":14152,"description":2030,"file":14153},"Inline 2: Right-Sizing KV Cache",{"url":14154,"details":14155,"fileName":14159,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3OzTa7fg0gAPOH7vfhe2JT\u002F24dd0a5fd56b7d9d9a47275fc08c07dd\u002FCR-11908-Inline_Graphic-BLOG-Practical_Guide-06.jpg",{"size":14156,"image":14157},3567466,{"width":14100,"height":14158},4501,"CR-11908-Inline Graphic-BLOG-Practical Guide-06.jpg",[],{"data":14162,"content":14163,"nodeType":1920},{},[14164],{"data":14165,"marks":14166,"value":14167,"nodeType":1906},{},[],"All the lower outcomes run on VAST. Loading KV from shared storage beats recompute even over NFS-over-TCP, which can saturate a GPU’s I\u002FO on most networks; RDMA\u002FRoCE with GPUDirect Storage is an optimization that lowers latency, frees host CPU, and loads KV straight into GPU memory; mass scale then layers on a dedicated context tier, KV-aware routing, and disaggregated prefill\u002Fdecode.",{"data":14169,"content":14170,"nodeType":12207},{},[14171],{"data":14172,"marks":14173,"value":14175,"nodeType":1906},{},[14174],{"type":12205},"Challenges of Sizing of KV$",{"data":14177,"content":14178,"nodeType":1920},{},[14179],{"data":14180,"marks":14181,"value":14182,"nodeType":1906},{},[],"Conventional storage sizing methodologies rely on a predictable workload as the primary input. In the absence of this data, the entire framework collapses, and attempting to force a precise calculation often leads to incorrect outcomes.This is not a failure of planning, but rather an intrinsic challenge of deploying AI infrastructure before specific workloads are fully understood. Acknowledging this uncertainty is the critical first step toward building a defensible infrastructure strategy that can scale with evolving demands. This is important in order to support various challenges and requirements such as: ",{"data":14184,"content":14185,"nodeType":1920},{},[14186],{"data":14187,"marks":14188,"value":14189,"nodeType":1906},{},[],"A fundamental requirement for modern AI infrastructure is the selection of a flexible architecture capable of scaling alongside evolving workloads. Relying on rigid, single-purpose solutions presents significant risks throughout the operational lifespan of a GPU investment. Constructing a defensible infrastructure strategy allows organizations to effectively address key operational challenges, including:",{"data":14191,"content":14192,"nodeType":1920},{},[14193,14198],{"data":14194,"marks":14195,"value":14197,"nodeType":1906},{},[14196],{"type":12205},"Seamless Scalability:",{"data":14199,"marks":14200,"value":14201,"nodeType":1906},{},[]," Facilitating mid-life expansions and upgrades without incurring performance degradation or system downtime.",{"data":14203,"content":14204,"nodeType":1920},{},[14205,14210],{"data":14206,"marks":14207,"value":14209,"nodeType":1906},{},[14208],{"type":12205},"Heterogeneous Support:",{"data":14211,"marks":14212,"value":14213,"nodeType":1906},{},[]," Enabling the integration of diverse server types, storage media, and multiple technology generations within a unified cluster.",{"data":14215,"content":14216,"nodeType":1920},{},[14217,14222],{"data":14218,"marks":14219,"value":14221,"nodeType":1906},{},[14220],{"type":12205},"Operational Resilience:",{"data":14223,"marks":14224,"value":14225,"nodeType":1906},{},[]," Ensuring non-disruptive firmware and software updates, alongside the ability to perform network infrastructure upgrades without service interruption.",{"data":14227,"content":14228,"nodeType":12207},{},[14229],{"data":14230,"marks":14231,"value":14233,"nodeType":1906},{},[14232],{"type":12205},"The Data Platform: VAST as the Shared KV Cache Tier",{"data":14235,"content":14236,"nodeType":1920},{},[14237],{"data":14238,"marks":14239,"value":14240,"nodeType":1906},{},[],"This is the third pillar. VAST does not replace your inference framework; it sits underneath all of them as the same persistent, shared bottom tier they offload to. The framework owns the cache logic; VAST owns persistence, capacity, cluster-wide sharing, and the data-platform services a raw cache silo can’t provide. ",{"data":14242,"content":14243,"nodeType":1920},{},[14244],{"data":14245,"marks":14246,"value":14247,"nodeType":1906},{},[],"A production-grade KV tier has to deliver:",{"data":14249,"content":14250,"nodeType":1920},{},[14251,14256],{"data":14252,"marks":14253,"value":14255,"nodeType":1906},{},[14254],{"type":12205},"Expandable capacity at scale.",{"data":14257,"marks":14258,"value":14259,"nodeType":1906},{},[]," KV cache is gigabytes per prompt; across thousands of users and resumable sessions it reaches petabytes. The tier must grow elastically without re-architecting.",{"data":14261,"content":14262,"nodeType":1920},{},[14263,14268],{"data":14264,"marks":14265,"value":14267,"nodeType":1906},{},[14266],{"type":12205},"Line-rate bandwidth, low latency.",{"data":14269,"marks":14270,"value":14271,"nodeType":1906},{},[]," Because offload is network-limited, the tier must saturate the GPU’s I\u002FO. Over RDMA\u002FRoCE with GPUDirect Storage, VAST loads KV blocks directly into GPU memory. In testing, saturating a single H100 at ~25 GB\u002Fs, confirming storage was not the bottleneck.",{"data":14273,"content":14274,"nodeType":1920},{},[14275,14280],{"data":14276,"marks":14277,"value":14279,"nodeType":1906},{},[14278],{"type":12205},"Truly shared, global access.",{"data":14281,"marks":14282,"value":14283,"nodeType":1906},{},[]," Any GPU or node reads any KV block, so cache computed on one prefill worker can be reused by a decode worker or a different replica entirely. This is the difference between a private per-node tier (L2) and a shared cluster tier (L3\u002FG4).",{"data":14285,"content":14286,"nodeType":1920},{},[14287,14292],{"data":14288,"marks":14289,"value":14291,"nodeType":1906},{},[14290],{"type":12205},"Persistence.",{"data":14293,"marks":14294,"value":14295,"nodeType":1906},{},[]," The cache survives session pauses, evictions, and server restarts - eliminating cold starts across hours or days, not just within one process.",{"data":14297,"content":14298,"nodeType":1920},{},[14299,14304],{"data":14300,"marks":14301,"value":14303,"nodeType":1906},{},[14302],{"type":12205},"Enterprise data services.",{"data":14305,"marks":14306,"value":14307,"nodeType":1906},{},[]," Multi-tenancy and isolation, encryption at rest and in flight, audit and governance, and data reduction (deduplicating shared system prompts and common context). And it’s the same platform that already holds your training data, RAG corpora, and checkpoints with no separate context silo.",{"data":14309,"content":14310,"nodeType":1920},{},[14311,14316],{"data":14312,"marks":14313,"value":14315,"nodeType":1906},{},[14314],{"type":12205},"Framework independence.",{"data":14317,"marks":14318,"value":14319,"nodeType":1906},{},[]," VAST attaches under KVBM (as a G4 backend via NVIDIA NIXL and the GPUDirect Storage plugin), under SGLang HiCache (as an L3 backend), and under LMCache (as its remote backend). You’re not locked into one inference stack.",{"data":14321,"content":14322,"nodeType":12207},{},[14323],{"data":14324,"marks":14325,"value":14327,"nodeType":1906},{},[14326],{"type":12205},"Where NVIDIA CMX fits",{"data":14329,"content":14330,"nodeType":1920},{},[14331,14335,14343],{"data":14332,"marks":14333,"value":14334,"nodeType":1906},{},[],"NVIDIA CMX, based on ",{"data":14336,"content":14338,"nodeType":1915},{"uri":14337},"https:\u002F\u002Fresources.nvidia.com\u002Fen-us-accelerated-networking-resource-library\u002Fcmx-tech-blog",[14339],{"data":14340,"marks":14341,"value":14342,"nodeType":1906},{},[],"NVIDIA STX",{"data":14344,"marks":14345,"value":14346,"nodeType":1906},{},[]," foundation for AI-native storage, establishes a new “G3.5” tier in the KV hierarchy: a pod-level, Ethernet-attached flash tier for ephemeral, latency-sensitive KV cache, driven by the BlueField-4 storage processor and orchestrated by Dynamo, NIXL, and DOCA over Spectrum-X. It sits between node-local SSD (G3) and durable shared storage (G4), filling the KV capacity that G1–G3 can’t hold, so a pod’s agents share and reuse evolving context at near-memory latency without exhausting HBM.",{"data":14348,"content":14349,"nodeType":1920},{},[14350],{"data":14351,"marks":14352,"value":14353,"nodeType":1906},{},[],"NVIDIA’s DOCA interfaces let storage partners integrate as the CMX layer directly. VAST is built to be integrated as the G3.5 tier itself, not just the G4 layer beneath it. VAST support for the G3.5 tier is landing in upcoming Dynamo\u002FVAST releases. Integration with BlueField-4 DPUs enables unencumbered performance with zero-copy KV transfer from storage straight into GPU memory. ",{"data":14355,"content":14356,"nodeType":1920},{},[14357],{"data":14358,"marks":14359,"value":14360,"nodeType":1906},{},[],"The VAST AI OS also provides security, multi-tenancy, and lifecycle management into the context tier, while reducing the overall compute overhead by utilizing the BF4 compute resources. Because the same platform also spans G3 and G4, VAST can serve the hot, pod-level context tier and the durable, cross-pod persistence layer (inactive multi-turn KV state, query history, logs) as one system - with Dynamo and NIXL prestaging blocks up the hierarchy ahead of decode - rather than VAST being relegated to slow storage below CMX.",{"data":14362,"content":14382,"nodeType":12909},{"target":14363},{"metadata":14364,"sys":14367,"fields":14374},{"tags":14365,"concepts":14366},[],[],{"space":14368,"id":14370,"type":933,"createdAt":14371,"updatedAt":14371,"environment":14372,"publishedVersion":846,"revision":1135,"locale":672},{"sys":14369},{"type":656,"linkType":657,"id":658},"HAplJl81ZoQRenF2YzUri","2026-07-06T16:43:28.775Z",{"sys":14373},{"id":664,"type":656,"linkType":665},{"title":14375,"description":2030,"file":14376},"Inline 3: Right-Sizing KV Cache",{"url":14377,"details":14378,"fileName":14381,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FHAplJl81ZoQRenF2YzUri\u002Fa99576003692d3c3e3bfbdb669b04101\u002FCR-11908-Inline_Graphic-BLOG-Practical_Guide-07.jpg",{"size":14379,"image":14380},2781872,{"width":14100,"height":14158},"CR-11908-Inline Graphic-BLOG-Practical Guide-07.jpg",[],{"data":14384,"content":14385,"nodeType":12207},{},[14386],{"data":14387,"marks":14388,"value":14390,"nodeType":1906},{},[14389],{"type":12205},"VAST Data: The Unified Platform for AI Data AND KV Cache",{"data":14392,"content":14393,"nodeType":1920},{},[14394,14398,14405,14409,14417,14421,14429,14433,14441,14445],{"data":14395,"marks":14396,"value":14397,"nodeType":1906},{},[],"VAST Data has partnered closely with NVIDIA on ",{"data":14399,"content":14400,"nodeType":1915},{"uri":13947},[14401],{"data":14402,"marks":14403,"value":14404,"nodeType":1906},{},[],"NVIDIA Dynamo",{"data":14406,"marks":14407,"value":14408,"nodeType":1906},{},[]," to transform KV Cache from a temporary byproduct into a long-term enterprise asset. By leveraging the ",{"data":14410,"content":14412,"nodeType":1915},{"uri":14411},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fhow-it-works",[14413],{"data":14414,"marks":14415,"value":14416,"nodeType":1906},{},[],"VAST DASE architecture",{"data":14418,"marks":14419,"value":14420,"nodeType":1906},{},[],", VAST enables GPUs to offload context memory to a shared pool via ",{"data":14422,"content":14424,"nodeType":1915},{"uri":14423},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fmeet-your-need-for-speed-with-nfs",[14425],{"data":14426,"marks":14427,"value":14428,"nodeType":1906},{},[],"NFS over RDMA",{"data":14430,"marks":14431,"value":14432,"nodeType":1906},{},[]," (and soon ",{"data":14434,"content":14436,"nodeType":1915},{"uri":14435},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fthe-rise-of-s3-rdma",[14437],{"data":14438,"marks":14439,"value":14440,"nodeType":1906},{},[],"S3 over RDMA",{"data":14442,"marks":14443,"value":14444,"nodeType":1906},{},[],"). ",{"data":14446,"marks":14447,"value":14449,"nodeType":1906},{},[14448],{"type":12205},"The VAST Advantage Includes:",{"data":14451,"content":14452,"nodeType":1920},{},[14453,14458],{"data":14454,"marks":14455,"value":14457,"nodeType":1906},{},[14456],{"type":12205},"90% Reduction in GPU Compute Time:",{"data":14459,"marks":14460,"value":14461,"nodeType":1906},{},[]," By retrieving stored KV blocks, GPUs bypass the prefill stage entirely.",{"data":14463,"content":14464,"nodeType":1920},{},[14465,14470],{"data":14466,"marks":14467,"value":14469,"nodeType":1906},{},[14468],{"type":12205},"20x Faster TTFT:",{"data":14471,"marks":14472,"value":14473,"nodeType":1906},{},[]," Context is made available at network line rate, ensuring near-instant response times even for multi-million token windows.",{"data":14475,"content":14476,"nodeType":1920},{},[14477,14482],{"data":14478,"marks":14479,"value":14481,"nodeType":1906},{},[14480],{"type":12205},"Infrastructure Efficiency:",{"data":14483,"marks":14484,"value":14485,"nodeType":1906},{},[]," VAST tested and achieved 1.4x data reduction on KV Cache data, which allows providers to support more concurrent users and longer sessions with the same storage footprint.",{"data":14487,"content":14488,"nodeType":1920},{},[14489,14494],{"data":14490,"marks":14491,"value":14493,"nodeType":1906},{},[14492],{"type":12205},"Unmatched Security:",{"data":14495,"marks":14496,"value":14497,"nodeType":1906},{},[]," Unlike fragmented cache silos, VAST provides at-rest\u002Fon-the-wire encryption, multi-tenancy, and audit trails for regulated environments.",{"data":14499,"content":14500,"nodeType":12305},{},[14501],{"data":14502,"marks":14503,"value":14505,"nodeType":1906},{},[14504],{"type":12205},"Sizing Your Inference Pipeline",{"data":14507,"content":14508,"nodeType":1920},{},[14509],{"data":14510,"marks":14511,"value":14512,"nodeType":1906},{},[],"Determining the appropriate KV cache size for inference deployment requires a careful evaluation of several key variables:",{"data":14514,"content":14515,"nodeType":1920},{},[14516,14521],{"data":14517,"marks":14518,"value":14520,"nodeType":1906},{},[14519],{"type":12205},"Model Architecture and Specification:",{"data":14522,"marks":14523,"value":14524,"nodeType":1906},{},[]," The specific models being served dictate GPU selection, weight-related memory demands, required context length, and the KV bytes needed per token. The size of KV blocks is directly related to model architecture. ",{"data":14526,"content":14527,"nodeType":1920},{},[14528,14533],{"data":14529,"marks":14530,"value":14532,"nodeType":1906},{},[14531],{"type":12205},"Workload Distribution:",{"data":14534,"marks":14535,"value":14536,"nodeType":1906},{},[]," This is the relative percentage of sessions assigned to different use cases (translation, coding, agentic workflows, etc.), each of which carries unique context length demands. Simple text chat workloads generate less KV cache than multimodal agentic workloads. ",{"data":14538,"content":14539,"nodeType":1920},{},[14540,14545],{"data":14541,"marks":14542,"value":14544,"nodeType":1906},{},[14543],{"type":12205},"Concurrency Requirements:",{"data":14546,"marks":14547,"value":14548,"nodeType":1906},{},[]," This is the total volume of active sessions the infrastructure must support during peak load periods. More active sessions will require more KV cache offloads and reloads from a fixed pool of GPUs. ",{"data":14550,"content":14551,"nodeType":1920},{},[14552,14557],{"data":14553,"marks":14554,"value":14556,"nodeType":1906},{},[14555],{"type":12205},"Retention Strategies:",{"data":14558,"marks":14559,"value":14560,"nodeType":1906},{},[]," Policies defining the duration a session remains available for rapid retrieval, which helps minimize prefill penalties and improve GPU utilization.",{"data":14562,"content":14563,"nodeType":1920},{},[14564,14569],{"data":14565,"marks":14566,"value":14568,"nodeType":1906},{},[14567],{"type":12205},"Anticipated Outcomes:",{"data":14570,"marks":14571,"value":14572,"nodeType":1906},{},[]," Precise sizing enables organizations to project critical metrics, including TTFT acceleration, GPU-hour savings via minimized recomputation, expected cache hit rates, and the total storage needed for target retention periods.",{"data":14574,"content":14575,"nodeType":1920},{},[14576],{"data":14577,"marks":14578,"value":14579,"nodeType":1906},{},[],"To streamline production-ready capacity planning, VAST Data experts can walk you through a KV$ sizing analysis, designed to convert these operational variables into specific infrastructure recommendations:",{"data":14581,"content":14582,"nodeType":1920},{},[14583,14588],{"data":14584,"marks":14585,"value":14587,"nodeType":1906},{},[14586],{"type":12205},"Core Inputs:",{"data":14589,"marks":14590,"value":14591,"nodeType":1906},{},[]," The analysis evaluates model architecture, anticipated workload mix (e.g., document Q&A or coding assistants), and GPU counts to determine L1 tier (HBM) availability and offloading requirements.",{"data":14593,"content":14594,"nodeType":1920},{},[14595,14600],{"data":14596,"marks":14597,"value":14599,"nodeType":1906},{},[14598],{"type":12205},"Primary Outputs:",{"data":14601,"marks":14602,"value":14603,"nodeType":1906},{},[]," The analysis generates projections for average TTFT gains over standard recomputation, recovered GPU cycles, hit rate percentages for token retrieval, and the storage capacity essential for meeting retention objectives.",{"data":14605,"content":14626,"nodeType":12909},{"target":14606},{"metadata":14607,"sys":14610,"fields":14617},{"tags":14608,"concepts":14609},[],[],{"space":14611,"id":14613,"type":933,"createdAt":14614,"updatedAt":14614,"environment":14615,"publishedVersion":846,"revision":1135,"locale":672},{"sys":14612},{"type":656,"linkType":657,"id":658},"2FfCtmCYBbJh58thi6dBrT","2026-07-06T16:43:57.038Z",{"sys":14616},{"id":664,"type":656,"linkType":665},{"title":14618,"description":2030,"file":14619},"Inline 4: Right-Sizing KV Cache",{"url":14620,"details":14621,"fileName":14625,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2FfCtmCYBbJh58thi6dBrT\u002Ffcd5cc69a934ad110240c26c00750f93\u002FCR-11908-Inline_Graphic-BLOG-Practical_Guide-08.jpg",{"size":14622,"image":14623},2174993,{"width":14100,"height":14624},3512,"CR-11908-Inline Graphic-BLOG-Practical Guide-08.jpg",[],{"data":14628,"content":14629,"nodeType":12207},{},[14630],{"data":14631,"marks":14632,"value":14634,"nodeType":1906},{},[14633],{"type":12205},"Simplify the Complex with VAST",{"data":14636,"content":14637,"nodeType":1920},{},[14638,14642,14650],{"data":14639,"marks":14640,"value":14641,"nodeType":1906},{},[],"Scaling inference does not have to be a source of architectural complexity. VAST provides a single, unified platform that handles every stage of the ",{"data":14643,"content":14645,"nodeType":1915},{"uri":14644},"https:\u002F\u002Fwww.vastdata.com\u002Fai-data-pipeline",[14646],{"data":14647,"marks":14648,"value":14649,"nodeType":1906},{},[],"AI pipeline",{"data":14651,"marks":14652,"value":14653,"nodeType":1906},{},[]," without requiring separate silos for each. Because all data lives in one place VAST ensures your model context is persistent across sessions and users across your GPU estate without compromising on security and governance.",{"data":14655,"content":14656,"nodeType":1920},{},[14657,14661,14668],{"data":14658,"marks":14659,"value":14660,"nodeType":1906},{},[],"Stop wasting GPU cycles on recomputation. 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inference is running into a new constraint, and it is not always compute. As LLMs move into production, workloads extend far beyond simple prompt-and-response interactions. Conversations unfold across multiple turns between humans and agents, continuously accumulating context along the way.",{"data":15546,"content":15547,"nodeType":1920},{},[15548],{"data":15549,"marks":15550,"value":15551,"nodeType":1906},{},[],"All that context lives in the model’s key-value cache, and as context windows grow, recomputing it gets expensive as GPUs spend valuable cycles regenerating what they’ve already seen.",{"data":15553,"content":15554,"nodeType":1920},{},[15555,15559,15567],{"data":15556,"marks":15557,"value":15558,"nodeType":1906},{},[],"NVIDIA Dynamo addresses the orchestration challenges of serving these workloads at scale. By separating prefill and decode, dynamically allocating GPU resources, routing requests based on KV cache locality, and reusing stored KV cache context, Dynamo maximizes performance across distributed inference clusters. This, combined with VAST Data supporting efficient KV cache storage and retrieval, means systems can reuse context instead of recomputing it, improving latency and GPU utilization. VAST has been collaborating with the",{"data":15560,"content":15562,"nodeType":1915},{"uri":15561},"..\u002F..\u002Fblog\u002Fnvidia-dynamo-vast-scalable-optimized-inference",[15563],{"data":15564,"marks":15565,"value":15566,"nodeType":1906},{},[]," NVIDIA Dynamo team for some time now",{"data":15568,"marks":15569,"value":15570,"nodeType":1906},{},[]," and are excited about the general availability of Dynamo 1.0.",{"data":15572,"content":15573,"nodeType":1920},{},[15574],{"data":15575,"marks":15576,"value":15577,"nodeType":1906},{},[],"NVIDIA announced the following KVBM features in their tech blog:",{"data":15579,"content":15580,"nodeType":12184},{},[15581,15596,15611],{"data":15582,"content":15583,"nodeType":12173},{},[15584],{"data":15585,"content":15586,"nodeType":1920},{},[15587,15592],{"data":15588,"marks":15589,"value":15591,"nodeType":1906},{},[15590],{"type":12205},"Object storage support:",{"data":15593,"marks":15594,"value":15595,"nodeType":1906},{},[]," S3- and Azure-style blob APIs",{"data":15597,"content":15598,"nodeType":12173},{},[15599],{"data":15600,"content":15601,"nodeType":1920},{},[15602,15607],{"data":15603,"marks":15604,"value":15606,"nodeType":1906},{},[15605],{"type":12205},"Global KV event emission:",{"data":15608,"marks":15609,"value":15610,"nodeType":1906},{},[]," Emits events when KV blocks move across storage tiers, enabling cluster-wide tracking",{"data":15612,"content":15613,"nodeType":12173},{},[15614],{"data":15615,"content":15616,"nodeType":1920},{},[15617,15622],{"data":15618,"marks":15619,"value":15621,"nodeType":1906},{},[15620],{"type":12205},"Pip-installable module:",{"data":15623,"marks":15624,"value":15625,"nodeType":1906},{},[]," KVBM can be installed directly into inference engines without the full Dynamo stack",{"data":15627,"content":15628,"nodeType":12207},{},[15629],{"data":15630,"marks":15631,"value":15632,"nodeType":1906},{},[],"Orchestrating Inference at Scale",{"data":15634,"content":15635,"nodeType":1920},{},[15636],{"data":15637,"marks":15638,"value":15639,"nodeType":1906},{},[],"As inference scales to many thousands of GPUs (and millions of requests), the challenge shifts from compute to coordination. Each request hits the system with different sequence lengths, context sizes, and latency requirements, and the system has to constantly determine how those requests should be handled across the cluster.",{"data":15641,"content":15642,"nodeType":1920},{},[15643],{"data":15644,"marks":15645,"value":15646,"nodeType":1906},{},[],"At scale, this becomes an endless series of real-time decisions. GPU memory capacity has to be allocated dynamically to keep resources fully fed without creating bottlenecks. This means requests have to get routed to nodes capable of serving them most efficiently, which ideally is where relevant KV cache state already exists.",{"data":15648,"content":15649,"nodeType":1920},{},[15650],{"data":15651,"marks":15652,"value":15653,"nodeType":1906},{},[],"The two phases of inference, prefill and decode, need to be scheduled across the cluster based on their very different resource demands. Prefill is compute-intensive and benefits from high-throughput GPU execution, while decode is typically memory- and bandwidth-bound. And all the while, KV cache blocks need to be managed and placed so shared context can be reused any time requests overlap.",{"data":15655,"content":15656,"nodeType":1920},{},[15657],{"data":15658,"marks":15659,"value":15660,"nodeType":1906},{},[],"Disaggregating pre-fill and decode, Dynamo lets each phase scale independently to run on the GPU resources best suited. KV-aware routing directs requests to those GPUs that already contain the needed context, meaning an end to redundant computation. At the same time, Dynamo dynamically adjusts GPU allocation across the cluster as traffic patterns change.",{"data":15662,"content":15663,"nodeType":1920},{},[15664],{"data":15665,"marks":15666,"value":15667,"nodeType":1906},{},[],"Dynamo also maintains awareness of KV block lifecycle events as they move across memory and storage tiers. This lets the cluster hold a consistent view of where context lives without having to poll every single node. By tracking when blocks get created, migrated, or kicked out, Dynamo enables more efficient routing and fetching of shared context.",{"data":15669,"content":15670,"nodeType":1920},{},[15671],{"data":15672,"marks":15673,"value":15674,"nodeType":1906},{},[],"The result of all this is an inference architecture where GPU clusters behave as coordinated systems rather than isolated nodes. Dynamo manages the flow of requests, resources, and context across the infrastructure, enabling large-scale AI services to maintain high utilization while adapting to unpredictable workloads.",{"data":15676,"content":15677,"nodeType":1920},{},[15678],{"data":15679,"marks":15680,"value":15681,"nodeType":1906},{},[],"In conversations with AI infrastructure teams deploying large inference clusters, a consistent pattern has emerged. Operators are concerned not only with raw model performance but also with how efficiently context can be reused across requests. Managing KV cache across GPUs and storage tiers has quickly become one of the central challenges in production inference environments.",{"data":15683,"content":15684,"nodeType":1920},{},[15685],{"data":15686,"marks":15687,"value":15688,"nodeType":1906},{},[],"What AI infrastructure operators face now as they deploy large scale deployment is that by taking advantage of high KV cache reuse, it not only saves costly compute, memory and power costs, it also dramatically reduces latency, which is critical for agentic workflows. This change in AI deployment is a pattern that we see as an organization transitions from POC stage to mass scale deployment.",{"data":15690,"content":15691,"nodeType":1920},{},[15692],{"data":15693,"marks":15694,"value":15695,"nodeType":1906},{},[],"Dynamo can determine where context should be reused and how requests move through the cluster but the next challenge is ensuring that the underlying infrastructure can store and retrieve that context efficiently.",{"data":15697,"content":15698,"nodeType":1920},{},[15699],{"data":15700,"marks":15701,"value":15702,"nodeType":1906},{},[],"As Dynamo is an open source framework with modular design, with Dynamo 1.0 the KVBM module of KVcache is a Pip-installable module which can be installed directly into inference engines without the full Dynamo stack. This enables interoperability with various frameworks.",{"data":15704,"content":15705,"nodeType":12207},{},[15706],{"data":15707,"marks":15708,"value":15709,"nodeType":1906},{},[],"Context Memory is The Next Bottleneck",{"data":15711,"content":15712,"nodeType":1920},{},[15713],{"data":15714,"marks":15715,"value":15716,"nodeType":1906},{},[],"Once orchestration is in place, another constraint rears its head. Dynamo can determine where requests should run and how context should be reused across the cluster. But the effectiveness of that reuse ultimately depends on where the context itself lives.",{"data":15718,"content":15719,"nodeType":1920},{},[15720,15724,15732],{"data":15721,"marks":15722,"value":15723,"nodeType":1906},{},[],"For modern LLM workloads, that context is stored in the model’s key-value (KV) cache. As models grow and context windows expand into the hundreds of thousands of tokens, KV cache quickly exceeds the capacity of GPU memory, especially when each GPU is serving multiple concurrent users.",{"data":15725,"content":15727,"nodeType":1915},{"uri":15726},"..\u002F..\u002Fblog\u002Fkvcache-context-memory-storage",[15728],{"data":15729,"marks":15730,"value":15731,"nodeType":1906},{},[]," Context must spill beyond costly high bandwidth memory (HBM)",{"data":15733,"marks":15734,"value":15735,"nodeType":1906},{},[]," into system memory, local storage, or shared infrastructure across the cluster.",{"data":15737,"content":15738,"nodeType":1920},{},[15739],{"data":15740,"marks":15741,"value":15742,"nodeType":1906},{},[],"This creates a new architectural requirement: a memory hierarchy for inference context which VAST Data provides high performance and efficient shared storage platform for at the G3 and G4 tiers.",{"data":15744,"content":15763,"nodeType":12909},{"target":15745},{"metadata":15746,"sys":15749,"fields":15756},{"tags":15747,"concepts":15748},[],[],{"space":15750,"id":15752,"type":933,"createdAt":15753,"updatedAt":15753,"environment":15754,"publishedVersion":846,"revision":1135,"locale":672},{"sys":15751},{"type":656,"linkType":657,"id":658},"4Dvl6L6Rp18aYGSmSK1KjP","2026-03-16T12:45:12.078Z",{"sys":15755},{"id":664,"type":656,"linkType":665},{"title":15499,"description":15499,"file":15757},{"url":15758,"details":15759,"fileName":15762,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4Dvl6L6Rp18aYGSmSK1KjP\u002Fe39fc4f66b9f68b47592d434c5a11c8e\u002Fhow-nvidia-dynamo-vast-unlock-context-reuse-at-scale-1.webp",{"size":15760,"image":15761},61332,{"width":1143,"height":1144},"how-nvidia-dynamo-vast-unlock-context-reuse-at-scale-1.webp",[],{"data":15765,"content":15766,"nodeType":1920},{},[15767],{"data":15768,"marks":15769,"value":15770,"nodeType":1906},{},[],"As context grows beyond what GPUs can hold locally, inference systems naturally form a memory hierarchy for KV cache. Different tiers store context based on how frequently it is accessed and how quickly it must be retrieved. In large inference clusters, this hierarchy typically includes:",{"data":15772,"content":15773,"nodeType":12184},{},[15774,15789,15804,15819],{"data":15775,"content":15776,"nodeType":12173},{},[15777],{"data":15778,"content":15779,"nodeType":1920},{},[15780,15785],{"data":15781,"marks":15782,"value":15784,"nodeType":1906},{},[15783],{"type":12205},"G1 — Active KV (GPU HBM)\n",{"data":15786,"marks":15787,"value":15788,"nodeType":1906},{},[]," The working set of context currently used by the model. This tier provides the lowest latency but is limited by GPU memory capacity.",{"data":15790,"content":15791,"nodeType":12173},{},[15792],{"data":15793,"content":15794,"nodeType":1920},{},[15795,15800],{"data":15796,"marks":15797,"value":15799,"nodeType":1906},{},[15798],{"type":12205},"G2 — Staging \u002F Spillover KV (Host Memory)\n",{"data":15801,"marks":15802,"value":15803,"nodeType":1906},{},[]," Context that no longer fits in GPU memory but may soon be reused. Host memory provides more capacity but slightly higher access latency.",{"data":15805,"content":15806,"nodeType":12173},{},[15807],{"data":15808,"content":15809,"nodeType":1920},{},[15810,15815],{"data":15811,"marks":15812,"value":15814,"nodeType":1906},{},[15813],{"type":12205},"G3 — Warm KV Reuse (Local or Shared Storage)\n",{"data":15816,"marks":15817,"value":15818,"nodeType":1906},{},[]," Previously computed context that may be reused across requests or sessions. Efficient access at this tier allows GPUs to avoid recomputing KV state.",{"data":15820,"content":15821,"nodeType":12173},{},[15822],{"data":15823,"content":15824,"nodeType":1920},{},[15825,15830],{"data":15826,"marks":15827,"value":15829,"nodeType":1906},{},[15828],{"type":12205},"G4 — Cold or Shared Context (Cluster-Level Storage) ",{"data":15831,"marks":15832,"value":15833,"nodeType":1906},{},[],"Longer-lived context shared across nodes or sessions. This tier enables persistent reuse of inference state across distributed clusters.",{"data":15835,"content":15836,"nodeType":1920},{},[15837],{"data":15838,"marks":15839,"value":15840,"nodeType":1906},{},[],"In Dynamo 1.0, KV block manager can persist KV blocks to object storage tiers compatible with S3 or Azure-style blob APIs, allowing clusters to store and retrieve context across distributed environments. Announced support by VAST Data for S3\u002FRDMA ensures simple, multi-protocol support for all common data types with a unified data platform that is simple and performant.",{"data":15842,"content":15843,"nodeType":1920},{},[15844],{"data":15845,"marks":15846,"value":15847,"nodeType":1906},{},[],"Active KV cache stays in GPU memory for immediate use, while warm or shared context must be stored in tiers that allow it to be retrieved quickly when requests overlap. Without an efficient way to persist and retrieve that context, the benefits of KV-aware routing diminish, and GPUs return to recomputing results that already exist elsewhere in the system.",{"data":15849,"content":15850,"nodeType":1920},{},[15851],{"data":15852,"marks":15853,"value":15854,"nodeType":1906},{},[],"This is where the infrastructure layer becomes critical.",{"data":15856,"content":15857,"nodeType":1920},{},[15858],{"data":15859,"marks":15860,"value":15862,"nodeType":1906},{},[15861],{"type":12205},"By integrating Dynamo’s KV cache management with the VAST Data platform, inference clusters gain a high-performance context storage tier capable of feeding GPUs at network line speeds. Instead of recomputing context for each request, systems can retrieve previously computed KV cache blocks and reuse them across nodes.",{"data":15864,"content":15865,"nodeType":1920},{},[15866],{"data":15867,"marks":15868,"value":15869,"nodeType":1906},{},[],"The result is a significant shift in inference efficiency. GPUs spend less time reconstructing context and more time generating new tokens, improving latency, utilization, and overall system throughput.",{"data":15871,"content":15872,"nodeType":1920},{},[15873],{"data":15874,"marks":15875,"value":15876,"nodeType":1906},{},[],"As part of the new Dynamo 1.0 implementation, there is support for Global KV event emission, which means Dynamo emits events when KV blocks move across storage tiers enabling cluster-wide tracking.",{"data":15878,"content":15879,"nodeType":1920},{},[15880,15884,15889],{"data":15881,"marks":15882,"value":15883,"nodeType":1906},{},[],"From a user perspective, ",{"data":15885,"marks":15886,"value":15888,"nodeType":1906},{},[15887],{"type":12205},"Global KV Event Emission",{"data":15890,"marks":15891,"value":15892,"nodeType":1906},{},[]," is the “invisible hand” that makes a massive, distributed AI cluster feel like a single, incredibly fast, and infinite-memory computer.\nWith Global KC event emission, the user’s “context” becomes a portable, global asset",{"data":15894,"content":15895,"nodeType":12207},{},[15896],{"data":15897,"marks":15898,"value":15899,"nodeType":1906},{},[],"Measuring the Impact of KV Cache Reuse",{"data":15901,"content":15902,"nodeType":1920},{},[15903,15907,15915],{"data":15904,"marks":15905,"value":15906,"nodeType":1906},{},[],"To quantify the impact of KV cache reuse in a distributed inference environment, experiments were conducted using a Llama-3 405B model with a 128K token context window running on a cluster of eight",{"data":15908,"content":15910,"nodeType":1915},{"uri":15909},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Ftechnologies\u002Fhopper-architecture\u002F",[15911],{"data":15912,"marks":15913,"value":15914,"nodeType":1906},{},[]," NVIDIA Hopper",{"data":15916,"marks":15917,"value":15918,"nodeType":1906},{},[]," GPUs connected to storage through 2×100 Gb\u002Fs networking. The experiment compared two execution paths: recomputing the KV cache for each request, and retrieving previously computed KV state from shared storage.",{"data":15920,"content":15921,"nodeType":1920},{},[15922],{"data":15923,"marks":15924,"value":15925,"nodeType":1906},{},[],"In a typical inference pipeline, the prefill stage processes the input prompt and generates the attention keys and values that represent the model’s context. For large models and long context windows, this step can be expensive in compute resources. When the same context must be recomputed repeatedly, GPUs spend a significant portion of each request rebuilding state before producing the first output token.",{"data":15927,"content":15928,"nodeType":1920},{},[15929,15933,15938,15942,15947],{"data":15930,"marks":15931,"value":15932,"nodeType":1906},{},[],"By retrieving KV cache blocks from the VAST Data platform instead of recomputing them, the system bypasses this recomputation step entirely. In these tests, fetching the KV cache from shared storage reduced time-to-first-token by up to ",{"data":15934,"marks":15935,"value":15937,"nodeType":1906},{},[15936],{"type":12205},"20×",{"data":15939,"marks":15940,"value":15941,"nodeType":1906},{},[],", while reducing GPU compute time by approximately ",{"data":15943,"marks":15944,"value":15946,"nodeType":1906},{},[15945],{"type":12205},"90 percent",{"data":15948,"marks":15949,"value":15950,"nodeType":1906},{},[],". Because KV blocks were delivered at network line speeds, the bottleneck shifted away from storage I\u002FO and toward the available network bandwidth.",{"data":15952,"content":15971,"nodeType":12909},{"target":15953},{"metadata":15954,"sys":15957,"fields":15964},{"tags":15955,"concepts":15956},[],[],{"space":15958,"id":15960,"type":933,"createdAt":15961,"updatedAt":15961,"environment":15962,"publishedVersion":846,"revision":1135,"locale":672},{"sys":15959},{"type":656,"linkType":657,"id":658},"31zPzLyMCxjPFPjd32Lx2G","2026-03-16T12:56:39.830Z",{"sys":15963},{"id":664,"type":656,"linkType":665},{"title":15499,"description":15499,"file":15965},{"url":15966,"details":15967,"fileName":15970,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F31zPzLyMCxjPFPjd32Lx2G\u002F685f0959cfb380519663201ab61b7150\u002Fhow-nvidia-dynamo-vast-unlock-context-reuse-at-scale-2.webp",{"size":15968,"image":15969},51516,{"width":1143,"height":1144},"how-nvidia-dynamo-vast-unlock-context-reuse-at-scale-2.webp",[],{"data":15973,"content":15974,"nodeType":1920},{},[15975],{"data":15976,"marks":15977,"value":15978,"nodeType":1906},{},[],"\nEnterprise-Ready Infrastructure: Efficiency at Scale",{"data":15980,"content":15981,"nodeType":1920},{},[15982,15986,15994],{"data":15983,"marks":15984,"value":15985,"nodeType":1906},{},[],"Beyond raw performance gains, the integration of",{"data":15987,"content":15989,"nodeType":1915},{"uri":15988},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fai\u002Fdynamo\u002F",[15990],{"data":15991,"marks":15992,"value":15993,"nodeType":1906},{},[]," NVIDIA Dynamo",{"data":15995,"marks":15996,"value":15997,"nodeType":1906},{},[]," and VAST Data addresses the operational hurdles of mass-scale deployment.",{"data":15999,"content":16000,"nodeType":12184},{},[16001,16029,16053],{"data":16002,"content":16003,"nodeType":12173},{},[16004],{"data":16005,"content":16006,"nodeType":1920},{},[16007,16011,16016,16020,16025],{"data":16008,"marks":16009,"value":16010,"nodeType":1906},{},[]," ",{"data":16012,"marks":16013,"value":16015,"nodeType":1906},{},[16014],{"type":12205},"1.4x Data Reduction:",{"data":16017,"marks":16018,"value":16019,"nodeType":1906},{},[]," VAST delivers a ",{"data":16021,"marks":16022,"value":16024,"nodeType":1906},{},[16023],{"type":12205},"1.4x data reduction ratio",{"data":16026,"marks":16027,"value":16028,"nodeType":1906},{},[]," specifically for KV cache data. This is a critical advantage given the current global shortage of enterprise SSDs, allowing organizations to store significantly more context within the same physical footprint.",{"data":16030,"content":16031,"nodeType":12173},{},[16032],{"data":16033,"content":16034,"nodeType":1920},{},[16035,16040,16044,16049],{"data":16036,"marks":16037,"value":16039,"nodeType":1906},{},[16038],{"type":12205},"Driverless GPU Nodes:",{"data":16041,"marks":16042,"value":16043,"nodeType":1906},{},[]," The architecture ",{"data":16045,"marks":16046,"value":16048,"nodeType":1906},{},[16047],{"type":12205},"does not require proprietary drivers on the GPU machines",{"data":16050,"marks":16051,"value":16052,"nodeType":1906},{},[],". It remains lightweight and portable across various frameworks without added kernel complexity.",{"data":16054,"content":16055,"nodeType":12173},{},[16056],{"data":16057,"content":16058,"nodeType":1920},{},[16059,16064],{"data":16060,"marks":16061,"value":16063,"nodeType":1906},{},[16062],{"type":12205},"Secure Multi-Tenancy:",{"data":16065,"marks":16066,"value":16067,"nodeType":1906},{},[]," VAST provides the logical isolation and Quality of Service (QoS) necessary to serve thousands of concurrent users. This ensures that sensitive conversation history or agentic context remains private and secure between different tenants on the shared cluster.",{"data":16069,"content":16070,"nodeType":12207},{},[16071],{"data":16072,"marks":16073,"value":16074,"nodeType":1906},{},[],"Enabling More Tokens Per GPU",{"data":16076,"content":16077,"nodeType":1920},{},[16078,16082,16087],{"data":16079,"marks":16080,"value":16081,"nodeType":1906},{},[],"By applying our test results to a real-world deployment scenario, ",{"data":16083,"marks":16084,"value":16086,"nodeType":1906},{},[16085],{"type":12205},"we project achieving 60-130% more tokens per dollar with VAST’s KV cache acceleration. We assume conservative cache hit rate of 40-60% for enterprise AI flows.",{"data":16088,"marks":16089,"value":16090,"nodeType":1906},{},[]," For Agentic and coding tasks cache hit rate may be even higher.",{"data":16092,"content":16112,"nodeType":12909},{"target":16093},{"metadata":16094,"sys":16097,"fields":16104},{"tags":16095,"concepts":16096},[],[],{"space":16098,"id":16100,"type":933,"createdAt":16101,"updatedAt":16101,"environment":16102,"publishedVersion":846,"revision":1135,"locale":672},{"sys":16099},{"type":656,"linkType":657,"id":658},"63U1hXNzJiR6rGjxdYTsmT","2026-03-16T13:18:23.275Z",{"sys":16103},{"id":664,"type":656,"linkType":665},{"title":15499,"description":15499,"file":16105},{"url":16106,"details":16107,"fileName":16111,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F63U1hXNzJiR6rGjxdYTsmT\u002F59cc894235e162ce3fa480558a66c074\u002Fhow-nvidia-dynamo-vast-unlock-context-reuse-at-scale-3.webp",{"size":16108,"image":16109},23808,{"width":1143,"height":16110},513,"how-nvidia-dynamo-vast-unlock-context-reuse-at-scale-3.webp",[],{"data":16114,"content":16115,"nodeType":1920},{},[16116],{"data":16117,"marks":16118,"value":16119,"nodeType":1906},{},[],"Our calculation demonstrates how VAST turns wasted GPU time into productive time. We take the time it takes to process a request with and without kvcache, with varying kvcache hit rate assumptions that are applicable to everyday use cases such as coding, chats, and agentic workloads.",{"data":16121,"content":16122,"nodeType":1920},{},[16123],{"data":16124,"marks":16125,"value":16126,"nodeType":1906},{},[],"This performance boost translates to significantly more token generation, and noticeably lower latency to the end user. The principle is clear: we are not making the GPU faster; we are making it available more often, turning storage into a compute force multiplier.",{"data":16128,"content":16129,"nodeType":1920},{},[16130],{"data":16131,"marks":16132,"value":16133,"nodeType":1906},{},[],"The impact of this shift extends way beyond latency improvements. When GPUs no longer spend cycles rebuilding context, they can devote a much larger portion of their compute budget to generating new tokens.",{"data":16135,"content":16136,"nodeType":1920},{},[16137],{"data":16138,"marks":16139,"value":16140,"nodeType":1906},{},[],"In practice, KV cache reuse transforms storage into a compute efficiency layer. Rather than accelerating the GPU itself, the system increases effective throughput by ensuring GPUs spend more of their time generating new tokens instead of repeating work that has already been done.",{"data":16142,"content":16143,"nodeType":12207},{},[16144],{"data":16145,"marks":16146,"value":16147,"nodeType":1906},{},[],"Introducing Context Memory Storage",{"data":16149,"content":16150,"nodeType":1920},{},[16151,16155,16162],{"data":16152,"marks":16153,"value":16154,"nodeType":1906},{},[],"As inference systems scale to larger clusters and longer context windows, even shared storage tiers eventually encounter limits. Context reuse becomes most effective when KV cache can be accessed with low latency and shared efficiently across GPUs serving related requests. This requirement has led to the development of a new architectural layer: NVIDIA Context Memory Storage (CMX), built on the recently announced ",{"data":16156,"content":16158,"nodeType":1915},{"uri":16157},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fai-storage\u002Fstx\u002F",[16159],{"data":16160,"marks":16161,"value":14342,"nodeType":1906},{},[],{"data":16163,"marks":16164,"value":16165,"nodeType":1906},{},[]," reference architecture.",{"data":16167,"content":16168,"nodeType":1920},{},[16169],{"data":16170,"marks":16171,"value":16172,"nodeType":1906},{},[],"CMX introduces a dedicated memory tier designed specifically for inference context. Rather than pushing active KV cache into higher-latency storage systems, CMX provides a pod-level context layer shared across GPUs within an inference cluster. This allows previously computed KV state to be reused across nodes while keeping retrieval latency low enough to support real-time workloads.",{"data":16174,"content":16193,"nodeType":12909},{"target":16175},{"metadata":16176,"sys":16179,"fields":16186},{"tags":16177,"concepts":16178},[],[],{"space":16180,"id":16182,"type":933,"createdAt":16183,"updatedAt":16183,"environment":16184,"publishedVersion":846,"revision":1135,"locale":672},{"sys":16181},{"type":656,"linkType":657,"id":658},"6kGwXQnevCgNDA42WTfhH7","2026-03-16T13:21:58.920Z",{"sys":16185},{"id":664,"type":656,"linkType":665},{"title":15499,"description":15499,"file":16187},{"url":16188,"details":16189,"fileName":16192,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6kGwXQnevCgNDA42WTfhH7\u002F2508767b37c6e8c66c958cfa63b889d4\u002Fhow-nvidia-dynamo-vast-unlock-context-reuse-at-scale-4.webp",{"size":16190,"image":16191},119258,{"width":1143,"height":1144},"how-nvidia-dynamo-vast-unlock-context-reuse-at-scale-4.webp",[],{"data":16195,"content":16196,"nodeType":1920},{},[16197,16201,16209,16213,16221],{"data":16198,"marks":16199,"value":16200,"nodeType":1906},{},[],"The CMX architecture combines several NVIDIA technologies to achieve this goal.",{"data":16202,"content":16204,"nodeType":1915},{"uri":16203},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fnetworking\u002Fproducts\u002Fdata-processing-unit\u002F",[16205],{"data":16206,"marks":16207,"value":16208,"nodeType":1906},{},[]," NVIDIA BlueField-4 data processing units (DPUs)",{"data":16210,"marks":16211,"value":16212,"nodeType":1906},{},[]," connected via high-speed link with RDMA to VAST Data D-nodes provide high-performance storage processing and data movement close to the compute nodes.",{"data":16214,"content":16216,"nodeType":1915},{"uri":16215},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fnetworking\u002Fspectrumx\u002F",[16217],{"data":16218,"marks":16219,"value":16220,"nodeType":1906},{},[]," NVIDIA Spectrum-X Ethernet",{"data":16222,"marks":16223,"value":16224,"nodeType":1906},{},[]," networking delivers predictable, low-latency connectivity across the cluster, ensuring that KV cache can be retrieved quickly when requests share context. Dynamo continues to orchestrate request routing and KV reuse across the system.",{"data":16226,"content":16245,"nodeType":12909},{"target":16227},{"metadata":16228,"sys":16231,"fields":16238},{"tags":16229,"concepts":16230},[],[],{"space":16232,"id":16234,"type":933,"createdAt":16235,"updatedAt":16235,"environment":16236,"publishedVersion":846,"revision":1135,"locale":672},{"sys":16233},{"type":656,"linkType":657,"id":658},"1CRro2hsK9doQ2698hjMMe","2026-03-16T13:26:50.244Z",{"sys":16237},{"id":664,"type":656,"linkType":665},{"title":15499,"description":15499,"file":16239},{"url":16240,"details":16241,"fileName":16244,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1CRro2hsK9doQ2698hjMMe\u002F35486969ccb12d8bca25893cc04d8b13\u002Fhow-nvidia-dynamo-vast-unlock-context-reuse-at-scale-5.webp",{"size":16242,"image":16243},115030,{"width":1143,"height":1144},"how-nvidia-dynamo-vast-unlock-context-reuse-at-scale-5.webp",[],{"data":16247,"content":16248,"nodeType":1920},{},[16249],{"data":16250,"marks":16251,"value":16252,"nodeType":1906},{},[],"Together, these components create an infrastructure where inference context behaves more like shared memory than traditional storage. GPUs can retrieve KV blocks across the cluster as needed, enabling efficient reuse of context across large distributed deployments. The software support for CMX in Dynamo will support the KV cache 3.5 tier in future releases.",{"data":16254,"content":16255,"nodeType":1920},{},[16256],{"data":16257,"marks":16258,"value":16259,"nodeType":1906},{},[],"VAST plays a key role in this architecture by providing the scalable data services that manage KV cache across these tiers. By integrating VAST’s distributed data platform with CMX, inference clusters can persist and retrieve context efficiently while benefiting from enterprise capabilities such as data reduction, security, and lifecycle management.",{"data":16261,"content":16262,"nodeType":1920},{},[16263],{"data":16264,"marks":16265,"value":16266,"nodeType":1906},{},[],"The result is a system designed for the next generation of AI inference utilizing large models with long input sequences including workloads like agentic AI, coding, chatbots, research and reasoning models with frequent content reuse. As context windows grow and agent-based workflows extend across longer interactions, CMX allows inference clusters to maintain low latency while scaling context memory far beyond the limits of individual GPU nodes.",{"data":16268,"content":16269,"nodeType":12207},{},[16270],{"data":16271,"marks":16272,"value":16273,"nodeType":1906},{},[],"Scaling Context for Real-World AI Systems",{"data":16275,"content":16276,"nodeType":1920},{},[16277],{"data":16278,"marks":16279,"value":16280,"nodeType":1906},{},[],"As organizations move from experimentation to production AI systems, the scale of inference context grows fast. Persistent workflows, multi-turn interactions, and agent-driven tasks all add to the mountain of context that has to be stored and reused across requests.",{"data":16282,"content":16283,"nodeType":1920},{},[16284],{"data":16285,"marks":16286,"value":16287,"nodeType":1906},{},[],"For example, imagine an organization serving roughly 10,000 concurrent users, each with a conversation history that generates approximately 32 GB of KV cache for a full session. Even modest retention goals can require hundreds of terabytes of context storage.",{"data":16289,"content":16290,"nodeType":1920},{},[16291],{"data":16292,"marks":16293,"value":16294,"nodeType":1906},{},[],"Retaining only the active session for each user requires roughly 320 TB of KV cache capacity. Expanding that retention to multiple sessions per user pushes requirements into the petabyte range, while longer-term context retention for agent workflows can grow far larger.",{"data":16296,"content":16297,"nodeType":1920},{},[16298],{"data":16299,"marks":16300,"value":16301,"nodeType":1906},{},[],"While the problem is clear, the solution is too. Architectures that treat KV cache as an first-class infrastructure layer allow systems to reuse previously computed state instead of recomputing it repeatedly. This improves latency and increases the productive use of GPU resources.",{"data":16303,"content":16304,"nodeType":1920},{},[16305],{"data":16306,"marks":16307,"value":16309,"nodeType":1906},{},[16308],{"type":12205},"The combination of Dynamo orchestration, efficient KV cache reuse, and scalable context memory infrastructure enables inference systems to serve more users and generate more tokens with the same underlying compute.",{"data":16311,"content":16312,"nodeType":1920},{},[16313],{"data":16314,"marks":16315,"value":16316,"nodeType":1906},{},[],"And as LLMs continue to evolve toward longer context windows and agent-based workflows, the importance of efficient context management will only grow. Dynamo provides the control plane that coordinates distributed inference across GPU clusters. 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Dynamo + VAST = Scalable, Optimized Inference","nvidia-dynamo-vast-scalable-optimized-inference","2025-12-16","Anat Heilper, Director of AI Architecture | Dan Aloni, Staff Software Engineer | Matthew Rogers, Field CTO, AI and Security | Alon Horev, CTO and Co-Founder",{"metadata":16481,"sys":16484,"fields":16493},{"tags":16482,"concepts":16483},[],[],{"space":16485,"id":16487,"type":14,"createdAt":16488,"updatedAt":16488,"environment":16489,"publishedVersion":900,"revision":1135,"contentType":16491,"locale":672},{"sys":16486},{"type":656,"linkType":657,"id":658},"Q1X1CRPztKkNmePSn739P","2025-12-16T20:09:40.065Z",{"sys":16490},{"id":664,"type":656,"linkType":665},{"sys":16492},{"type":656,"linkType":670,"id":12083},{"entryTitle":16476,"image":16494},{"metadata":16495,"sys":16498,"fields":16505},{"tags":16496,"concepts":16497},[],[],{"space":16499,"id":16501,"type":933,"createdAt":16502,"updatedAt":16502,"environment":16503,"publishedVersion":846,"revision":1135,"locale":672},{"sys":16500},{"type":656,"linkType":657,"id":658},"62zUKLoTZXHthlK8IoiXLi","2025-12-16T20:09:35.169Z",{"sys":16504},{"id":664,"type":656,"linkType":665},{"title":16476,"description":16476,"file":16506},{"url":16507,"details":16508,"fileName":16511,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F62zUKLoTZXHthlK8IoiXLi\u002Fa3bb25122b47dec5e32e2c1e39fc7ade\u002Fnvidia-dynamo-vast-scalable-optimized-inference.webp",{"size":16509,"image":16510},79238,{"width":1143,"height":1144},"nvidia-dynamo-vast-scalable-optimized-inference.webp",{"data":16513,"content":16514,"nodeType":1921},{},[16515,16522,16655,16659,16666,16684,16723,16730,16748,16784,16787,16794,16801,16808,16815,16822,16885,16892,16914,16932,16935,16942,16949,16992,16999,17055,17058,17065,17072,17079,17086,17102,17117,17125,17132,17165,17168,17175,17182,17194,17201,17263,17270,17273,17280,17292,17299,17311,17323,17335,17365,17372,17380,17383,17390,17397,17520,17527,17563,17584,17596,17603,17646,17667,17670,17677,17694,17701,17704,17711,17718,17748],{"data":16516,"content":16517,"nodeType":12305},{},[16518],{"data":16519,"marks":16520,"value":16521,"nodeType":1906},{},[],"TLDR:",{"data":16523,"content":16524,"nodeType":12184},{},[16525,16535,16545,16555,16565,16575,16585,16625,16635],{"data":16526,"content":16527,"nodeType":12173},{},[16528],{"data":16529,"content":16530,"nodeType":1920},{},[16531],{"data":16532,"marks":16533,"value":16534,"nodeType":1906},{},[],"Long-context 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":16536,"content":16537,"nodeType":12173},{},[16538],{"data":16539,"content":16540,"nodeType":1920},{},[16541],{"data":16542,"marks":16543,"value":16544,"nodeType":1906},{},[],"The NVIDIA Dynamo inference framework is bringing novel capabilities to market such as key-value cache (KV$ in short) offload",{"data":16546,"content":16547,"nodeType":12173},{},[16548],{"data":16549,"content":16550,"nodeType":1920},{},[16551],{"data":16552,"marks":16553,"value":16554,"nodeType":1906},{},[],"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":16556,"content":16557,"nodeType":12173},{},[16558],{"data":16559,"content":16560,"nodeType":1920},{},[16561],{"data":16562,"marks":16563,"value":16564,"nodeType":1906},{},[],"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":16566,"content":16567,"nodeType":12173},{},[16568],{"data":16569,"content":16570,"nodeType":1920},{},[16571],{"data":16572,"marks":16573,"value":16574,"nodeType":1906},{},[],"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":16576,"content":16577,"nodeType":12173},{},[16578],{"data":16579,"content":16580,"nodeType":1920},{},[16581],{"data":16582,"marks":16583,"value":16584,"nodeType":1906},{},[],"This blog post covers sizing and architecture considerations when building for large-scale inference infrastructure that is enabled for KV$ offload",{"data":16586,"content":16587,"nodeType":12173},{},[16588],{"data":16589,"content":16590,"nodeType":1920},{},[16591,16595,16603,16607,16612,16616,16621],{"data":16592,"marks":16593,"value":16594,"nodeType":1906},{},[],"VAST collaborated with CoreWeave to evaluate ",{"data":16596,"content":16598,"nodeType":1915},{"uri":16597},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-to-reduce-kv-cache-bottlenecks-with-nvidia-dynamo\u002F",[16599],{"data":16600,"marks":16601,"value":16602,"nodeType":1906},{},[],"NVIDIA’s Dynamo KV$",{"data":16604,"marks":16605,"value":16606,"nodeType":1906},{},[]," offload with the VAST AI OS and saw ",{"data":16608,"marks":16609,"value":16611,"nodeType":1906},{},[16610],{"type":12205},"GPU efficiency improve by 90%",{"data":16613,"marks":16614,"value":16615,"nodeType":1906},{},[]," and ",{"data":16617,"marks":16618,"value":16620,"nodeType":1906},{},[16619],{"type":12205},"TTFT improve by 20x",{"data":16622,"marks":16623,"value":16624,"nodeType":1906},{},[]," as compared to when GPUs need to prefill calculate every past conversation",{"data":16626,"content":16627,"nodeType":12173},{},[16628],{"data":16629,"content":16630,"nodeType":1920},{},[16631],{"data":16632,"marks":16633,"value":16634,"nodeType":1906},{},[],"Dynamo data persistence now relies on data batching for large asynchronous writes, and bulk synchronous reads - resulting in an IO profile focused on storage throughput, not IOPS and low latency",{"data":16636,"content":16637,"nodeType":12173},{},[16638],{"data":16639,"content":16640,"nodeType":1920},{},[16641,16645,16652],{"data":16642,"marks":16643,"value":16644,"nodeType":1906},{},[],"Ultimately, VAST’s testing identified that most KV$ 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":16646,"content":16647,"nodeType":1915},{"uri":14121},[16648],{"data":16649,"marks":16650,"value":16651,"nodeType":1906},{},[],"here",{"data":16653,"marks":16654,"value":1919,"nodeType":1906},{},[],{"data":16656,"content":16657,"nodeType":16658},{},[],"hr",{"data":16660,"content":16661,"nodeType":1920},{},[16662],{"data":16663,"marks":16664,"value":16665,"nodeType":1906},{},[],"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":16667,"content":16668,"nodeType":1920},{},[16669,16673,16680],{"data":16670,"marks":16671,"value":16672,"nodeType":1906},{},[],"Previously, we wrote about ",{"data":16674,"content":16675,"nodeType":1915},{"uri":12217},[16676],{"data":16677,"marks":16678,"value":16679,"nodeType":1906},{},[],"accelerating inference and the AI data pipeline",{"data":16681,"marks":16682,"value":16683,"nodeType":1906},{},[]," 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":16685,"content":16686,"nodeType":1920},{},[16687,16691,16697,16700,16707,16711,16719],{"data":16688,"marks":16689,"value":16690,"nodeType":1906},{},[],"Today, we're excited to share the next major step in this journey: the collaboration and integration between the ",{"data":16692,"content":16693,"nodeType":1915},{"uri":11214},[16694],{"data":16695,"marks":16696,"value":1122,"nodeType":1906},{},[],{"data":16698,"marks":16699,"value":16615,"nodeType":1906},{},[],{"data":16701,"content":16703,"nodeType":1915},{"uri":16702},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fdynamo\u002F",[16704],{"data":16705,"marks":16706,"value":14404,"nodeType":1906},{},[],{"data":16708,"marks":16709,"value":16710,"nodeType":1906},{},[],". This article will detail Dynamo's sophisticated ",{"data":16712,"content":16714,"nodeType":1915},{"uri":16713},"https:\u002F\u002Fdocs.nvidia.com\u002Fdynamo\u002Farchive\u002F0.2.0\u002Farchitecture\u002Fkv_cache_manager.html",[16715],{"data":16716,"marks":16717,"value":16718,"nodeType":1906},{},[],"architecture",{"data":16720,"marks":16721,"value":16722,"nodeType":1906},{},[]," and show how the VAST AI OS is now integrated as a foundational, high-performance capacity solution to solve the GPU memory wall.",{"data":16724,"content":16725,"nodeType":12207},{},[16726],{"data":16727,"marks":16728,"value":16729,"nodeType":1906},{},[],"What is NVIDIA Dynamo?",{"data":16731,"content":16732,"nodeType":1920},{},[16733,16737,16744],{"data":16734,"marks":16735,"value":16736,"nodeType":1906},{},[],"Before we dive into the problem, let's briefly introduce ",{"data":16738,"content":16740,"nodeType":1915},{"uri":16739},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fwhy-everyones-talking-about-nvidia-dynamo-and-why-it-actually-matters",[16741],{"data":16742,"marks":16743,"value":14404,"nodeType":1906},{},[],{"data":16745,"marks":16746,"value":16747,"nodeType":1906},{},[],". NVIDIA Dynamo is an open-source, high-performance AI inference framework designed to manage the complexities of serving AI models at data center scale. Dynamo is not just a single tool, but is rather a modular system with components such as routing, scheduling, and (most importantly for this discussion) memory management.",{"data":16749,"content":16750,"nodeType":1920},{},[16751,16755,16763,16768,16772,16780],{"data":16752,"marks":16753,"value":16754,"nodeType":1906},{},[],"One of the key components for solving the memory challenge is the ",{"data":16756,"content":16758,"nodeType":1915},{"uri":16757},"https:\u002F\u002Fdocs.nvidia.com\u002Fdynamo\u002Flatest\u002Farchitecture\u002Fkvbm_components.html",[16759],{"data":16760,"marks":16761,"value":16762,"nodeType":1906},{},[],"KV Block Manager (KVBM",{"data":16764,"marks":16765,"value":16767,"nodeType":1906},{},[16766],{"type":12205},")",{"data":16769,"marks":16770,"value":16771,"nodeType":1906},{},[],". This is the \"brain\" within Dynamo specifically designed to intelligently manage the LLM's ",{"data":16773,"content":16775,"nodeType":1915},{"uri":16774},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fopen-and-undivided-attention",[16776],{"data":16777,"marks":16778,"value":16779,"nodeType":1906},{},[],"KV$",{"data":16781,"marks":16782,"value":16783,"nodeType":1906},{},[],". It's the KVBM that enables the sophisticated, caching system that allows us to break through the GPU memory wall.",{"data":16785,"content":16786,"nodeType":16658},{},[],{"data":16788,"content":16789,"nodeType":12207},{},[16790],{"data":16791,"marks":16792,"value":16793,"nodeType":1906},{},[],"The Challenge: The GPU Memory Wall",{"data":16795,"content":16796,"nodeType":1920},{},[16797],{"data":16798,"marks":16799,"value":16800,"nodeType":1906},{},[],"The KV$ stores the key and value states for every token in a sequence. 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":16802,"content":16803,"nodeType":12207},{},[16804],{"data":16805,"marks":16806,"value":16807,"nodeType":1906},{},[],"The Solution: A New Caching Architecture",{"data":16809,"content":16810,"nodeType":1920},{},[16811],{"data":16812,"marks":16813,"value":16814,"nodeType":1906},{},[],"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":16816,"content":16817,"nodeType":1920},{},[16818],{"data":16819,"marks":16820,"value":16821,"nodeType":1906},{},[],"Here is the four-tiered architecture as defined by NVIDIA:",{"data":16823,"content":16824,"nodeType":12184},{},[16825,16840,16855,16870],{"data":16826,"content":16827,"nodeType":12173},{},[16828],{"data":16829,"content":16830,"nodeType":1920},{},[16831,16836],{"data":16832,"marks":16833,"value":16835,"nodeType":1906},{},[16834],{"type":12205},"G1: Device Memory (GPU HBM):",{"data":16837,"marks":16838,"value":16839,"nodeType":1906},{},[]," The “hot” tier. This is the fastest and most expensive memory, residing directly on the GPU. It holds the cache for tokens being actively computed.",{"data":16841,"content":16842,"nodeType":12173},{},[16843],{"data":16844,"content":16845,"nodeType":1920},{},[16846,16851],{"data":16847,"marks":16848,"value":16850,"nodeType":1906},{},[16849],{"type":12205},"G2: CPU Memory:",{"data":16852,"marks":16853,"value":16854,"nodeType":1906},{},[]," The “warm” staging tier. This is a larger, but slower, tier using the server's main CPU memory. When HBM is needed for new requests, less-active KV$ blocks are moved here.",{"data":16856,"content":16857,"nodeType":12173},{},[16858],{"data":16859,"content":16860,"nodeType":1920},{},[16861,16866],{"data":16862,"marks":16863,"value":16865,"nodeType":1906},{},[16864],{"type":12205},"G3: Local\u002FPooled SSDs (High-Capacity Cache):",{"data":16867,"marks":16868,"value":16869,"nodeType":1906},{},[]," The “cold” tier. This is where the VAST AI OS currently integrates. 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":16871,"content":16872,"nodeType":12173},{},[16873],{"data":16874,"content":16875,"nodeType":1920},{},[16876,16881],{"data":16877,"marks":16878,"value":16880,"nodeType":1906},{},[16879],{"type":12205},"G4: Shared Network Storage:",{"data":16882,"marks":16883,"value":16884,"nodeType":1906},{},[]," 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":16886,"content":16887,"nodeType":1920},{},[16888],{"data":16889,"marks":16890,"value":16891,"nodeType":1906},{},[],"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":16893,"content":16913,"nodeType":12909},{"target":16894},{"metadata":16895,"sys":16898,"fields":16905},{"tags":16896,"concepts":16897},[],[],{"space":16899,"id":16901,"type":933,"createdAt":16902,"updatedAt":16902,"environment":16903,"publishedVersion":846,"revision":1135,"locale":672},{"sys":16900},{"type":656,"linkType":657,"id":658},"3rd6P1QQmnIepmPq1YkZd1","2026-01-14T12:20:42.908Z",{"sys":16904},{"id":664,"type":656,"linkType":665},{"title":16476,"description":16476,"file":16906},{"url":16907,"details":16908,"fileName":16912,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3rd6P1QQmnIepmPq1YkZd1\u002F5d441ebfa62d8cefe45cd048c47ddeb1\u002Fnvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",{"size":16909,"image":16910},46764,{"width":1143,"height":16911},781,"nvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",[],{"data":16915,"content":16916,"nodeType":1920},{},[16917,16922,16929],{"data":16918,"marks":16919,"value":16921,"nodeType":1906},{},[16920],{"type":12269},"Dynamo architecture ",{"data":16923,"content":16924,"nodeType":1915},{"uri":16713},[16925],{"data":16926,"marks":16927,"value":16713,"nodeType":1906},{},[16928],{"type":12269},{"data":16930,"marks":16931,"value":2030,"nodeType":1906},{},[],{"data":16933,"content":16934,"nodeType":16658},{},[],{"data":16936,"content":16937,"nodeType":12207},{},[16938],{"data":16939,"marks":16940,"value":16941,"nodeType":1906},{},[],"Architecting For KV$ Offload",{"data":16943,"content":16944,"nodeType":12305},{},[16945],{"data":16946,"marks":16947,"value":16948,"nodeType":1906},{},[],"General Architecture Considerations",{"data":16950,"content":16951,"nodeType":13998},{},[16952,16962,16972,16982],{"data":16953,"content":16954,"nodeType":12173},{},[16955],{"data":16956,"content":16957,"nodeType":1920},{},[16958],{"data":16959,"marks":16960,"value":16961,"nodeType":1906},{},[],"KV$ is stored in large blocks, making KV$ offload a workload that demands high throughput",{"data":16963,"content":16964,"nodeType":12173},{},[16965],{"data":16966,"content":16967,"nodeType":1920},{},[16968],{"data":16969,"marks":16970,"value":16971,"nodeType":1906},{},[],"The network and supporting storage subsystems must scale with\u002Fto the count of GPUs",{"data":16973,"content":16974,"nodeType":12173},{},[16975],{"data":16976,"content":16977,"nodeType":1920},{},[16978],{"data":16979,"marks":16980,"value":16981,"nodeType":1906},{},[],"Each host’s network access to storage will determine its TTFT performance",{"data":16983,"content":16984,"nodeType":12173},{},[16985],{"data":16986,"content":16987,"nodeType":1920},{},[16988],{"data":16989,"marks":16990,"value":16991,"nodeType":1906},{},[],"KV$ carries conversation information and should be encrypted using an encryption key per-customer, following the best practices of securing user information",{"data":16993,"content":16994,"nodeType":12305},{},[16995],{"data":16996,"marks":16997,"value":16998,"nodeType":1906},{},[],"VAST Data Architecture Considerations",{"data":17000,"content":17001,"nodeType":13998},{},[17002,17023,17045],{"data":17003,"content":17004,"nodeType":12173},{},[17005],{"data":17006,"content":17007,"nodeType":1920},{},[17008,17012,17020],{"data":17009,"marks":17010,"value":17011,"nodeType":1906},{},[],"Any VAST cluster supports KV$ offload today via Dynamo’s transfer library (",{"data":17013,"content":17015,"nodeType":1915},{"uri":17014},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fnixl",[17016],{"data":17017,"marks":17018,"value":17019,"nodeType":1906},{},[],"NIXL",{"data":17021,"marks":17022,"value":16767,"nodeType":1906},{},[],{"data":17024,"content":17025,"nodeType":12173},{},[17026],{"data":17027,"content":17028,"nodeType":1920},{},[17029,17033,17041],{"data":17030,"marks":17031,"value":17032,"nodeType":1906},{},[],"Use the ",{"data":17034,"content":17036,"nodeType":1915},{"uri":17035},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002FIntro.html",[17037],{"data":17038,"marks":17039,"value":17040,"nodeType":1906},{},[],"VAST NFS",{"data":17042,"marks":17043,"value":17044,"nodeType":1906},{},[]," driver, to enable multipathing to saturate the IO needs of any GPU machine",{"data":17046,"content":17047,"nodeType":12173},{},[17048],{"data":17049,"content":17050,"nodeType":1920},{},[17051],{"data":17052,"marks":17053,"value":17054,"nodeType":1906},{},[],"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":17056,"content":17057,"nodeType":16658},{},[],{"data":17059,"content":17060,"nodeType":12207},{},[17061],{"data":17062,"marks":17063,"value":17064,"nodeType":1906},{},[],"Calculating KV$ Footprint",{"data":17066,"content":17067,"nodeType":1920},{},[17068],{"data":17069,"marks":17070,"value":17071,"nodeType":1906},{},[],"KV$ creates gigabytes of data per prompt and is strongly dependent on model parameters:",{"data":17073,"content":17074,"nodeType":1920},{},[17075],{"data":17076,"marks":17077,"value":17078,"nodeType":1906},{},[],"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":17080,"content":17081,"nodeType":1920},{},[17082],{"data":17083,"marks":17084,"value":17085,"nodeType":1906},{},[],"Size per Token = 126 * 2 ( for Keys and Values)  * 8 * 128 * 2 (bytes) = 516,096 bytes",{"data":17087,"content":17088,"nodeType":1920},{},[17089,17093,17098],{"data":17090,"marks":17091,"value":17092,"nodeType":1906},{},[],"This results in approximately ",{"data":17094,"marks":17095,"value":17097,"nodeType":1906},{},[17096],{"type":12205},"504 KiB per token",{"data":17099,"marks":17100,"value":17101,"nodeType":1906},{},[],". For a single user utilizing a full 127,188-token context window, the cache becomes massive:",{"data":17103,"content":17104,"nodeType":1920},{},[17105,17109,17113],{"data":17106,"marks":17107,"value":17108,"nodeType":1906},{},[],"504KiB\u002Ftoken * 127,188 tokens ≈",{"data":17110,"marks":17111,"value":16010,"nodeType":1906},{},[17112],{"type":12205},{"data":17114,"marks":17115,"value":17116,"nodeType":1906},{},[]," 61 GB",{"data":17118,"content":17119,"nodeType":12207},{},[17120],{"data":17121,"marks":17122,"value":17124,"nodeType":1906},{},[17123],{"type":12205},"Modeling the \"Data Dump\" Scenarios",{"data":17126,"content":17127,"nodeType":1920},{},[17128],{"data":17129,"marks":17130,"value":17131,"nodeType":1906},{},[],"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":17133,"content":17134,"nodeType":12184},{},[17135,17150],{"data":17136,"content":17137,"nodeType":12173},{},[17138],{"data":17139,"content":17140,"nodeType":1920},{},[17141,17146],{"data":17142,"marks":17143,"value":17145,"nodeType":1906},{},[17144],{"type":12205},"The 'Power User' Returns:",{"data":17147,"marks":17148,"value":17149,"nodeType":1906},{},[]," 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":17151,"content":17152,"nodeType":12173},{},[17153],{"data":17154,"content":17155,"nodeType":1920},{},[17156,17161],{"data":17157,"marks":17158,"value":17160,"nodeType":1906},{},[17159],{"type":12205},"The RAG Workflow:",{"data":17162,"marks":17163,"value":17164,"nodeType":1906},{},[]," 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":17166,"content":17167,"nodeType":16658},{},[],{"data":17169,"content":17170,"nodeType":12305},{},[17171],{"data":17172,"marks":17173,"value":17174,"nodeType":1906},{},[],"How Much Capacity Do You Need?",{"data":17176,"content":17177,"nodeType":1920},{},[17178],{"data":17179,"marks":17180,"value":17181,"nodeType":1906},{},[],"The following is an example formula that can guide customers in sizing for KV$:",{"data":17183,"content":17184,"nodeType":1920},{},[17185,17190],{"data":17186,"marks":17187,"value":17189,"nodeType":1906},{},[17188],{"type":12205},"Total Capacity = ",{"data":17191,"marks":17192,"value":17193,"nodeType":1906},{},[],"Number of Users * Average KV$ per User * Retention Multiplier",{"data":17195,"content":17196,"nodeType":1920},{},[17197],{"data":17198,"marks":17199,"value":17200,"nodeType":1906},{},[],"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":17202,"content":17203,"nodeType":12184},{},[17204,17228,17243],{"data":17205,"content":17206,"nodeType":12173},{},[17207],{"data":17208,"content":17209,"nodeType":1920},{},[17210,17215,17219,17224],{"data":17211,"marks":17212,"value":17214,"nodeType":1906},{},[17213],{"type":12205},"Average KV$ per Prompt:",{"data":17216,"marks":17217,"value":17218,"nodeType":1906},{},[]," 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":17220,"marks":17221,"value":17223,"nodeType":1906},{},[17222],{"type":12269},"average",{"data":17225,"marks":17226,"value":17227,"nodeType":1906},{},[]," for a prompt is 64K tokens, roughly 30 GB",{"data":17229,"content":17230,"nodeType":12173},{},[17231],{"data":17232,"content":17233,"nodeType":1920},{},[17234,17239],{"data":17235,"marks":17236,"value":17238,"nodeType":1906},{},[17237],{"type":12205},"Retention Multiplier:",{"data":17240,"marks":17241,"value":17242,"nodeType":1906},{},[]," We'll use 15 to provide a good user experience",{"data":17244,"content":17245,"nodeType":12173},{},[17246],{"data":17247,"content":17248,"nodeType":1920},{},[17249,17254,17258],{"data":17250,"marks":17251,"value":17253,"nodeType":1906},{},[17252],{"type":12205},"Calculation:",{"data":17255,"marks":17256,"value":17257,"nodeType":1906},{},[]," 100,000 users * 30 GB * 15 = 45,000,000 GB = ",{"data":17259,"marks":17260,"value":17262,"nodeType":1906},{},[17261],{"type":12205},"45 PB",{"data":17264,"content":17265,"nodeType":1920},{},[17266],{"data":17267,"marks":17268,"value":17269,"nodeType":1906},{},[],"To support 100,000 users\u002Fagents on a large-context model with a robust conversation history, you need 45 petabytes of storage. ",{"data":17271,"content":17272,"nodeType":16658},{},[],{"data":17274,"content":17275,"nodeType":12207},{},[17276],{"data":17277,"marks":17278,"value":17279,"nodeType":1906},{},[],"Understanding the KV$ I\u002FO Profile",{"data":17281,"content":17282,"nodeType":1920},{},[17283,17288],{"data":17284,"marks":17285,"value":17287,"nodeType":1906},{},[17286],{"type":12205},"Asynchronous Writes (Eviction):",{"data":17289,"marks":17290,"value":17291,"nodeType":1906},{},[]," 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":17293,"content":17294,"nodeType":1920},{},[17295],{"data":17296,"marks":17297,"value":17298,"nodeType":1906},{},[],"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":17300,"content":17301,"nodeType":1920},{},[17302,17307],{"data":17303,"marks":17304,"value":17306,"nodeType":1906},{},[17305],{"type":12205},"Total Cache per Token (all 126 layers):",{"data":17308,"marks":17309,"value":17310,"nodeType":1906},{},[]," 504 KiB",{"data":17312,"content":17313,"nodeType":1920},{},[17314,17319],{"data":17315,"marks":17316,"value":17318,"nodeType":1906},{},[17317],{"type":12205},"Total Cache per Block (16 tokens):",{"data":17320,"marks":17321,"value":17322,"nodeType":1906},{},[]," 504 KiB\u002Ftoken * 16 tokens ≈ 8 MB",{"data":17324,"content":17325,"nodeType":1920},{},[17326,17331],{"data":17327,"marks":17328,"value":17330,"nodeType":1906},{},[17329],{"type":12205},"Data Written per GPU per Evicted Block (8 ways):",{"data":17332,"marks":17333,"value":17334,"nodeType":1906},{},[]," 8064 KiB \u002F 8 GPUs ≈ 1 MB ",{"data":17336,"content":17337,"nodeType":1920},{},[17338,17343,17347,17352,17356,17361],{"data":17339,"marks":17340,"value":17342,"nodeType":1906},{},[17341],{"type":12205},"Latency-Critical Reads (Retrieval)",{"data":17344,"marks":17345,"value":17346,"nodeType":1906},{},[],": When a user session\u002Fconversation is reactivated, the system must fetch the offloaded KV$. While modern inference engines utilize pipeline parallelism and batching to keep the GPU compute units active with ",{"data":17348,"marks":17349,"value":17351,"nodeType":1906},{},[17350],{"type":12269},"other",{"data":17353,"marks":17354,"value":17355,"nodeType":1906},{},[]," concurrent requests, the TTFT for the ",{"data":17357,"marks":17358,"value":17360,"nodeType":1906},{},[17359],{"type":12269},"specific",{"data":17362,"marks":17363,"value":17364,"nodeType":1906},{},[]," re-activated user\u002Fconversation is entirely bound by how fast this data can be retrieved.",{"data":17366,"content":17367,"nodeType":1920},{},[17368],{"data":17369,"marks":17370,"value":17371,"nodeType":1906},{},[],"Therefore, the goal is not to maximize IOPS (processing many small requests) and overly focus on latency, but to maximize throughput (delivering a massive stream of data instantly). AI inference systems need to stream GBs of data linearly into HBM to unblock that specific sequence. This is a bandwidth-intensive workload where the storage tier must deliver the entire KV$ at line-rate to minimize the response time perceived by the user.",{"data":17373,"content":17374,"nodeType":1920},{},[17375],{"data":17376,"marks":17377,"value":17379,"nodeType":1906},{},[17378],{"type":12269},"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":17381,"content":17382,"nodeType":16658},{},[],{"data":17384,"content":17385,"nodeType":12207},{},[17386],{"data":17387,"marks":17388,"value":17389,"nodeType":1906},{},[],"Real-World Performance: VAST and Dynamo in Action",{"data":17391,"content":17392,"nodeType":1920},{},[17393],{"data":17394,"marks":17395,"value":17396,"nodeType":1906},{},[],"Hardware:",{"data":17398,"content":17399,"nodeType":12184},{},[17400,17415,17430,17445,17460,17475,17490,17505],{"data":17401,"content":17402,"nodeType":12173},{},[17403],{"data":17404,"content":17405,"nodeType":1920},{},[17406,17411],{"data":17407,"marks":17408,"value":17410,"nodeType":1906},{},[17409],{"type":12205},"Client system:",{"data":17412,"marks":17413,"value":17414,"nodeType":1906},{},[]," NVIDIA HGX™ H100",{"data":17416,"content":17417,"nodeType":12173},{},[17418],{"data":17419,"content":17420,"nodeType":1920},{},[17421,17426],{"data":17422,"marks":17423,"value":17425,"nodeType":1906},{},[17424],{"type":12205},"GPUs:",{"data":17427,"marks":17428,"value":17429,"nodeType":1906},{},[]," 8x NVIDIA HGX H100 80GB HBM3",{"data":17431,"content":17432,"nodeType":12173},{},[17433],{"data":17434,"content":17435,"nodeType":1920},{},[17436,17441],{"data":17437,"marks":17438,"value":17440,"nodeType":1906},{},[17439],{"type":12205},"Network link",{"data":17442,"marks":17443,"value":17444,"nodeType":1906},{},[],": 2x100Gbit\u002Fsec Ethernet, Bluefied-3 integrated connectX",{"data":17446,"content":17447,"nodeType":12173},{},[17448],{"data":17449,"content":17450,"nodeType":1920},{},[17451,17456],{"data":17452,"marks":17453,"value":17455,"nodeType":1906},{},[17454],{"type":12205},"Software:",{"data":17457,"marks":17458,"value":17459,"nodeType":1906},{},[]," NVIDIA Dynamo pre-release 0.7.0 (Nov 21, 2025, modified) + vLLM v0.11.0 (modified) + NIXL 0.7.1",{"data":17461,"content":17462,"nodeType":12173},{},[17463],{"data":17464,"content":17465,"nodeType":1920},{},[17466,17471],{"data":17467,"marks":17468,"value":17470,"nodeType":1906},{},[17469],{"type":12205},"Model:",{"data":17472,"marks":17473,"value":17474,"nodeType":1906},{},[]," clowman\u002FLlama-3.1-405B-Instruct-AWQ-Int4",{"data":17476,"content":17477,"nodeType":12173},{},[17478],{"data":17479,"content":17480,"nodeType":1920},{},[17481,17486],{"data":17482,"marks":17483,"value":17485,"nodeType":1906},{},[17484],{"type":12205},"Run mode:",{"data":17487,"marks":17488,"value":17489,"nodeType":1906},{},[]," Tensor parallelism across 8 GPUs",{"data":17491,"content":17492,"nodeType":12173},{},[17493],{"data":17494,"content":17495,"nodeType":1920},{},[17496,17501],{"data":17497,"marks":17498,"value":17500,"nodeType":1906},{},[17499],{"type":12205},"Storage:",{"data":17502,"marks":17503,"value":17504,"nodeType":1906},{},[]," NFS (TCP, VAST NFS multipath, VAST server)",{"data":17506,"content":17507,"nodeType":12173},{},[17508],{"data":17509,"content":17510,"nodeType":1920},{},[17511,17516],{"data":17512,"marks":17513,"value":17515,"nodeType":1906},{},[17514],{"type":12205},"Prompt:",{"data":17517,"marks":17518,"value":17519,"nodeType":1906},{},[]," 127,188 tokens",{"data":17521,"content":17522,"nodeType":12305},{},[17523],{"data":17524,"marks":17525,"value":17526,"nodeType":1906},{},[],"Results",{"data":17528,"content":17529,"nodeType":1920},{},[17530,17534,17541,17545,17550,17554,17559],{"data":17531,"marks":17532,"value":17533,"nodeType":1906},{},[],"Collaborating on this configuration with our partner ",{"data":17535,"content":17537,"nodeType":1915},{"uri":17536},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Fcoreweave",[17538],{"data":17539,"marks":17540,"value":8151,"nodeType":1906},{},[],{"data":17542,"marks":17543,"value":17544,"nodeType":1906},{},[],", VAST consistently saturates the network during KV$ retrieval, sustaining ",{"data":17546,"marks":17547,"value":17549,"nodeType":1906},{},[17548],{"type":12205},"over 90% of line rate (181 Gbps)",{"data":17551,"marks":17552,"value":17553,"nodeType":1906},{},[]," and peaking at ",{"data":17555,"marks":17556,"value":17558,"nodeType":1906},{},[17557],{"type":12205},"~99% (198 Gbps)",{"data":17560,"marks":17561,"value":17562,"nodeType":1906},{},[]," on a 200 Gbps link. ",{"data":17564,"content":17583,"nodeType":12909},{"target":17565},{"metadata":17566,"sys":17569,"fields":17576},{"tags":17567,"concepts":17568},[],[],{"space":17570,"id":17572,"type":933,"createdAt":17573,"updatedAt":17573,"environment":17574,"publishedVersion":846,"revision":1135,"locale":672},{"sys":17571},{"type":656,"linkType":657,"id":658},"6DWr3VdK0ZzHJfYC0Wh4a6","2025-12-16T20:19:33.551Z",{"sys":17575},{"id":664,"type":656,"linkType":665},{"title":16476,"description":16476,"file":17577},{"url":17578,"details":17579,"fileName":17582,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWr3VdK0ZzHJfYC0Wh4a6\u002Ff41d8b35caefaba50dcc1245b279fb39\u002Fnvidia-dynamo-vast-scalable-optimized-inference-2.webp",{"size":17580,"image":17581},120100,{"width":1143,"height":1144},"nvidia-dynamo-vast-scalable-optimized-inference-2.webp",[],{"data":17585,"content":17586,"nodeType":1920},{},[17587,17592],{"data":17588,"marks":17589,"value":17591,"nodeType":1906},{},[17590],{"type":12205},"Note:",{"data":17593,"marks":17594,"value":17595,"nodeType":1906},{},[]," 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":17597,"content":17598,"nodeType":1920},{},[17599],{"data":17600,"marks":17601,"value":17602,"nodeType":1906},{},[],"As relates to the inference user experience, we observed the following performance: ",{"data":17604,"content":17605,"nodeType":12184},{},[17606,17616,17626,17636],{"data":17607,"content":17608,"nodeType":12173},{},[17609],{"data":17610,"content":17611,"nodeType":1920},{},[17612],{"data":17613,"marks":17614,"value":17615,"nodeType":1906},{},[],"TTFT with prefill compute: 62 seconds",{"data":17617,"content":17618,"nodeType":12173},{},[17619],{"data":17620,"content":17621,"nodeType":1920},{},[17622],{"data":17623,"marks":17624,"value":17625,"nodeType":1906},{},[],"TTFT with KV$ load from VAST: 3 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Data",{"metadata":18721,"sys":18724,"fields":18733},{"tags":18722,"concepts":18723},[],[],{"space":18725,"id":18727,"type":14,"createdAt":18728,"updatedAt":18728,"environment":18729,"publishedVersion":846,"revision":1135,"contentType":18731,"locale":672},{"sys":18726},{"type":656,"linkType":657,"id":658},"49WMtGrGgLpQCKYWV8q8MY","2025-12-17T08:17:38.419Z",{"sys":18730},{"id":664,"type":656,"linkType":665},{"sys":18732},{"type":656,"linkType":670,"id":12083},{"entryTitle":18716,"image":18734},{"metadata":18735,"sys":18738,"fields":18745},{"tags":18736,"concepts":18737},[],[],{"space":18739,"id":18741,"type":933,"createdAt":18742,"updatedAt":18742,"environment":18743,"publishedVersion":846,"revision":1135,"locale":672},{"sys":18740},{"type":656,"linkType":657,"id":658},"EUJ67K91gpFgVwYYJH4mT","2025-12-17T08:17:33.795Z",{"sys":18744},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":18746},{"url":18747,"details":18748,"fileName":18751,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FEUJ67K91gpFgVwYYJH4mT\u002F41f162bd643e8873e40b653a6c52bcdc\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai.webp",{"size":18749,"image":18750},127378,{"width":1143,"height":1144},"simplifying-data-management-with-vast-and-nvidia-run-ai.webp",{"data":18753,"content":18754,"nodeType":1921},{},[18755,18762,18782,18789,18796,18808,18830,18838,18850,18857,18864,18922,18929,18949,18956,18963,18970,18977,19000,19008,19030,19037,19044,19063,19070,19077,19084,19103,19126,19148,19178,19185,19204,19216,19224,19257,19278,19290,19297,19304,19323,19330,19361,19391,19398,19405,19424,19443,19450,19457,19489,19496,19503,19510,19517,19524,19531,19538,19545,19553,19570,19578,19585,19606,19613,19635,19655,19662,19685,19693,19700,19763,19771,19778,19785,19793,19813,19821,19828,19836,19843,19865,19872,19891,19898,19906,19928,19935,19954,19961],{"data":18756,"content":18757,"nodeType":1920},{},[18758],{"data":18759,"marks":18760,"value":18761,"nodeType":1906},{},[],"Managing data for AI workloads can be complex, but combining the VAST CSI driver and NVIDIA Run:AI’s data source abstraction makes the process dramatically simpler. ",{"data":18763,"content":18764,"nodeType":1920},{},[18765,18769,18778],{"data":18766,"marks":18767,"value":18768,"nodeType":1906},{},[],"Together, ",{"data":18770,"content":18772,"nodeType":1915},{"uri":18771},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-run-ai-and-vast",[18773],{"data":18774,"marks":18775,"value":18777,"nodeType":1906},{},[18776],{"type":12327},"VAST and NVIDIA Run.ai",{"data":18779,"marks":18780,"value":18781,"nodeType":1906},{},[]," streamline provisioning, unify storage access, and remove low-level Kubernetes friction through seamless integration with VAST’s multi-protocol architecture and NVIDIA Run.ai’s PVC-based data management. ",{"data":18783,"content":18784,"nodeType":12207},{},[18785],{"data":18786,"marks":18787,"value":18788,"nodeType":1906},{},[],"Simplified Provisioning and Abstraction",{"data":18790,"content":18791,"nodeType":1920},{},[18792],{"data":18793,"marks":18794,"value":18795,"nodeType":1906},{},[],"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.",{"data":18797,"content":18798,"nodeType":1920},{},[18799,18804],{"data":18800,"marks":18801,"value":18803,"nodeType":1906},{},[18802],{"type":12205},"VAST Multi-Protocol Efficiency:",{"data":18805,"marks":18806,"value":18807,"nodeType":1906},{},[]," The VAST CSI driver can provision both block volumes and file system (NFS) volumes (for shared access and model serving) from the same underlying data platform. This simplifies the administrator’s job as they only manage one data platform for all AI needs. Using VAST CSI, the administrator simply selects the appropriate storage class from the list and then provisions a new or existing PVC based on that selection.",{"data":18809,"content":18829,"nodeType":12909},{"target":18810},{"metadata":18811,"sys":18814,"fields":18822},{"tags":18812,"concepts":18813},[],[],{"space":18815,"id":18817,"type":933,"createdAt":18818,"updatedAt":18819,"environment":18820,"publishedVersion":711,"revision":900,"locale":672},{"sys":18816},{"type":656,"linkType":657,"id":658},"hLYNpMJCZalHaQRLe7zbb","2025-12-16T13:56:03.037Z","2025-12-17T08:24:19.414Z",{"sys":18821},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":18823},{"url":18824,"details":18825,"fileName":18828,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FhLYNpMJCZalHaQRLe7zbb\u002Fd9e8064fe548a4066f12ced7bd876f0f\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",{"size":18826,"image":18827},19930,{"width":1143,"height":1144},"simplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",[],{"data":18831,"content":18832,"nodeType":1920},{},[18833],{"data":18834,"marks":18835,"value":18837,"nodeType":1906},{},[18836],{"type":12269},"In this example, the storage class for both block and file systems is installed using the VAST CSI.",{"data":18839,"content":18840,"nodeType":1920},{},[18841,18846],{"data":18842,"marks":18843,"value":18845,"nodeType":1906},{},[18844],{"type":12205},"NVIDIA Run:AI PVC Abstraction:",{"data":18847,"marks":18848,"value":18849,"nodeType":1906},{},[]," NVIDIA Run:AI abstracts the complexity of Kubernetes PVCs via its Data Sources feature. An administrator pre-defines PVC type Data Sources (backed by VAST StorageClasses) and exposes them to researchers. Researcher Self-Service: Researchers request data access simply by name (e.g., “project-a-pvc”) rather than needing to know the complex details of the VAST provisioner, StorageClass mount parameters, or volume binding.",{"data":18851,"content":18852,"nodeType":12207},{},[18853],{"data":18854,"marks":18855,"value":18856,"nodeType":1906},{},[],"Consistency and Advanced Feature Utilization",{"data":18858,"content":18859,"nodeType":1920},{},[18860],{"data":18861,"marks":18862,"value":18863,"nodeType":1906},{},[],"Beyond simplifying provisioning, the integration also ensures that advanced VAST features are consistently available through Kubernetes without adding operational overhead.",{"data":18865,"content":18866,"nodeType":12184},{},[18867,18907],{"data":18868,"content":18869,"nodeType":12173},{},[18870],{"data":18871,"content":18872,"nodeType":1920},{},[18873,18878,18882,18891,18894,18903],{"data":18874,"marks":18875,"value":18877,"nodeType":1906},{},[18876],{"type":12205},"Snapshots and Clones:",{"data":18879,"marks":18880,"value":18881,"nodeType":1906},{},[]," VAST’s native storage efficiency and performance features, such as high-performance ",{"data":18883,"content":18885,"nodeType":1915},{"uri":18884},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-snapshots-to-vasts-element-store",[18886],{"data":18887,"marks":18888,"value":18890,"nodeType":1906},{},[18889],{"type":12327},"snapshots",{"data":18892,"marks":18893,"value":16615,"nodeType":1906},{},[],{"data":18895,"content":18897,"nodeType":1915},{"uri":18896},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fglobal-snapshot-clones-intelligent-multi-site-data-availability",[18898],{"data":18899,"marks":18900,"value":18902,"nodeType":1906},{},[18901],{"type":12327},"clones",{"data":18904,"marks":18905,"value":18906,"nodeType":1906},{},[],", are exposed directly through the CSI interface. The administrator manages these capabilities via standard Kubernetes VolumeSnapshot objects, ensuring fast data iteration and recovery without needing to learn separate VAST management tools for basic operations.",{"data":18908,"content":18909,"nodeType":12173},{},[18910],{"data":18911,"content":18912,"nodeType":1920},{},[18913,18918],{"data":18914,"marks":18915,"value":18917,"nodeType":1906},{},[18916],{"type":12205},"Simplified Model Lifecycle:",{"data":18919,"marks":18920,"value":18921,"nodeType":1906},{},[]," The ability to instantly clone a trained model PVC or restore from a snapshot makes managing the lifecycle of development, training, and deployment seamless and efficient for the administrator.",{"data":18923,"content":18924,"nodeType":12207},{},[18925],{"data":18926,"marks":18927,"value":18928,"nodeType":1906},{},[],"Multi-Tenancy on VAST",{"data":18930,"content":18931,"nodeType":1920},{},[18932,18936,18945],{"data":18933,"marks":18934,"value":18935,"nodeType":1906},{},[],"With provisioning and advanced features handled, the next challenge is ensuring each project or team has secure, isolated access to storage. VAST’s ",{"data":18937,"content":18939,"nodeType":1915},{"uri":18938},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fvast-4-6-multi-tenant-qos-demo",[18940],{"data":18941,"marks":18942,"value":18944,"nodeType":1906},{},[18943],{"type":12327},"native multi-tenancy",{"data":18946,"marks":18947,"value":18948,"nodeType":1906},{},[]," makes this simple.",{"data":18950,"content":18951,"nodeType":1920},{},[18952],{"data":18953,"marks":18954,"value":18955,"nodeType":1906},{},[],"The VAST Cluster’s multi-tenancy directly supports the needs of a job orchestrator like NVIDIA Run:AI by providing complete administrative and data isolation. Tenants are defined within VAST as isolated domains, each having its own file system hierarchy, unique security policies and performance controls (QoS). ",{"data":18957,"content":18958,"nodeType":1920},{},[18959],{"data":18960,"marks":18961,"value":18962,"nodeType":1906},{},[],"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.",{"data":18964,"content":18965,"nodeType":1920},{},[18966],{"data":18967,"marks":18968,"value":18969,"nodeType":1906},{},[],"First, we configure two tenants, differentiated by vippool, and create two corresponding storage classes, each assigned to one of the tenants.",{"data":18971,"content":18972,"nodeType":1920},{},[18973],{"data":18974,"marks":18975,"value":18976,"nodeType":1906},{},[],"Subsequently, we assign two projects in NVIDIA Run:AI, one for each storage class. This configuration ensures definitive data isolation between the projects.",{"data":18978,"content":18999,"nodeType":12909},{"target":18979},{"metadata":18980,"sys":18983,"fields":18991},{"tags":18981,"concepts":18982},[],[],{"space":18984,"id":18986,"type":933,"createdAt":18987,"updatedAt":18988,"environment":18989,"publishedVersion":711,"revision":900,"locale":672},{"sys":18985},{"type":656,"linkType":657,"id":658},"6yc2QsKV9sGw5SVVSci6Vs","2025-12-16T14:00:19.048Z","2025-12-17T08:24:05.430Z",{"sys":18990},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":18992},{"url":18993,"details":18994,"fileName":18998,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6yc2QsKV9sGw5SVVSci6Vs\u002F8445177ac0cbc88c3ba92b94d977cbe8\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",{"size":18995,"image":18996},34318,{"width":1143,"height":18997},734,"simplifying-data-management-with-vast-and-nvidia-run-ai-2.webp",[],{"data":19001,"content":19002,"nodeType":1920},{},[19003],{"data":19004,"marks":19005,"value":19007,"nodeType":1906},{},[19006],{"type":12269},"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.",{"data":19009,"content":19029,"nodeType":12909},{"target":19010},{"metadata":19011,"sys":19014,"fields":19022},{"tags":19012,"concepts":19013},[],[],{"space":19015,"id":19017,"type":933,"createdAt":19018,"updatedAt":19019,"environment":19020,"publishedVersion":711,"revision":900,"locale":672},{"sys":19016},{"type":656,"linkType":657,"id":658},"kdNFeI1EdTQVWw3l6H2HJ","2025-12-16T14:01:52.688Z","2025-12-17T08:23:52.260Z",{"sys":19021},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19023},{"url":19024,"details":19025,"fileName":19028,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FkdNFeI1EdTQVWw3l6H2HJ\u002F5bc2cbf1b6c6a1c9da29b8fcf30a3269\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",{"size":19026,"image":19027},36150,{"width":1143,"height":1144},"simplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",[],{"data":19031,"content":19032,"nodeType":12207},{},[19033],{"data":19034,"marks":19035,"value":19036,"nodeType":1906},{},[],"Controlling Tenants’ QoS",{"data":19038,"content":19039,"nodeType":1920},{},[19040],{"data":19041,"marks":19042,"value":19043,"nodeType":1906},{},[],"Once tenants are defined, the next step is ensuring each project gets the right performance. ",{"data":19045,"content":19046,"nodeType":1920},{},[19047,19050,19059],{"data":19048,"marks":19049,"value":2030,"nodeType":1906},{},[],{"data":19051,"content":19053,"nodeType":1915},{"uri":19052},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fenforcing-io-discipline-dynamic-qos-model-training",[19054],{"data":19055,"marks":19056,"value":19058,"nodeType":1906},{},[19057],{"type":12327},"VAST’s QoS policies",{"data":19060,"marks":19061,"value":19062,"nodeType":1906},{},[]," 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.",{"data":19064,"content":19065,"nodeType":1920},{},[19066],{"data":19067,"marks":19068,"value":19069,"nodeType":1906},{},[],"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.",{"data":19071,"content":19072,"nodeType":1920},{},[19073],{"data":19074,"marks":19075,"value":19076,"nodeType":1906},{},[],"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.",{"data":19078,"content":19079,"nodeType":12207},{},[19080],{"data":19081,"marks":19082,"value":19083,"nodeType":1906},{},[],"Multiple Sites \u002F Global Namespace Integration",{"data":19085,"content":19086,"nodeType":1920},{},[19087,19091,19099],{"data":19088,"marks":19089,"value":19090,"nodeType":1906},{},[],"While we’ve concentrated on local PVC workloads with NVIDIA Run:AI, VAST’s global namespace (",{"data":19092,"content":19094,"nodeType":1915},{"uri":19093},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdataspace",[19095],{"data":19096,"marks":19097,"value":3525,"nodeType":1906},{},[19098],{"type":12327},{"data":19100,"marks":19101,"value":19102,"nodeType":1906},{},[],") is vital for extending resource utilization to remote sites and enabling advanced Run:AI scenarios. Let’s discuss two scenarios: ",{"data":19104,"content":19105,"nodeType":12184},{},[19106,19116],{"data":19107,"content":19108,"nodeType":12173},{},[19109],{"data":19110,"content":19111,"nodeType":1920},{},[19112],{"data":19113,"marks":19114,"value":19115,"nodeType":1906},{},[],"Seamless multi-site burst compute for both training and inference",{"data":19117,"content":19118,"nodeType":12173},{},[19119],{"data":19120,"content":19121,"nodeType":1920},{},[19122],{"data":19123,"marks":19124,"value":19125,"nodeType":1906},{},[],"Resilient LLM Training",{"data":19127,"content":19147,"nodeType":12909},{"target":19128},{"metadata":19129,"sys":19132,"fields":19139},{"tags":19130,"concepts":19131},[],[],{"space":19133,"id":19135,"type":933,"createdAt":19136,"updatedAt":19136,"environment":19137,"publishedVersion":846,"revision":1135,"locale":672},{"sys":19134},{"type":656,"linkType":657,"id":658},"4VJFjVdk0kDHrqMAd5QX8L","2025-12-17T08:23:40.138Z",{"sys":19138},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19140},{"url":19141,"details":19142,"fileName":19146,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4VJFjVdk0kDHrqMAd5QX8L\u002Fb0e52250dfd10cb5363a5fc25784bb1e\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",{"size":19143,"image":19144},117848,{"width":1143,"height":19145},808,"simplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",[],{"data":19149,"content":19150,"nodeType":1920},{},[19151,19156,19162,19167,19173],{"data":19152,"marks":19153,"value":19155,"nodeType":1906},{},[19154],{"type":12269},"Run:AI can dynamically burst the job queue to a ",{"data":19157,"marks":19158,"value":19161,"nodeType":1906},{},[19159,19160],{"type":12269},{"type":12205},"secondary site (Site B)",{"data":19163,"marks":19164,"value":19166,"nodeType":1906},{},[19165],{"type":12269}," or a ",{"data":19168,"marks":19169,"value":19172,"nodeType":1906},{},[19170,19171],{"type":12269},{"type":12205},"cloud environment",{"data":19174,"marks":19175,"value":19177,"nodeType":1906},{},[19176],{"type":12269}," where more compute resources are available.",{"data":19179,"content":19180,"nodeType":12207},{},[19181],{"data":19182,"marks":19183,"value":19184,"nodeType":1906},{},[],"Burst Compute for Training and Inference",{"data":19186,"content":19187,"nodeType":1920},{},[19188,19192,19200],{"data":19189,"marks":19190,"value":19191,"nodeType":1906},{},[],"This is a classic use case enabled by the ",{"data":19193,"content":19195,"nodeType":1915},{"uri":19194},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fglobal-namespace-demo",[19196],{"data":19197,"marks":19198,"value":19199,"nodeType":1906},{},[],"global, persistent nature",{"data":19201,"marks":19202,"value":19203,"nodeType":1906},{},[]," of the VAST DataSpace.",{"data":19205,"content":19206,"nodeType":1920},{},[19207,19212],{"data":19208,"marks":19209,"value":19211,"nodeType":1906},{},[19210],{"type":12205},"Scenario:",{"data":19213,"marks":19214,"value":19215,"nodeType":1906},{},[]," 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.",{"data":19217,"content":19218,"nodeType":1920},{},[19219],{"data":19220,"marks":19221,"value":19223,"nodeType":1906},{},[19222],{"type":12205},"Solution:",{"data":19225,"content":19226,"nodeType":13998},{},[19227,19237,19247],{"data":19228,"content":19229,"nodeType":12173},{},[19230],{"data":19231,"content":19232,"nodeType":1920},{},[19233],{"data":19234,"marks":19235,"value":19236,"nodeType":1906},{},[],"A job on Site A writes its training checkpoints to the VAST file system mounted via NFS. ",{"data":19238,"content":19239,"nodeType":12173},{},[19240],{"data":19241,"content":19242,"nodeType":1920},{},[19243],{"data":19244,"marks":19245,"value":19246,"nodeType":1906},{},[],"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. ",{"data":19248,"content":19249,"nodeType":12173},{},[19250],{"data":19251,"content":19252,"nodeType":1920},{},[19253],{"data":19254,"marks":19255,"value":19256,"nodeType":1906},{},[],"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. ",{"data":19258,"content":19277,"nodeType":12909},{"target":19259},{"metadata":19260,"sys":19263,"fields":19270},{"tags":19261,"concepts":19262},[],[],{"space":19264,"id":19266,"type":933,"createdAt":19267,"updatedAt":19267,"environment":19268,"publishedVersion":846,"revision":1135,"locale":672},{"sys":19265},{"type":656,"linkType":657,"id":658},"68urmPYKyxmQ5RH1ZwDewH","2025-12-17T08:26:53.179Z",{"sys":19269},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19271},{"url":19272,"details":19273,"fileName":19276,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F68urmPYKyxmQ5RH1ZwDewH\u002Fdc8556b00c19ac8043e5539c5a41fe85\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",{"size":19274,"image":19275},41180,{"width":1143,"height":1144},"simplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",[],{"data":19279,"content":19280,"nodeType":1920},{},[19281,19286],{"data":19282,"marks":19283,"value":19285,"nodeType":1906},{},[19284],{"type":12205},"Benefit:",{"data":19287,"marks":19288,"value":19289,"nodeType":1906},{},[]," 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.",{"data":19291,"content":19292,"nodeType":12207},{},[19293],{"data":19294,"marks":19295,"value":19296,"nodeType":1906},{},[],"Resilient LLM Training: Checkpoint and Recover in Seconds",{"data":19298,"content":19299,"nodeType":1920},{},[19300],{"data":19301,"marks":19302,"value":19303,"nodeType":1906},{},[],"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.",{"data":19305,"content":19306,"nodeType":1920},{},[19307,19311,19319],{"data":19308,"marks":19309,"value":19310,"nodeType":1906},{},[],"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 ",{"data":19312,"content":19314,"nodeType":1915},{"uri":19313},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency",[19315],{"data":19316,"marks":19317,"value":17733,"nodeType":1906},{},[19318],{"type":12327},{"data":19320,"marks":19321,"value":19322,"nodeType":1906},{},[]," to reproduce it.",{"data":19324,"content":19325,"nodeType":12305},{},[19326],{"data":19327,"marks":19328,"value":19329,"nodeType":1906},{},[],"The High Cost of Unplanned Interruptions",{"data":19331,"content":19332,"nodeType":1920},{},[19333,19337,19357],{"data":19334,"marks":19335,"value":19336,"nodeType":1906},{},[],"Failures during training aren’t reserved for hyperscale clusters - they can strike any environment, from a handful of GPUs to multi-region deployments. ",{"data":19338,"content":19340,"nodeType":1915},{"uri":19339},"https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fthe-llama-3-herd-of-models\u002F",[19341,19346,19352],{"data":19342,"marks":19343,"value":19345,"nodeType":1906},{},[19344],{"type":12327},"In Meta’s ",{"data":19347,"marks":19348,"value":19351,"nodeType":1906},{},[19349,19350],{"type":12327},{"type":12269},"Llama 3",{"data":19353,"marks":19354,"value":19356,"nodeType":1906},{},[19355],{"type":12327}," pretraining report",{"data":19358,"marks":19359,"value":19360,"nodeType":1906},{},[],", we get a glimpse into just how common these interruptions are:",{"data":19362,"content":19363,"nodeType":1920},{},[19364,19369,19375,19380,19386],{"data":19365,"marks":19366,"value":19368,"nodeType":1906},{},[19367],{"type":12269},"Over a 54-day window, Meta’s 16,384-GPU cluster recorded ",{"data":19370,"marks":19371,"value":19374,"nodeType":1906},{},[19372,19373],{"type":12269},{"type":12205},"466 job interruptions",{"data":19376,"marks":19377,"value":19379,"nodeType":1906},{},[19378],{"type":12269},", 90% of which were unplanned. GPU hardware alone caused ",{"data":19381,"marks":19382,"value":19385,"nodeType":1906},{},[19383,19384],{"type":12269},{"type":12205},"59%",{"data":19387,"marks":19388,"value":19390,"nodeType":1906},{},[19389],{"type":12269}," of those failures.",{"data":19392,"content":19393,"nodeType":1920},{},[19394],{"data":19395,"marks":19396,"value":19397,"nodeType":1906},{},[],"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.",{"data":19399,"content":19400,"nodeType":12305},{},[19401],{"data":19402,"marks":19403,"value":19404,"nodeType":1906},{},[],"Why Checkpointing Matters",{"data":19406,"content":19407,"nodeType":1920},{},[19408,19411,19420],{"data":19409,"marks":19410,"value":2030,"nodeType":1906},{},[],{"data":19412,"content":19414,"nodeType":1915},{"uri":19413},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fa-checkpoint-on-checkpoints-in-llms",[19415],{"data":19416,"marks":19417,"value":19419,"nodeType":1906},{},[19418],{"type":12327},"Checkpointing",{"data":19421,"marks":19422,"value":19423,"nodeType":1906},{},[]," - 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.",{"data":19425,"content":19426,"nodeType":1920},{},[19427,19430,19439],{"data":19428,"marks":19429,"value":2030,"nodeType":1906},{},[],{"data":19431,"content":19433,"nodeType":1915},{"uri":19432},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Foptimizing-checkpoint-bandwidth-for-llm-training",[19434],{"data":19435,"marks":19436,"value":19438,"nodeType":1906},{},[19437],{"type":12327},"Checkpoint strategies",{"data":19440,"marks":19441,"value":19442,"nodeType":1906},{},[]," 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.",{"data":19444,"content":19445,"nodeType":12305},{},[19446],{"data":19447,"marks":19448,"value":19449,"nodeType":1906},{},[],"The Storage Dilemma",{"data":19451,"content":19452,"nodeType":1920},{},[19453],{"data":19454,"marks":19455,"value":19456,"nodeType":1906},{},[],"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.",{"data":19458,"content":19459,"nodeType":1920},{},[19460,19464,19472,19476,19485],{"data":19461,"marks":19462,"value":19463,"nodeType":1906},{},[],"VAST bridges these gaps. VAST’s ",{"data":19465,"content":19466,"nodeType":1915},{"uri":14411},[19467],{"data":19468,"marks":19469,"value":19471,"nodeType":1906},{},[19470],{"type":12327},"disaggregated architecture",{"data":19473,"marks":19474,"value":19475,"nodeType":1906},{},[]," aggregates NVMe drives across an entire cluster into a single global namespace, providing parallel I\u002FO at tens of gigabytes per second plus ",{"data":19477,"content":19479,"nodeType":1915},{"uri":19478},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[19480],{"data":19481,"marks":19482,"value":19484,"nodeType":1906},{},[19483],{"type":12327},"built‑in erasure coding",{"data":19486,"marks":19487,"value":19488,"nodeType":1906},{},[]," for data durability.",{"data":19490,"content":19491,"nodeType":12305},{},[19492],{"data":19493,"marks":19494,"value":19495,"nodeType":1906},{},[],"Orchestration with NVIDIA Run:AI",{"data":19497,"content":19498,"nodeType":1920},{},[19499],{"data":19500,"marks":19501,"value":19502,"nodeType":1906},{},[],"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. ",{"data":19504,"content":19505,"nodeType":1920},{},[19506],{"data":19507,"marks":19508,"value":19509,"nodeType":1906},{},[],"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.",{"data":19511,"content":19512,"nodeType":12207},{},[19513],{"data":19514,"marks":19515,"value":19516,"nodeType":1906},{},[],"Fast, Cross‑Region Recovery in Action",{"data":19518,"content":19519,"nodeType":1920},{},[19520],{"data":19521,"marks":19522,"value":19523,"nodeType":1906},{},[],"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. ",{"data":19525,"content":19526,"nodeType":1920},{},[19527],{"data":19528,"marks":19529,"value":19530,"nodeType":1906},{},[],"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. ",{"data":19532,"content":19533,"nodeType":1920},{},[19534],{"data":19535,"marks":19536,"value":19537,"nodeType":1906},{},[],"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.",{"data":19539,"content":19540,"nodeType":1920},{},[19541],{"data":19542,"marks":19543,"value":19544,"nodeType":1906},{},[],"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.",{"data":19546,"content":19547,"nodeType":1920},{},[19548],{"data":19549,"marks":19550,"value":19552,"nodeType":1906},{},[19551],{"type":12205},"Putting it into Practice: A Step-by-Step Job Submission",{"data":19554,"content":19555,"nodeType":1920},{},[19556,19560,19567],{"data":19557,"marks":19558,"value":19559,"nodeType":1906},{},[],"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 ",{"data":19561,"content":19562,"nodeType":1915},{"uri":19313},[19563],{"data":19564,"marks":19565,"value":17733,"nodeType":1906},{},[19566],{"type":12327},{"data":19568,"marks":19569,"value":1919,"nodeType":1906},{},[],{"data":19571,"content":19572,"nodeType":1920},{},[19573],{"data":19574,"marks":19575,"value":19577,"nodeType":1906},{},[19576],{"type":12205},"Step 1: Creating a PVC with VAST CSI filesystem",{"data":19579,"content":19580,"nodeType":1920},{},[19581],{"data":19582,"marks":19583,"value":19584,"nodeType":1906},{},[],"Let’s create a PVC with VAST CSI filesystem and mount it to \u002Fmodel. ",{"data":19586,"content":19605,"nodeType":12909},{"target":19587},{"metadata":19588,"sys":19591,"fields":19598},{"tags":19589,"concepts":19590},[],[],{"space":19592,"id":19594,"type":933,"createdAt":19595,"updatedAt":19595,"environment":19596,"publishedVersion":846,"revision":1135,"locale":672},{"sys":19593},{"type":656,"linkType":657,"id":658},"77QPXz8kJ5suw2HsXf7PIO","2025-12-17T08:28:42.070Z",{"sys":19597},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19599},{"url":19600,"details":19601,"fileName":19604,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F77QPXz8kJ5suw2HsXf7PIO\u002F87bbdebb1642d12787d37b08e1feff92\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",{"size":19602,"image":19603},51050,{"width":1143,"height":1144},"simplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",[],{"data":19607,"content":19608,"nodeType":1920},{},[19609],{"data":19610,"marks":19611,"value":19612,"nodeType":1906},{},[],"Create a new training workload job with pytorch-one-gpu:",{"data":19614,"content":19634,"nodeType":12909},{"target":19615},{"metadata":19616,"sys":19619,"fields":19626},{"tags":19617,"concepts":19618},[],[],{"space":19620,"id":19622,"type":933,"createdAt":19623,"updatedAt":19623,"environment":19624,"publishedVersion":846,"revision":1135,"locale":672},{"sys":19621},{"type":656,"linkType":657,"id":658},"41lMeyZPrg1hwFMqvUlEdp","2025-12-17T08:30:29.308Z",{"sys":19625},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19627},{"url":19628,"details":19629,"fileName":19633,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F41lMeyZPrg1hwFMqvUlEdp\u002F8ed7d59c7fd81eff3525165efcfe2e2d\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",{"size":19630,"image":19631},12608,{"width":1143,"height":19632},521,"simplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",[],{"data":19636,"content":19637,"nodeType":1920},{},[19638,19642,19651],{"data":19639,"marks":19640,"value":19641,"nodeType":1906},{},[],"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 ",{"data":19643,"content":19645,"nodeType":1915},{"uri":19644},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency\u002Fblob\u002Fmain\u002Fmodel-loading\u002Fclone.sh",[19646],{"data":19647,"marks":19648,"value":19650,"nodeType":1906},{},[19649],{"type":12327},"in this repository",{"data":19652,"marks":19653,"value":19654,"nodeType":1906},{},[]," 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.",{"data":19656,"content":19657,"nodeType":1920},{},[19658],{"data":19659,"marks":19660,"value":19661,"nodeType":1906},{},[],"After this step completes, your VAST volume contains:",{"data":19663,"content":19664,"nodeType":12184},{},[19665,19675],{"data":19666,"content":19667,"nodeType":12173},{},[19668],{"data":19669,"content":19670,"nodeType":1920},{},[19671],{"data":19672,"marks":19673,"value":19674,"nodeType":1906},{},[],"\u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct - The base model (one-time download)",{"data":19676,"content":19677,"nodeType":12173},{},[19678],{"data":19679,"content":19680,"nodeType":1920},{},[19681],{"data":19682,"marks":19683,"value":19684,"nodeType":1906},{},[],"\u002Fmodel\u002Fcheckpoints\u002F - Empty directory ready for checkpoints",{"data":19686,"content":19687,"nodeType":1920},{},[19688],{"data":19689,"marks":19690,"value":19692,"nodeType":1906},{},[19691],{"type":12205},"Step 2: Creating Training Script ",{"data":19694,"content":19695,"nodeType":1920},{},[19696],{"data":19697,"marks":19698,"value":19699,"nodeType":1906},{},[],"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; ",{"data":19701,"content":19702,"nodeType":12184},{},[19703,19718,19733,19748],{"data":19704,"content":19705,"nodeType":12173},{},[19706],{"data":19707,"content":19708,"nodeType":1920},{},[19709,19714],{"data":19710,"marks":19711,"value":19713,"nodeType":1906},{},[19712],{"type":12205},"Checkpoint frequency: ",{"data":19715,"marks":19716,"value":19717,"nodeType":1906},{},[],"Saves every 5 training steps to \u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F",{"data":19719,"content":19720,"nodeType":12173},{},[19721],{"data":19722,"content":19723,"nodeType":1920},{},[19724,19729],{"data":19725,"marks":19726,"value":19728,"nodeType":1906},{},[19727],{"type":12205},"Automatic resume logic: ",{"data":19730,"marks":19731,"value":19732,"nodeType":1906},{},[],"On startup, the script searches for existing checkpoints and automatically resumes from the latest one",{"data":19734,"content":19735,"nodeType":12173},{},[19736],{"data":19737,"content":19738,"nodeType":1920},{},[19739,19744],{"data":19740,"marks":19741,"value":19743,"nodeType":1906},{},[19742],{"type":12205},"LoRa fine-tuning:",{"data":19745,"marks":19746,"value":19747,"nodeType":1906},{},[]," Uses parameter-efficient LoRa adapters to fine-tune Llama 3.1 with minimal memory overhead",{"data":19749,"content":19750,"nodeType":12173},{},[19751],{"data":19752,"content":19753,"nodeType":1920},{},[19754,19759],{"data":19755,"marks":19756,"value":19758,"nodeType":1906},{},[19757],{"type":12205},"Distributed training ready:",{"data":19760,"marks":19761,"value":19762,"nodeType":1906},{},[]," Leverages torchrun for multi-GPU training via the launch.sh entrypoint",{"data":19764,"content":19765,"nodeType":1920},{},[19766],{"data":19767,"marks":19768,"value":19770,"nodeType":1906},{},[19769],{"type":12205},"Step 3: Build the Training Image (or Use Ours)",{"data":19772,"content":19773,"nodeType":1920},{},[19774],{"data":19775,"marks":19776,"value":19777,"nodeType":1906},{},[],"The training\u002F directory includes a Dockerfile that packages everything needed. ",{"data":19779,"content":19780,"nodeType":1920},{},[19781],{"data":19782,"marks":19783,"value":19784,"nodeType":1906},{},[],"If you want to build your own image:",{"data":19786,"content":19787,"nodeType":1920},{},[19788],{"data":19789,"marks":19790,"value":19792,"nodeType":1906},{},[19791],{"type":12423},"cd training \ndocker build --platform linux\u002Famd64 -t \u003CYOUR_REGISTRY>\u002Frunai-vast-training\ndocker push \u003CYOUR_REGISTRY>\u002Frunai-vast-training",{"data":19794,"content":19795,"nodeType":1920},{},[19796,19800,19809],{"data":19797,"marks":19798,"value":19799,"nodeType":1906},{},[],"Alternatively, you can use ",{"data":19801,"content":19803,"nodeType":1915},{"uri":19802},"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fekarabulut844\u002Frunai-vast-training",[19804],{"data":19805,"marks":19806,"value":19808,"nodeType":1906},{},[19807],{"type":12327},"our pre-built Docker image",{"data":19810,"marks":19811,"value":19812,"nodeType":1906},{},[]," that’s ready to go. ",{"data":19814,"content":19815,"nodeType":1920},{},[19816],{"data":19817,"marks":19818,"value":19820,"nodeType":1906},{},[19819],{"type":12205},"Step 4: Launch the Training Job",{"data":19822,"content":19823,"nodeType":1920},{},[19824],{"data":19825,"marks":19826,"value":19827,"nodeType":1906},{},[],"With the model loaded on VAST and the training image ready, submit your training job via Run:AI:",{"data":19829,"content":19830,"nodeType":1920},{},[19831],{"data":19832,"marks":19833,"value":19835,"nodeType":1906},{},[19834],{"type":12423},"runai training pytorch submit lora-fine-tune-model --existing-pvc \"claimname=$DATA,path=\u002Fmodel\" -i docker.io\u002F\u003CREGISTRY>\u002Frunai-vast-training -g 1",{"data":19837,"content":19838,"nodeType":1920},{},[19839],{"data":19840,"marks":19841,"value":19842,"nodeType":1906},{},[],"Once the job starts, you’ll see training logs showing the LoRa fine-tuning in progress. Every 5 steps, you’ll see checkpoint confirmations: ",{"data":19844,"content":19864,"nodeType":12909},{"target":19845},{"metadata":19846,"sys":19849,"fields":19856},{"tags":19847,"concepts":19848},[],[],{"space":19850,"id":19852,"type":933,"createdAt":19853,"updatedAt":19853,"environment":19854,"publishedVersion":846,"revision":1135,"locale":672},{"sys":19851},{"type":656,"linkType":657,"id":658},"qqhA5Y8rP0XN94LDN7MeA","2025-12-17T08:38:12.304Z",{"sys":19855},{"id":664,"type":656,"linkType":665},{"title":18716,"description":18716,"file":19857},{"url":19858,"details":19859,"fileName":19863,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FqqhA5Y8rP0XN94LDN7MeA\u002F5f56d596378a69b3341bfeea28ae4fbe\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",{"size":19860,"image":19861},204520,{"width":1143,"height":19862},972,"simplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",[],{"data":19866,"content":19867,"nodeType":1920},{},[19868],{"data":19869,"marks":19870,"value":19871,"nodeType":1906},{},[],"Checkpoints are already saving to \u002Fmodel. 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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. ",{"data":20396,"content":20397,"nodeType":1920},{},[20398,20402],{"data":20399,"marks":20400,"value":20401,"nodeType":1906},{},[],"The future isn’t one hyper-intelligent agent; it’s a swarm of millions. Why? ",{"data":20403,"marks":20404,"value":20406,"nodeType":1906},{},[20405],{"type":12205},"Specialization.",{"data":20408,"content":20409,"nodeType":1920},{},[20410,20414,20419],{"data":20411,"marks":20412,"value":20413,"nodeType":1906},{},[],"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 ",{"data":20415,"marks":20416,"value":20418,"nodeType":1906},{},[20417],{"type":12269},"team of specialized agents",{"data":20420,"marks":20421,"value":20422,"nodeType":1906},{},[]," —a PlannerAgent, a ResearchAgent, a DataAnalystAgent, a SecurityMonitorAgent, and a NotificationAgent — all working in concert.",{"data":20424,"content":20425,"nodeType":1920},{},[20426],{"data":20427,"marks":20428,"value":20429,"nodeType":1906},{},[],"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.",{"data":20431,"content":20432,"nodeType":1920},{},[20433,20437,20446,20450],{"data":20434,"marks":20435,"value":20436,"nodeType":1906},{},[],"However, this also creates an unprecedented infrastructure problem. A swarm of millions of autonomous agents, all reasoning, communicating, and accessing data simultaneously, ",{"data":20438,"content":20440,"nodeType":1915},{"uri":20439},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbuilding-enterprise-infrastructure-for-ai-agents",[20441],{"data":20442,"marks":20443,"value":20445,"nodeType":1906},{},[20444],{"type":12205},"cannot run on today’s siloed architecture",{"data":20447,"marks":20448,"value":1919,"nodeType":1906},{},[20449],{"type":12205},{"data":20451,"marks":20452,"value":20453,"nodeType":1906},{},[]," It’s an infrastructure nightmare, creating problems of cost, security, and complexity.",{"data":20455,"content":20456,"nodeType":12207},{},[20457],{"data":20458,"marks":20459,"value":20460,"nodeType":1906},{},[],"The Bottleneck: The Environment vs. The Swarm",{"data":20462,"content":20463,"nodeType":1920},{},[20464],{"data":20465,"marks":20466,"value":20467,"nodeType":1906},{},[],"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.",{"data":20469,"content":20470,"nodeType":1920},{},[20471],{"data":20472,"marks":20473,"value":20474,"nodeType":1906},{},[],"This creates critical bottlenecks that make swarm deployment challenging:",{"data":20476,"content":20477,"nodeType":12184},{},[20478,20493,20508,20523],{"data":20479,"content":20480,"nodeType":12173},{},[20481],{"data":20482,"content":20483,"nodeType":1920},{},[20484,20489],{"data":20485,"marks":20486,"value":20488,"nodeType":1906},{},[20487],{"type":12205},"The coupled scaling problem:",{"data":20490,"marks":20491,"value":20492,"nodeType":1906},{},[]," 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.",{"data":20494,"content":20495,"nodeType":12173},{},[20496],{"data":20497,"content":20498,"nodeType":1920},{},[20499,20504],{"data":20500,"marks":20501,"value":20503,"nodeType":1906},{},[20502],{"type":12205},"The state and amnesia problem:",{"data":20505,"marks":20506,"value":20507,"nodeType":1906},{},[]," 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.",{"data":20509,"content":20510,"nodeType":12173},{},[20511],{"data":20512,"content":20513,"nodeType":1920},{},[20514,20519],{"data":20515,"marks":20516,"value":20518,"nodeType":1906},{},[20517],{"type":12205},"The communication bottleneck:",{"data":20520,"marks":20521,"value":20522,"nodeType":1906},{},[]," 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.",{"data":20524,"content":20525,"nodeType":12173},{},[20526],{"data":20527,"content":20528,"nodeType":1920},{},[20529,20534,20538,20543],{"data":20530,"marks":20531,"value":20533,"nodeType":1906},{},[20532],{"type":12205},"The security and identity silo:",{"data":20535,"marks":20536,"value":20537,"nodeType":1906},{},[]," In a monolithic setup, each agent is an island. It has to manage its ",{"data":20539,"marks":20540,"value":20542,"nodeType":1906},{},[20541],{"type":12269},"own",{"data":20544,"marks":20545,"value":20546,"nodeType":1906},{},[]," 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.",{"data":20548,"content":20549,"nodeType":1920},{},[20550],{"data":20551,"marks":20552,"value":20553,"nodeType":1906},{},[],"These aren’t agent failures; they’re platform gaps - the absence of infrastructure primitives purpose-built for multi-agent systems.",{"data":20555,"content":20556,"nodeType":12207},{},[20557],{"data":20558,"marks":20559,"value":20560,"nodeType":1906},{},[],"A Shared, Decoupled Foundation for Running AI Agents",{"data":20562,"content":20563,"nodeType":1920},{},[20564],{"data":20565,"marks":20566,"value":20567,"nodeType":1906},{},[],"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.",{"data":20569,"content":20570,"nodeType":1920},{},[20571,20575,20580],{"data":20572,"marks":20573,"value":20574,"nodeType":1906},{},[],"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 ",{"data":20576,"marks":20577,"value":20579,"nodeType":1906},{},[20578],{"type":12269},"separate functions",{"data":20581,"marks":20582,"value":20583,"nodeType":1906},{},[]," 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.",{"data":20585,"content":20586,"nodeType":1920},{},[20587,20591,20596],{"data":20588,"marks":20589,"value":20590,"nodeType":1906},{},[],"The primary advantage of this disaggregated architecture is the ability to scale ",{"data":20592,"marks":20593,"value":20595,"nodeType":1906},{},[20594],{"type":12269},"only",{"data":20597,"marks":20598,"value":20599,"nodeType":1906},{},[]," the component that is the bottleneck.",{"data":20601,"content":20602,"nodeType":12184},{},[20603,20618,20641,20656],{"data":20604,"content":20605,"nodeType":12173},{},[20606],{"data":20607,"content":20608,"nodeType":1920},{},[20609,20614],{"data":20610,"marks":20611,"value":20613,"nodeType":1906},{},[20612],{"type":12205},"Scaling CPU (kernels)",{"data":20615,"marks":20616,"value":20617,"nodeType":1906},{},[],": If 10,000 tasks arrive, the platform spins up 10,000 cheap, stateless Agent Kernels (on VAST C-Nodes).",{"data":20619,"content":20620,"nodeType":12173},{},[20621],{"data":20622,"content":20623,"nodeType":1920},{},[20624,20629,20633,20637],{"data":20625,"marks":20626,"value":20628,"nodeType":1906},{},[20627],{"type":12205},"Scaling GPU (reasoning)",{"data":20630,"marks":20631,"value":20632,"nodeType":1906},{},[],": If those agents create 100,000 reasoning requests, ",{"data":20634,"marks":20635,"value":20595,"nodeType":1906},{},[20636],{"type":12269},{"data":20638,"marks":20639,"value":20640,"nodeType":1906},{},[]," the GPU servers are scaled, backed by the VAST KVCache to maximize GPU utility.",{"data":20642,"content":20643,"nodeType":12173},{},[20644],{"data":20645,"content":20646,"nodeType":1920},{},[20647,20652],{"data":20648,"marks":20649,"value":20651,"nodeType":1906},{},[20650],{"type":12205},"Scaling storage (memory)",{"data":20653,"marks":20654,"value":20655,"nodeType":1906},{},[],": 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.",{"data":20657,"content":20658,"nodeType":12173},{},[20659],{"data":20660,"content":20661,"nodeType":1920},{},[20662,20667],{"data":20663,"marks":20664,"value":20666,"nodeType":1906},{},[20665],{"type":12205},"Scaling I\u002FO (comms)",{"data":20668,"marks":20669,"value":20670,"nodeType":1906},{},[],": 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.",{"data":20672,"content":20690,"nodeType":12909},{"target":20673},{"metadata":20674,"sys":20677,"fields":20684},{"tags":20675,"concepts":20676},[],[],{"space":20678,"id":20680,"type":933,"createdAt":20681,"updatedAt":20681,"environment":20682,"publishedVersion":846,"revision":1135,"locale":672},{"sys":20679},{"type":656,"linkType":657,"id":658},"1O2NCk6JSuSs1UXnguRpiS","2025-11-19T14:14:44.267Z",{"sys":20683},{"id":664,"type":656,"linkType":665},{"title":20337,"description":20337,"file":20685},{"url":20686,"details":20687,"fileName":20373,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1O2NCk6JSuSs1UXnguRpiS\u002Fa2a722a7966a4271d9fc7ff17f7fa313\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"size":20688,"image":20689},159002,{"width":1143,"height":1144},[],{"data":20692,"content":20693,"nodeType":12207},{},[20694],{"data":20695,"marks":20696,"value":20697,"nodeType":1906},{},[],"Architectural and Engineering Deep Dive: Pillars of a Scalable Agentic Platform",{"data":20699,"content":20700,"nodeType":1920},{},[20701],{"data":20702,"marks":20703,"value":20704,"nodeType":1906},{},[],"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.",{"data":20706,"content":20707,"nodeType":12305},{},[20708],{"data":20709,"marks":20710,"value":20711,"nodeType":1906},{},[],"1. The Execution Model: Stateless Compute with Shared State Access",{"data":20713,"content":20714,"nodeType":1920},{},[20715],{"data":20716,"marks":20717,"value":20718,"nodeType":1906},{},[],"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.",{"data":20720,"content":20721,"nodeType":1920},{},[20722],{"data":20723,"marks":20724,"value":20726,"nodeType":1906},{},[20725],{"type":12205},"Agent Kernels as Ephemeral Functions",{"data":20728,"content":20729,"nodeType":1920},{},[20730],{"data":20731,"marks":20732,"value":20733,"nodeType":1906},{},[],"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:",{"data":20735,"content":20736,"nodeType":12184},{},[20737,20747,20757,20767],{"data":20738,"content":20739,"nodeType":12173},{},[20740],{"data":20741,"content":20742,"nodeType":1920},{},[20743],{"data":20744,"marks":20745,"value":20746,"nodeType":1906},{},[],"Execute in response to events (e.g., file writes, object creation, table mutations)",{"data":20748,"content":20749,"nodeType":12173},{},[20750],{"data":20751,"content":20752,"nodeType":1920},{},[20753],{"data":20754,"marks":20755,"value":20756,"nodeType":1906},{},[],"Have direct NVMe access to the underlying storage fabric",{"data":20758,"content":20759,"nodeType":12173},{},[20760],{"data":20761,"content":20762,"nodeType":1920},{},[20763],{"data":20764,"marks":20765,"value":20766,"nodeType":1906},{},[],"Scale horizontally without coordination, so spawning 10,000 agent kernels doesn’t require inter-kernel communication",{"data":20768,"content":20769,"nodeType":12173},{},[20770],{"data":20771,"content":20772,"nodeType":1920},{},[20773],{"data":20774,"marks":20775,"value":20776,"nodeType":1906},{},[],"Die after execution; state persistence is the storage layer’s responsibility, not the kernel’s",{"data":20778,"content":20779,"nodeType":1920},{},[20780],{"data":20781,"marks":20782,"value":20783,"nodeType":1906},{},[],"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.",{"data":20785,"content":20786,"nodeType":1920},{},[20787],{"data":20788,"marks":20789,"value":20791,"nodeType":1906},{},[20790],{"type":12205},"The Data Locality Advantage",{"data":20793,"content":20794,"nodeType":1920},{},[20795],{"data":20796,"marks":20797,"value":20798,"nodeType":1906},{},[],"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).",{"data":20800,"content":20801,"nodeType":1920},{},[20802],{"data":20803,"marks":20804,"value":20806,"nodeType":1906},{},[20805],{"type":12205},"Execution Triggers and Lifecycle",{"data":20808,"content":20809,"nodeType":1920},{},[20810],{"data":20811,"marks":20812,"value":20813,"nodeType":1906},{},[],"Agent kernel triggers can be:",{"data":20815,"content":20816,"nodeType":13998},{},[20817,20832,20847],{"data":20818,"content":20819,"nodeType":12173},{},[20820],{"data":20821,"content":20822,"nodeType":1920},{},[20823,20828],{"data":20824,"marks":20825,"value":20827,"nodeType":1906},{},[20826],{"type":12205},"Event-driven",{"data":20829,"marks":20830,"value":20831,"nodeType":1906},{},[],": File write, object creation, table insert → kernel spawns automatically",{"data":20833,"content":20834,"nodeType":12173},{},[20835],{"data":20836,"content":20837,"nodeType":1920},{},[20838,20843],{"data":20839,"marks":20840,"value":20842,"nodeType":1906},{},[20841],{"type":12205},"API-driven",{"data":20844,"marks":20845,"value":20846,"nodeType":1906},{},[],": External system calls an endpoint → kernel executes on-demand",{"data":20848,"content":20849,"nodeType":12173},{},[20850],{"data":20851,"content":20852,"nodeType":1920},{},[20853,20858],{"data":20854,"marks":20855,"value":20857,"nodeType":1906},{},[20856],{"type":12205},"Scheduled",{"data":20859,"marks":20860,"value":20861,"nodeType":1906},{},[],": Cron-like triggers for periodic agent tasks",{"data":20863,"content":20864,"nodeType":1920},{},[20865],{"data":20866,"marks":20867,"value":20869,"nodeType":1906},{},[20868],{"type":12205},"When is it Important to Write Agents Output Storage:",{"data":20871,"content":20872,"nodeType":1920},{},[20873],{"data":20874,"marks":20875,"value":20876,"nodeType":1906},{},[],"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.",{"data":20878,"content":20879,"nodeType":1920},{},[20880],{"data":20881,"marks":20882,"value":20883,"nodeType":1906},{},[],"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",{"data":20885,"content":20886,"nodeType":1920},{},[20887],{"data":20888,"marks":20889,"value":20890,"nodeType":1906},{},[],"Write everything to persistent storage when:",{"data":20892,"content":20893,"nodeType":12184},{},[20894,20904,20914,20924],{"data":20895,"content":20896,"nodeType":12173},{},[20897],{"data":20898,"content":20899,"nodeType":1920},{},[20900],{"data":20901,"marks":20902,"value":20903,"nodeType":1906},{},[],"Regulatory or compliance requirements mandate traceability (finance, healthcare)",{"data":20905,"content":20906,"nodeType":12173},{},[20907],{"data":20908,"content":20909,"nodeType":1920},{},[20910],{"data":20911,"marks":20912,"value":20913,"nodeType":1906},{},[],"System reliability is paramount",{"data":20915,"content":20916,"nodeType":12173},{},[20917],{"data":20918,"content":20919,"nodeType":1920},{},[20920],{"data":20921,"marks":20922,"value":20923,"nodeType":1906},{},[],"Agents work on long-horizon tasks",{"data":20925,"content":20926,"nodeType":12173},{},[20927],{"data":20928,"content":20929,"nodeType":1920},{},[20930],{"data":20931,"marks":20932,"value":20933,"nodeType":1906},{},[],"Agent interactions should be inspectable, replayable, or learning-enabled for future deployments.",{"data":20935,"content":20936,"nodeType":12305},{},[20937],{"data":20938,"marks":20939,"value":20940,"nodeType":1906},{},[],"2. Event-Driven Coordination: Communication Through the Environment",{"data":20942,"content":20943,"nodeType":1920},{},[20944],{"data":20945,"marks":20946,"value":20947,"nodeType":1906},{},[],"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.",{"data":20949,"content":20950,"nodeType":1920},{},[20951],{"data":20952,"marks":20953,"value":20954,"nodeType":1906},{},[],"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.",{"data":20956,"content":20957,"nodeType":1920},{},[20958],{"data":20959,"marks":20960,"value":20962,"nodeType":1906},{},[20961],{"type":12205},"Environment-Driven Agent Triggers",{"data":20964,"content":20965,"nodeType":1920},{},[20966],{"data":20967,"marks":20968,"value":20969,"nodeType":1906},{},[],"Agents register themselves to be triggered by specific environmental changes:",{"data":20971,"content":20972,"nodeType":12184},{},[20973,20988,21003,21018],{"data":20974,"content":20975,"nodeType":12173},{},[20976],{"data":20977,"content":20978,"nodeType":1920},{},[20979,20984],{"data":20980,"marks":20981,"value":20983,"nodeType":1906},{},[20982],{"type":12205},"File system events",{"data":20985,"marks":20986,"value":20987,"nodeType":1906},{},[],": An agent triggers when a file matching \u002Fvast\u002Fdatasets\u002Fsales_*.parquet is created",{"data":20989,"content":20990,"nodeType":12173},{},[20991],{"data":20992,"content":20993,"nodeType":1920},{},[20994,20999],{"data":20995,"marks":20996,"value":20998,"nodeType":1906},{},[20997],{"type":12205},"Database mutations",{"data":21000,"marks":21001,"value":21002,"nodeType":1906},{},[],": An agent triggers when a new row is inserted into the pending_tasks table",{"data":21004,"content":21005,"nodeType":12173},{},[21006],{"data":21007,"content":21008,"nodeType":1920},{},[21009,21014],{"data":21010,"marks":21011,"value":21013,"nodeType":1906},{},[21012],{"type":12205},"Vector updates",{"data":21015,"marks":21016,"value":21017,"nodeType":1906},{},[],": An agent triggers when new embeddings are added to the knowledge base",{"data":21019,"content":21020,"nodeType":12173},{},[21021],{"data":21022,"content":21023,"nodeType":1920},{},[21024,21029],{"data":21025,"marks":21026,"value":21028,"nodeType":1906},{},[21027],{"type":12205},"Time-based events",{"data":21030,"marks":21031,"value":21032,"nodeType":1906},{},[],": An agent triggers on a schedule to perform periodic analysis",{"data":21034,"content":21035,"nodeType":1920},{},[21036],{"data":21037,"marks":21038,"value":21039,"nodeType":1906},{},[],"Here’s an example workflow:",{"data":21041,"content":21042,"nodeType":13998},{},[21043,21053,21063,21073,21083,21093],{"data":21044,"content":21045,"nodeType":12173},{},[21046],{"data":21047,"content":21048,"nodeType":1920},{},[21049],{"data":21050,"marks":21051,"value":21052,"nodeType":1906},{},[],"DataCollectorAgent writes a new file: \u002Fvast\u002Fdatasets\u002Fsales_2025_q3.parquet",{"data":21054,"content":21055,"nodeType":12173},{},[21056],{"data":21057,"content":21058,"nodeType":1920},{},[21059],{"data":21060,"marks":21061,"value":21062,"nodeType":1906},{},[],"The file write generates a file.created event on the Event Broker",{"data":21064,"content":21065,"nodeType":12173},{},[21066],{"data":21067,"content":21068,"nodeType":1920},{},[21069],{"data":21070,"marks":21071,"value":21072,"nodeType":1906},{},[],"DataValidatorAgent, registered to trigger on file.created events in \u002Fvast\u002Fdatasets\u002F, automatically spawns",{"data":21074,"content":21075,"nodeType":12173},{},[21076],{"data":21077,"content":21078,"nodeType":1920},{},[21079],{"data":21080,"marks":21081,"value":21082,"nodeType":1906},{},[],"DataValidatorAgent reads the file, validates it, and updates a database table with status = “validated”",{"data":21084,"content":21085,"nodeType":12173},{},[21086],{"data":21087,"content":21088,"nodeType":1920},{},[21089],{"data":21090,"marks":21091,"value":21092,"nodeType":1906},{},[],"The table update generates a table.updated event",{"data":21094,"content":21095,"nodeType":12173},{},[21096],{"data":21097,"content":21098,"nodeType":1920},{},[21099],{"data":21100,"marks":21101,"value":21102,"nodeType":1906},{},[],"AnalysisPipelineAgent, registered to trigger on validation status changes, automatically spawns",{"data":21104,"content":21105,"nodeType":1920},{},[21106],{"data":21107,"marks":21108,"value":21109,"nodeType":1906},{},[],"No agent explicitly called another agent. They communicated through changes to the shared environment, with the Event Broker mediating the triggers.",{"data":21111,"content":21112,"nodeType":1920},{},[21113],{"data":21114,"marks":21115,"value":21117,"nodeType":1906},{},[21116],{"type":12205},"Why This Model Scales",{"data":21119,"content":21120,"nodeType":1920},{},[21121],{"data":21122,"marks":21123,"value":21124,"nodeType":1906},{},[],"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.",{"data":21126,"content":21127,"nodeType":1920},{},[21128],{"data":21129,"marks":21130,"value":21131,"nodeType":1906},{},[],"Environment-driven communication decouples agents completely:",{"data":21133,"content":21134,"nodeType":12184},{},[21135,21145,21155],{"data":21136,"content":21137,"nodeType":12173},{},[21138],{"data":21139,"content":21140,"nodeType":1920},{},[21141],{"data":21142,"marks":21143,"value":21144,"nodeType":1906},{},[],"Agents don’t need to know which other agents exist",{"data":21146,"content":21147,"nodeType":12173},{},[21148],{"data":21149,"content":21150,"nodeType":1920},{},[21151],{"data":21152,"marks":21153,"value":21154,"nodeType":1906},{},[],"New agents can be added without modifying existing agents",{"data":21156,"content":21157,"nodeType":12173},{},[21158],{"data":21159,"content":21160,"nodeType":1920},{},[21161],{"data":21162,"marks":21163,"value":21164,"nodeType":1906},{},[],"Failed agents don’t cause cascading failures—their work simply remains in the environment until they recover",{"data":21166,"content":21167,"nodeType":1920},{},[21168,21171,21179],{"data":21169,"marks":21170,"value":2030,"nodeType":1906},{},[],{"data":21172,"content":21174,"nodeType":1915},{"uri":21173},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fannouncing-the-vast-event-broker",[21175],{"data":21176,"marks":21177,"value":21178,"nodeType":1906},{},[],"VAST’s Event Broker",{"data":21180,"marks":21181,"value":21182,"nodeType":1906},{},[]," 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:",{"data":21184,"content":21185,"nodeType":12184},{},[21186,21201,21216],{"data":21187,"content":21188,"nodeType":12173},{},[21189],{"data":21190,"content":21191,"nodeType":1920},{},[21192,21197],{"data":21193,"marks":21194,"value":21196,"nodeType":1906},{},[21195],{"type":12205},"No broker bottleneck",{"data":21198,"marks":21199,"value":21200,"nodeType":1906},{},[],": Event capacity scales with storage capacity, not broker node count",{"data":21202,"content":21203,"nodeType":12173},{},[21204],{"data":21205,"content":21206,"nodeType":1920},{},[21207,21212],{"data":21208,"marks":21209,"value":21211,"nodeType":1906},{},[21210],{"type":12205},"Unlimited retention",{"data":21213,"marks":21214,"value":21215,"nodeType":1906},{},[],": Events can persist indefinitely because storage is cheap and abundant",{"data":21217,"content":21218,"nodeType":12173},{},[21219],{"data":21220,"content":21221,"nodeType":1920},{},[21222,21227],{"data":21223,"marks":21224,"value":21226,"nodeType":1906},{},[21225],{"type":12205},"Parallel consumption",{"data":21228,"marks":21229,"value":21230,"nodeType":1906},{},[],": Thousands of agents can consume events concurrently without overwhelming broker nodes—they’re reading directly from distributed storage",{"data":21232,"content":21233,"nodeType":1920},{},[21234],{"data":21235,"marks":21236,"value":21237,"nodeType":1906},{},[],"Because events are part of the storage fabric:",{"data":21239,"content":21240,"nodeType":12184},{},[21241,21256,21271],{"data":21242,"content":21243,"nodeType":12173},{},[21244],{"data":21245,"content":21246,"nodeType":1920},{},[21247,21252],{"data":21248,"marks":21249,"value":21251,"nodeType":1906},{},[21250],{"type":12205},"Sub-millisecond publish latency",{"data":21253,"marks":21254,"value":21255,"nodeType":1906},{},[],": Writing an event is a storage write, leveraging NVMe and RDMA",{"data":21257,"content":21258,"nodeType":12173},{},[21259],{"data":21260,"content":21261,"nodeType":1920},{},[21262,21267],{"data":21263,"marks":21264,"value":21266,"nodeType":1906},{},[21265],{"type":12205},"Massive parallel throughput",{"data":21268,"marks":21269,"value":21270,"nodeType":1906},{},[],": The storage fabric can handle millions of events per second across the cluster",{"data":21272,"content":21273,"nodeType":12173},{},[21274],{"data":21275,"content":21276,"nodeType":1920},{},[21277,21282],{"data":21278,"marks":21279,"value":21281,"nodeType":1906},{},[21280],{"type":12205},"No data movement for triggers",{"data":21283,"marks":21284,"value":21285,"nodeType":1906},{},[],": When an event triggers a DataEngine function, the function executes on the same C-Node where the event is stored—zero data movement",{"data":21287,"content":21288,"nodeType":12305},{},[21289],{"data":21290,"marks":21291,"value":21292,"nodeType":1906},{},[],"3. State Management: Transactional Tables, Vectors, and Consistency",{"data":21294,"content":21295,"nodeType":1920},{},[21296],{"data":21297,"marks":21298,"value":21299,"nodeType":1906},{},[],"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.",{"data":21301,"content":21302,"nodeType":1920},{},[21303],{"data":21304,"marks":21305,"value":21306,"nodeType":1906},{},[],"VAST DataBase unifies three capabilities in a single storage layer:",{"data":21308,"content":21309,"nodeType":13998},{},[21310,21325,21340],{"data":21311,"content":21312,"nodeType":12173},{},[21313],{"data":21314,"content":21315,"nodeType":1920},{},[21316,21321],{"data":21317,"marks":21318,"value":21320,"nodeType":1906},{},[21319],{"type":12205},"OLTP: ",{"data":21322,"marks":21323,"value":21324,"nodeType":1906},{},[],"Transactional state (agent metadata, task status, workflow state)",{"data":21326,"content":21327,"nodeType":12173},{},[21328],{"data":21329,"content":21330,"nodeType":1920},{},[21331,21336],{"data":21332,"marks":21333,"value":21335,"nodeType":1906},{},[21334],{"type":12205},"OLAP:",{"data":21337,"marks":21338,"value":21339,"nodeType":1906},{},[]," Analytical queries (aggregate agent performance, system metrics)",{"data":21341,"content":21342,"nodeType":12173},{},[21343],{"data":21344,"content":21345,"nodeType":1920},{},[21346,21351],{"data":21347,"marks":21348,"value":21350,"nodeType":1906},{},[21349],{"type":12205},"Vector storage:",{"data":21352,"marks":21353,"value":21354,"nodeType":1906},{},[]," Embeddings for RAG, semantic search, and memory retrieval",{"data":21356,"content":21357,"nodeType":1920},{},[21358],{"data":21359,"marks":21360,"value":21361,"nodeType":1906},{},[],"The DataBase is built on VAST’s disaggregated storage architecture. Data is stored in columnar format on D-Nodes, with multiple layers of indexing:",{"data":21363,"content":21364,"nodeType":12184},{},[21365,21380,21395],{"data":21366,"content":21367,"nodeType":12173},{},[21368],{"data":21369,"content":21370,"nodeType":1920},{},[21371,21376],{"data":21372,"marks":21373,"value":21375,"nodeType":1906},{},[21374],{"type":12205},"Row-based index: ",{"data":21377,"marks":21378,"value":21379,"nodeType":1906},{},[],"For transactional lookups (e.g., “fetch state for agent_id=123”)",{"data":21381,"content":21382,"nodeType":12173},{},[21383],{"data":21384,"content":21385,"nodeType":1920},{},[21386,21391],{"data":21387,"marks":21388,"value":21390,"nodeType":1906},{},[21389],{"type":12205},"Column-based index:",{"data":21392,"marks":21393,"value":21394,"nodeType":1906},{},[]," For analytical scans (e.g., “count all agents with status=failed”)",{"data":21396,"content":21397,"nodeType":12173},{},[21398],{"data":21399,"content":21400,"nodeType":1920},{},[21401,21406],{"data":21402,"marks":21403,"value":21405,"nodeType":1906},{},[21404],{"type":12205},"Vector index:",{"data":21407,"marks":21408,"value":21409,"nodeType":1906},{},[]," approximate nearest neighbor search for embeddings",{"data":21411,"content":21412,"nodeType":1920},{},[21413],{"data":21414,"marks":21415,"value":21417,"nodeType":1906},{},[21416],{"type":12205},"Vector Operations at Scale",{"data":21419,"content":21420,"nodeType":1920},{},[21421],{"data":21422,"marks":21423,"value":21424,"nodeType":1906},{},[],"The vector storage subsystem is designed for trillion-scale vector search. Key characteristics include:",{"data":21426,"content":21427,"nodeType":12184},{},[21428,21443,21458],{"data":21429,"content":21430,"nodeType":12173},{},[21431],{"data":21432,"content":21433,"nodeType":1920},{},[21434,21439],{"data":21435,"marks":21436,"value":21438,"nodeType":1906},{},[21437],{"type":12205},"Parallel updates",{"data":21440,"marks":21441,"value":21442,"nodeType":1906},{},[],": Unlike traditional vector DBs that require full re-indexing, VAST’s parallel architecture allows concurrent inserts without locking the entire index",{"data":21444,"content":21445,"nodeType":12173},{},[21446],{"data":21447,"content":21448,"nodeType":1920},{},[21449,21454],{"data":21450,"marks":21451,"value":21453,"nodeType":1906},{},[21452],{"type":12205},"Freshness of data: ",{"data":21455,"marks":21456,"value":21457,"nodeType":1906},{},[],"All of the data is searchable, including data that was not already converted to the vectorDB",{"data":21459,"content":21460,"nodeType":12173},{},[21461],{"data":21462,"content":21463,"nodeType":1920},{},[21464,21469],{"data":21465,"marks":21466,"value":21468,"nodeType":1906},{},[21467],{"type":12205},"Trillion-vector scale",{"data":21470,"marks":21471,"value":21472,"nodeType":1906},{},[],": No sharding and no performance degradation at scale",{"data":21474,"content":21475,"nodeType":12305},{},[21476],{"data":21477,"marks":21478,"value":21479,"nodeType":1906},{},[],"4. The Memory Hierarchy: KV Cache Offloading for GPU Efficiency and Endless Context",{"data":21481,"content":21482,"nodeType":1920},{},[21483],{"data":21484,"marks":21485,"value":21486,"nodeType":1906},{},[],"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.",{"data":21488,"content":21489,"nodeType":1920},{},[21490,21493,21500],{"data":21491,"marks":21492,"value":18156,"nodeType":1906},{},[],{"data":21494,"content":21495,"nodeType":1915},{"uri":12217},[21496],{"data":21497,"marks":21498,"value":21499,"nodeType":1906},{},[],"KV cache is the key to unlocking high GPU utilization,",{"data":21501,"marks":21502,"value":21503,"nodeType":1906},{},[]," reducing latency of response, and the ability to support endless context conversations that multi-agent systems require.",{"data":21505,"content":21506,"nodeType":1920},{},[21507],{"data":21508,"marks":21509,"value":21511,"nodeType":1906},{},[21510],{"type":12205},"The GPU Utilization Problem",{"data":21513,"content":21514,"nodeType":1920},{},[21515],{"data":21516,"marks":21517,"value":21518,"nodeType":1906},{},[],"A GPU’s compute capacity is wasted if its memory (HBM) fills up before its processors are fully utilized. ",{"data":21520,"content":21521,"nodeType":1920},{},[21522],{"data":21523,"marks":21524,"value":21525,"nodeType":1906},{},[],"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.",{"data":21527,"content":21528,"nodeType":1920},{},[21529],{"data":21530,"marks":21531,"value":21532,"nodeType":1906},{},[],"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.",{"data":21534,"content":21535,"nodeType":1920},{},[21536],{"data":21537,"marks":21538,"value":21540,"nodeType":1906},{},[21539],{"type":12205},"The Solution: KV Cache Offloading",{"data":21542,"content":21543,"nodeType":1920},{},[21544],{"data":21545,"marks":21546,"value":21547,"nodeType":1906},{},[],"By offloading the KV cache from GPU HBM to VAST’s high-speed storage fabric, you break the memory bottleneck:",{"data":21549,"content":21550,"nodeType":12184},{},[21551,21561,21571],{"data":21552,"content":21553,"nodeType":12173},{},[21554],{"data":21555,"content":21556,"nodeType":1920},{},[21557],{"data":21558,"marks":21559,"value":21560,"nodeType":1906},{},[],"Hot cache blocks (actively being used for generation) stay in GPU HBM",{"data":21562,"content":21563,"nodeType":12173},{},[21564],{"data":21565,"content":21566,"nodeType":1920},{},[21567],{"data":21568,"marks":21569,"value":21570,"nodeType":1906},{},[],"Cold cache blocks (common prefixes, older context, inactive requests) are moved to VAST storage",{"data":21572,"content":21573,"nodeType":12173},{},[21574],{"data":21575,"content":21576,"nodeType":1920},{},[21577],{"data":21578,"marks":21579,"value":21580,"nodeType":1906},{},[],"When needed, cold blocks are fetched back to HBM in under 1ms",{"data":21582,"content":21583,"nodeType":1920},{},[21584],{"data":21585,"marks":21586,"value":21587,"nodeType":1906},{},[],"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.",{"data":21589,"content":21590,"nodeType":1920},{},[21591],{"data":21592,"marks":21593,"value":21595,"nodeType":1906},{},[21594],{"type":12205},"Enabling Endless Context for Multi-Agent Systems",{"data":21597,"content":21598,"nodeType":1920},{},[21599],{"data":21600,"marks":21601,"value":21602,"nodeType":1906},{},[],"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.",{"data":21604,"content":21605,"nodeType":1920},{},[21606],{"data":21607,"marks":21608,"value":21609,"nodeType":1906},{},[],"With KV cache offloading:",{"data":21611,"content":21612,"nodeType":12184},{},[21613,21623,21633],{"data":21614,"content":21615,"nodeType":12173},{},[21616],{"data":21617,"content":21618,"nodeType":1920},{},[21619],{"data":21620,"marks":21621,"value":21622,"nodeType":1906},{},[],"Agents can maintain effectively unlimited context length—bounded only by storage, not GPU memory",{"data":21624,"content":21625,"nodeType":12173},{},[21626],{"data":21627,"content":21628,"nodeType":1920},{},[21629],{"data":21630,"marks":21631,"value":21632,"nodeType":1906},{},[],"Long-running agents don’t need to truncate or summarize their context to fit memory constraints",{"data":21634,"content":21635,"nodeType":12173},{},[21636],{"data":21637,"content":21638,"nodeType":1920},{},[21639],{"data":21640,"marks":21641,"value":21642,"nodeType":1906},{},[],"Multi-turn reasoning tasks can operate over massive context windows without performance degradation",{"data":21644,"content":21645,"nodeType":1920},{},[21646],{"data":21647,"marks":21648,"value":21650,"nodeType":1906},{},[21649],{"type":12205},"The Technical Mechanism",{"data":21652,"content":21653,"nodeType":1920},{},[21654],{"data":21655,"marks":21656,"value":21657,"nodeType":1906},{},[],"VAST achieves KV cache offloading by integrating with inference frameworks (e.g., NVIDIA Dynamo, LMCache) at the KV cache layer:",{"data":21659,"content":21660,"nodeType":12184},{},[21661,21671,21681,21691,21701],{"data":21662,"content":21663,"nodeType":12173},{},[21664],{"data":21665,"content":21666,"nodeType":1920},{},[21667],{"data":21668,"marks":21669,"value":21670,"nodeType":1906},{},[],"The inference engine generates KV cache blocks during forward passes",{"data":21672,"content":21673,"nodeType":12173},{},[21674],{"data":21675,"content":21676,"nodeType":1920},{},[21677],{"data":21678,"marks":21679,"value":21680,"nodeType":1906},{},[],"The cache manager (modified to use VAST as backing storage) keeps frequently-accessed blocks in GPU HBM",{"data":21682,"content":21683,"nodeType":12173},{},[21684],{"data":21685,"content":21686,"nodeType":1920},{},[21687],{"data":21688,"marks":21689,"value":21690,"nodeType":1906},{},[],"When HBM fills up, cold blocks are evicted to VAST storage via high-speed NVMe and RDMA",{"data":21692,"content":21693,"nodeType":12173},{},[21694],{"data":21695,"content":21696,"nodeType":1920},{},[21697],{"data":21698,"marks":21699,"value":21700,"nodeType":1906},{},[],"On a cache miss, the block is fetched from VAST storage with sub-millisecond latency",{"data":21702,"content":21703,"nodeType":12173},{},[21704],{"data":21705,"content":21706,"nodeType":1920},{},[21707],{"data":21708,"marks":21709,"value":21710,"nodeType":1906},{},[],"Prefetch hints and access patterns allow proactive loading of likely-needed blocks",{"data":21712,"content":21713,"nodeType":1920},{},[21714],{"data":21715,"marks":21716,"value":21717,"nodeType":1906},{},[],"Technically, there is a trade-off when offloading KV cache to external storage: generation latency increases by 1-2ms per cache miss. However, this small latency increase enables:",{"data":21719,"content":21720,"nodeType":12184},{},[21721,21736,21751],{"data":21722,"content":21723,"nodeType":12173},{},[21724],{"data":21725,"content":21726,"nodeType":1920},{},[21727,21732],{"data":21728,"marks":21729,"value":21731,"nodeType":1906},{},[21730],{"type":12205},"3-5x higher throughput",{"data":21733,"marks":21734,"value":21735,"nodeType":1906},{},[],": Serve more agents per GPU by breaking the memory bottleneck",{"data":21737,"content":21738,"nodeType":12173},{},[21739],{"data":21740,"content":21741,"nodeType":1920},{},[21742,21747],{"data":21743,"marks":21744,"value":21746,"nodeType":1906},{},[21745],{"type":12205},"3-4x lower GPU costs",{"data":21748,"marks":21749,"value":21750,"nodeType":1906},{},[],": Need fewer GPUs to handle the same agent workload",{"data":21752,"content":21753,"nodeType":12173},{},[21754],{"data":21755,"content":21756,"nodeType":1920},{},[21757,21762],{"data":21758,"marks":21759,"value":21761,"nodeType":1906},{},[21760],{"type":12205},"Unlimited context",{"data":21763,"marks":21764,"value":21765,"nodeType":1906},{},[],": Support long-running agents with massive context windows",{"data":21767,"content":21768,"nodeType":1920},{},[21769],{"data":21770,"marks":21771,"value":21772,"nodeType":1906},{},[],"For multi-agent systems, where agents are performing reasoning tasks rather than real-time chat, this trade-off is overwhelmingly favorable. As costs add up fast and GPU efficiency becomes a bottleneck, KV cache offloading is a crucial component in multi-agent deployments.",{"data":21774,"content":21775,"nodeType":12305},{},[21776],{"data":21777,"marks":21778,"value":21779,"nodeType":1906},{},[],"5. Security and Isolation: Identity, Permissions, and Audit",{"data":21781,"content":21782,"nodeType":1920},{},[21783],{"data":21784,"marks":21785,"value":21786,"nodeType":1906},{},[],"When thousands of agents are executing autonomously, security cannot be an afterthought. The traditional approach—giving each service a long-lived API key or shared credentials—breaks down catastrophically at scale. A compromised key becomes a skeleton key to your entire system. Credential rotation becomes an operational nightmare. And when something goes wrong, tracing which agent did what becomes nearly impossible.",{"data":21788,"content":21789,"nodeType":1920},{},[21790],{"data":21791,"marks":21792,"value":21793,"nodeType":1906},{},[],"The alternative is to treat agents as first-class principals in your security model, with the same rigor you’d apply to human users or external services. Each agent must have:",{"data":21795,"content":21796,"nodeType":13998},{},[21797,21812,21827],{"data":21798,"content":21799,"nodeType":12173},{},[21800],{"data":21801,"content":21802,"nodeType":1920},{},[21803,21808],{"data":21804,"marks":21805,"value":21807,"nodeType":1906},{},[21806],{"type":12205},"Identity",{"data":21809,"marks":21810,"value":21811,"nodeType":1906},{},[],": Who is this agent?",{"data":21813,"content":21814,"nodeType":12173},{},[21815],{"data":21816,"content":21817,"nodeType":1920},{},[21818,21823],{"data":21819,"marks":21820,"value":21822,"nodeType":1906},{},[21821],{"type":12205},"Authorization",{"data":21824,"marks":21825,"value":21826,"nodeType":1906},{},[],": What can this agent access?",{"data":21828,"content":21829,"nodeType":12173},{},[21830],{"data":21831,"content":21832,"nodeType":1920},{},[21833,21838],{"data":21834,"marks":21835,"value":21837,"nodeType":1906},{},[21836],{"type":12205},"Auditability",{"data":21839,"marks":21840,"value":21841,"nodeType":1906},{},[],": What did this agent do?",{"data":21843,"content":21844,"nodeType":1920},{},[21845],{"data":21846,"marks":21847,"value":21849,"nodeType":1906},{},[21848],{"type":12205},"Identity Fabric: Short-Lived Credentials",{"data":21851,"content":21852,"nodeType":1920},{},[21853],{"data":21854,"marks":21855,"value":21856,"nodeType":1906},{},[],"Agents don’t have static API keys. Instead, when an agent kernel spawns, it requests a short-lived identity token from the platform’s identity service. This token:",{"data":21858,"content":21859,"nodeType":12184},{},[21860,21870,21880],{"data":21861,"content":21862,"nodeType":12173},{},[21863],{"data":21864,"content":21865,"nodeType":1920},{},[21866],{"data":21867,"marks":21868,"value":21869,"nodeType":1906},{},[],"Has a TTL (e.g., 15 minutes)",{"data":21871,"content":21872,"nodeType":12173},{},[21873],{"data":21874,"content":21875,"nodeType":1920},{},[21876],{"data":21877,"marks":21878,"value":21879,"nodeType":1906},{},[],"Carries scoped permissions (e.g., read access to \u002Fvast\u002Fdatasets\u002Fsales\u002F*)",{"data":21881,"content":21882,"nodeType":12173},{},[21883],{"data":21884,"content":21885,"nodeType":1920},{},[21886],{"data":21887,"marks":21888,"value":21889,"nodeType":1906},{},[],"Is tied to a specific agent instance (not reusable across spawns",{"data":21891,"content":21892,"nodeType":1920},{},[21893],{"data":21894,"marks":21895,"value":21896,"nodeType":1906},{},[],"When the agent makes a request (read from DataBase, publish to Event Broker), the token is validated. 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It’s easy to mock the infamous quote often attributed to Bill Gates — that 640KB of memory ought to be enough for anybody — when $2,000 will get you a new iPhone with nearly 8,000 times as much RAM (12GB) and 2TB of SSD storage. That is, after all, about 100,000 times what passed as a large internal hard drive when those began shipping with PCs in the mid-1980s.",{"data":34851,"content":34852,"nodeType":1920},{},[34853],{"data":34854,"marks":34855,"value":34856,"nodeType":1906},{},[],"In fact, easy access to storage and memory, paired with ubiquitous high-speed internet, has arguably led to operational laziness. Popular data systems like Apache Kafka might require storing triplicates of data in order to assure high availability. And even when it’s easily achievable, application developers might make minimal effort to optimize memory efficiency. Just buy more storage. Just switch to higher-memory cloud instances. ",{"data":34858,"content":34859,"nodeType":1920},{},[34860,34864,34869,34873,34878],{"data":34861,"marks":34862,"value":34863,"nodeType":1906},{},[],"But those days are coming to an end. AI inference workloads are creating an entirely new data type that’s set to skyrocket in terms of volume: the ",{"data":34865,"marks":34866,"value":34868,"nodeType":1906},{},[34867],{"type":12269},"context memory,",{"data":34870,"marks":34871,"value":34872,"nodeType":1906},{},[]," or the working memory",{"data":34874,"marks":34875,"value":34877,"nodeType":1906},{},[34876],{"type":12269},", ",{"data":34879,"marks":34880,"value":34881,"nodeType":1906},{},[],"of our interactions with AI models. ",{"data":34883,"content":34884,"nodeType":1920},{},[34885,34889,34897,34901,34909,34913,34921,34925,34933],{"data":34886,"marks":34887,"value":34888,"nodeType":1906},{},[],"It’s a big enough deal that, at CES last week, NVIDIA CEO Jensen Huang ",{"data":34890,"content":34892,"nodeType":1915},{"uri":34891},"https:\u002F\u002Fnvidianews.nvidia.com\u002Fnews\u002Frubin-platform-ai-supercomputer",[34893],{"data":34894,"marks":34895,"value":34896,"nodeType":1906},{},[],"unveiled a forthcoming AI supercomputer",{"data":34898,"marks":34899,"value":34900,"nodeType":1906},{},[]," that can use ",{"data":34902,"content":34904,"nodeType":1915},{"uri":34903},"https:\u002F\u002Fblocksandfiles.com\u002F2026\u002F01\u002F12\u002Fnvidias-basic-context-memory-extension-infrastructure\u002F",[34905],{"data":34906,"marks":34907,"value":34908,"nodeType":1906},{},[],"up to 18 petabytes of solid-state-drive capacity",{"data":34910,"marks":34911,"value":34912,"nodeType":1906},{},[]," — or up to 16TB per each of its 1,152 GPUs — in a ",{"data":34914,"content":34916,"nodeType":1915},{"uri":34915},"https:\u002F\u002Finvestor.nvidia.com\u002Fnews\u002Fpress-release-details\u002F2026\u002FNVIDIA-BlueField-4-Powers-New-Class-of-AI-Native-Storage-Infrastructure-for-the-Next-Frontier-of-AI\u002Fdefault.aspx",[34917],{"data":34918,"marks":34919,"value":34920,"nodeType":1906},{},[],"new tier of context memory storage",{"data":34922,"marks":34923,"value":34924,"nodeType":1906},{},[]," designed to help manage all the inference context we’ll be generating. For AI factories scaling into the tens or hundreds of thousands of GPUs, that number could balloon into exabytes of capacity for inference alone. And this in the face of ",{"data":34926,"content":34928,"nodeType":1915},{"uri":34927},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbeating-the-flash-crunch-with-efficiency-not-supply",[34929],{"data":34930,"marks":34931,"value":34932,"nodeType":1906},{},[],"an industry-wide shortage",{"data":34934,"marks":34935,"value":34936,"nodeType":1906},{},[]," of both high-speed memory and SSD capacity (the building blocks of context-memory storage) because manufacturers can’t keep up with demand.",{"data":34938,"content":34939,"nodeType":12207},{},[34940],{"data":34941,"marks":34942,"value":34943,"nodeType":1906},{},[],"Context memory lives in the KVcache",{"data":34945,"content":34946,"nodeType":1920},{},[34947,34951,34959],{"data":34948,"marks":34949,"value":34950,"nodeType":1906},{},[],"For LLMs and other transformer-based AI models, context memory lives in the key-value cache, or ",{"data":34952,"content":34954,"nodeType":1915},{"uri":34953},"https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fnot-lain\u002Fkv-caching",[34955],{"data":34956,"marks":34957,"value":34958,"nodeType":1906},{},[],"KVcache",{"data":34960,"marks":34961,"value":34962,"nodeType":1906},{},[],". Here’s a high-level explanation of how it works:",{"data":34964,"content":34965,"nodeType":12184},{},[34966,34985,35004],{"data":34967,"content":34968,"nodeType":12173},{},[34969],{"data":34970,"content":34971,"nodeType":1920},{},[34972,34976,34981],{"data":34973,"marks":34974,"value":34975,"nodeType":1906},{},[],"When a human or an agent calls a model, the model must first compute the initial context (such as the current prompt, document upload, retrieved content, or conversation history) before it can generate the first token (in the case of an LLM, a token usually equates to about one word). This is called the ",{"data":34977,"marks":34978,"value":34980,"nodeType":1906},{},[34979],{"type":12269},"prefill",{"data":34982,"marks":34983,"value":34984,"nodeType":1906},{},[]," stage; it’s compute-intensive and it’s why you might notice a lag before the model starts generating a response. ",{"data":34986,"content":34987,"nodeType":12173},{},[34988],{"data":34989,"content":34990,"nodeType":1920},{},[34991,34995,35000],{"data":34992,"marks":34993,"value":34994,"nodeType":1906},{},[],"For each subsequent token generated as part of that response, during the",{"data":34996,"marks":34997,"value":34999,"nodeType":1906},{},[34998],{"type":12269}," decode",{"data":35001,"marks":35002,"value":35003,"nodeType":1906},{},[]," stage, the model generates the next token based on the keys and values of all previous tokens, and then appends to the KVcache accordingly. This step is memory bandwidth-intensive, consisting of continuous, but relatively small, interactions between GPU compute and memory — where all those keys and values reside.",{"data":35005,"content":35006,"nodeType":12173},{},[35007],{"data":35008,"content":35009,"nodeType":1920},{},[35010,35015,35019,35024],{"data":35011,"marks":35012,"value":35014,"nodeType":1906},{},[35013],{"type":12269},"Without KVcache",{"data":35016,"marks":35017,"value":35018,"nodeType":1906},{},[],", LLMs would have to execute the prefill stage for each token. Because the context grows with every pass, each additional token would take ",{"data":35020,"marks":35021,"value":35023,"nodeType":1906},{},[35022],{"type":12269},"more",{"data":35025,"marks":35026,"value":35027,"nodeType":1906},{},[]," time to compute, consume more resources, and generate a larger cache. ",{"data":35029,"content":35030,"nodeType":1920},{},[35031,35035,35040],{"data":35032,"marks":35033,"value":35034,"nodeType":1906},{},[],"When you hear about the ",{"data":35036,"marks":35037,"value":35039,"nodeType":1906},{},[35038],{"type":12269},"context windows",{"data":35041,"marks":35042,"value":35043,"nodeType":1906},{},[]," of LLMs, this is what they’re talking about: How many tokens a model can technically handle. But even then, model performance can degrade as user sessions stretch beyond the context window, or merely beyond the available memory and\u002For GPU capacity. At this point, earlier context might be flushed to make room for new content, leading to a model’s “forgetfulness.”",{"data":35045,"content":35046,"nodeType":1920},{},[35047],{"data":35048,"marks":35049,"value":35050,"nodeType":1906},{},[],"Today, a large producer of context memory (and, therefore, consumer of KVcache capacity) might be a workload like code generation, where someone could upload an entire codebase into a context window as part of their prompt. Video models can also generate a large amount of context memory, as each frame is composed of multiple (and sometimes hundreds of) tokens.",{"data":35052,"content":35053,"nodeType":1920},{},[35054,35058,35066],{"data":35055,"marks":35056,"value":35057,"nodeType":1906},{},[],"Looking to the near future, agentic workflows will also be large producers of context memory. This is ",{"data":35059,"content":35061,"nodeType":1915},{"uri":35060},"https:\u002F\u002Fmanus.im\u002Fblog\u002FContext-Engineering-for-AI-Agents-Lessons-from-Building-Manus",[35062],{"data":35063,"marks":35064,"value":35065,"nodeType":1906},{},[],"especially true of multi-agent systems",{"data":35067,"marks":35068,"value":35069,"nodeType":1906},{},[],", where the orchestration agent has to maintain context across any number of other agents. Looking a little further out, multimodal models for applications like robotics could also generate huge amounts of context memory. ",{"data":35071,"content":35072,"nodeType":12207},{},[35073],{"data":35074,"marks":35075,"value":35076,"nodeType":1906},{},[],"From 640 kilobytes of computer memory to exabytes of context memory",{"data":35078,"content":35079,"nodeType":1920},{},[35080,35084,35089,35093,35098,35102,35109],{"data":35081,"marks":35082,"value":35083,"nodeType":1906},{},[],"Let’s get back to the earlier point about how greatly memory and storage capacity have increased over the past 40ish years. While it’s ",{"data":35085,"marks":35086,"value":35088,"nodeType":1906},{},[35087],{"type":12269},"fun",{"data":35090,"marks":35091,"value":35092,"nodeType":1906},{},[]," to marvel at the specs of a modern smartphone, here’s a statistic that ",{"data":35094,"marks":35095,"value":35097,"nodeType":1906},{},[35096],{"type":12269},"really",{"data":35099,"marks":35100,"value":35101,"nodeType":1906},{},[]," matters today: The key-value cache (KVcache or KV$) of a large language model with a 128,000-token context window (like Llama 3), will ",{"data":35103,"content":35104,"nodeType":1915},{"uri":13947},[35105],{"data":35106,"marks":35107,"value":35108,"nodeType":1906},{},[],"generate about 61GB of data",{"data":35110,"marks":35111,"value":35112,"nodeType":1906},{},[]," per conversation. ",{"data":35114,"content":35115,"nodeType":1920},{},[35116,35120,35127],{"data":35117,"marks":35118,"value":35119,"nodeType":1906},{},[],"Now, 61GB might not sound like a lot, but it adds up. ",{"data":35121,"content":35122,"nodeType":1915},{"uri":13947},[35123],{"data":35124,"marks":35125,"value":35126,"nodeType":1906},{},[],"Here’s a hypothetical situation",{"data":35128,"marks":35129,"value":35130,"nodeType":1906},{},[]," our engineering team recently laid out, using the the still relatively standard 128,000-token context window as a starting point:",{"data":35132,"content":35133,"nodeType":12305},{},[35134],{"data":35135,"marks":35136,"value":17174,"nodeType":1906},{},[35137],{"type":12269},{"data":35139,"content":35140,"nodeType":1920},{},[35141],{"data":35142,"marks":35143,"value":17181,"nodeType":1906},{},[35144],{"type":12269},{"data":35146,"content":35147,"nodeType":1920},{},[35148],{"data":35149,"marks":35150,"value":35152,"nodeType":1906},{},[35151],{"type":12269},"Total Capacity = Number of Users × Average KV$ per User × Retention Multiplier",{"data":35154,"content":35155,"nodeType":1920},{},[35156],{"data":35157,"marks":35158,"value":35160,"nodeType":1906},{},[35159],{"type":12269},"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":35162,"content":35163,"nodeType":12184},{},[35164,35175,35186],{"data":35165,"content":35166,"nodeType":12173},{},[35167],{"data":35168,"content":35169,"nodeType":1920},{},[35170],{"data":35171,"marks":35172,"value":35174,"nodeType":1906},{},[35173],{"type":12269},"Average KV$ per Prompt: 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 61GB in size. Let’s assume a more typical average for a prompt is 64K tokens, roughly 30GB",{"data":35176,"content":35177,"nodeType":12173},{},[35178],{"data":35179,"content":35180,"nodeType":1920},{},[35181],{"data":35182,"marks":35183,"value":35185,"nodeType":1906},{},[35184],{"type":12269},"Retention Multiplier: We’ll use 15 to provide a good user experience",{"data":35187,"content":35188,"nodeType":12173},{},[35189],{"data":35190,"content":35191,"nodeType":1920},{},[35192],{"data":35193,"marks":35194,"value":35196,"nodeType":1906},{},[35195],{"type":12269},"Calculation: 100,000 users × 30GB × 15 = 45,000,000GB = 45PB",{"data":35198,"content":35199,"nodeType":1920},{},[35200],{"data":35201,"marks":35202,"value":35203,"nodeType":1906},{},[],"Both architecturally and cost-wise, maintaining petabytes or exabytes of KVcache solely in GPU memory or even system CPU memory is out of the question.",{"data":35205,"content":35206,"nodeType":1920},{},[35207],{"data":35208,"marks":35209,"value":35210,"nodeType":1906},{},[],"And while persistently storing that much context memory — or more — might seem a little crazy today (beyond for a handful of large model providers), it might seem much more reasonable tomorrow. Nobody wants their AI model to forget the last 5 questions they asked, and everyone wants to be able to resume their last conversation. Even if use cases somehow remained static, user growth, larger models, longer context windows, and a desire to store more context for longer periods of time will also cause context-memory storage to balloon in the years to come.",{"data":35212,"content":35213,"nodeType":12207},{},[35214],{"data":35215,"marks":35216,"value":35217,"nodeType":1906},{},[],"Storing all the world’s context on SSDs",{"data":35219,"content":35220,"nodeType":1920},{},[35221,35225,35233],{"data":35222,"marks":35223,"value":35224,"nodeType":1906},{},[],"The incredible amount of context memory about to be generated and stored is why Jensen Huang stood in front of that large supercomputer at CES and, with the world watching, ",{"data":35226,"content":35228,"nodeType":1915},{"uri":35227},"https:\u002F\u002Fyoutu.be\u002F0NBILspM4c4?t=4688",[35229],{"data":35230,"marks":35231,"value":35232,"nodeType":1906},{},[],"spoke at length about KVcache",{"data":35234,"marks":35235,"value":35236,"nodeType":1906},{},[]," and the lengths NVIDIA went to make sure its next-generation systems can manage it at production scale.",{"data":35238,"content":35239,"nodeType":1920},{},[35240,35244,35252],{"data":35241,"marks":35242,"value":35243,"nodeType":1906},{},[],"SSDs, obviously, cost much less than GPU memory in terms of terabytes — or petabytes — per dollar. And although pulling a KVcache from external storage isn’t as fast as GPU memory for current or “hot” sessions, properly architected storage systems – such as the newly announced context memory storage architecture – can still feed context to GPUs ",{"data":35245,"content":35247,"nodeType":1915},{"uri":35246},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fmore-inference-less-infrastructure-vast-nvidia",[35248],{"data":35249,"marks":35250,"value":35251,"nodeType":1906},{},[],"at near real-time speed",{"data":35253,"marks":35254,"value":35255,"nodeType":1906},{},[],". Because it mitigates cold starts, in fact, this setup will likely improve performance for workloads that require accessing past conversations or context.",{"data":35257,"content":35258,"nodeType":1920},{},[35259],{"data":35260,"marks":35261,"value":35262,"nodeType":1906},{},[],"Looking forward, tiered KVcache storage points to a future where we can do even more by blowing past some of AI’s architectural hurdles:",{"data":35264,"content":35265,"nodeType":12184},{},[35266,35288,35298],{"data":35267,"content":35268,"nodeType":12173},{},[35269],{"data":35270,"content":35271,"nodeType":1920},{},[35272,35276,35284],{"data":35273,"marks":35274,"value":35275,"nodeType":1906},{},[],"Multi-agent workflows and robotics models ",{"data":35277,"content":35279,"nodeType":1915},{"uri":35278},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system",[35280],{"data":35281,"marks":35282,"value":35283,"nodeType":1906},{},[],"get high-speed access to stateful context",{"data":35285,"marks":35286,"value":35287,"nodeType":1906},{},[],", allowing them to perform complex, multi-turn tasks, and learn from historical interactions, without constant prefill computation.",{"data":35289,"content":35290,"nodeType":12173},{},[35291],{"data":35292,"content":35293,"nodeType":1920},{},[35294],{"data":35295,"marks":35296,"value":35297,"nodeType":1906},{},[],"State-space models could offset their lack of attention by calling on externally stored context, thus opening their superior efficiency up to new classes of applications.",{"data":35299,"content":35300,"nodeType":12173},{},[35301],{"data":35302,"content":35303,"nodeType":1920},{},[35304],{"data":35305,"marks":35306,"value":35307,"nodeType":1906},{},[],"Smaller models could use historical context to overcome their inherently worse accuracy, which is the natural tradeoff of being trained with fewer parameters.",{"data":35309,"content":35310,"nodeType":1920},{},[35311],{"data":35312,"marks":35313,"value":35314,"nodeType":1906},{},[],"The more context we can offload to SSDs (or other flash) with low-latency GPU connections, the more creative we can get around compute utilization, model and application architecture, and data center design.",{"data":35316,"content":35317,"nodeType":12207},{},[35318],{"data":35319,"marks":35320,"value":35321,"nodeType":1906},{},[],"Supply Chain, Meet Demand … Meet Opportunity",{"data":35323,"content":35324,"nodeType":1920},{},[35325],{"data":35326,"marks":35327,"value":35328,"nodeType":1906},{},[],"Of course, all of this AI investment does come at a cost: insatiable demand for DRAM and NAND flash capacity. For the first time in a long time, “just buy more storage” doesn’t look like a viable strategy. Costs are up. Some manufacturers are scrapping consumer product lines to focus on data center production. Hyperscale buyers are snatching up capacity as soon as it’s available.",{"data":35330,"content":35331,"nodeType":1920},{},[35332,35336,35344],{"data":35333,"marks":35334,"value":35335,"nodeType":1906},{},[],"The opportunity comes from figuring out ",{"data":35337,"content":35339,"nodeType":1915},{"uri":35338},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fwhite-papers\u002Fscaling-through-flash-scarcity-with-purpose-built-architecture",[35340],{"data":35341,"marks":35342,"value":35343,"nodeType":1906},{},[],"how to squeeze more capacity out of existing gear",{"data":35345,"marks":35346,"value":35347,"nodeType":1906},{},[],". Step up data reduction. Share storage across systems, applications, and users. Find better, modern alternatives to legacy systems that waste capacity. Clever engineering today will pay dividends down the road, even after DRAM and NAND capacity are readily available again.",{"data":35349,"content":35350,"nodeType":1920},{},[35351],{"data":35352,"marks":35353,"value":35354,"nodeType":1906},{},[],"It’s also a good time to start thinking seriously about how to secure context memory. Consider the amount of sensitive personal data and intellectual property that might be contained within, to use our previous example, 45PB of AI interactions and model “thought processes” for 100,000 users. Anyone storing that much data unencrypted and\u002For weakly isolated, even ephemerally, is asking for leakage.",{"data":35356,"content":35357,"nodeType":1920},{},[35358,35362,35366],{"data":35359,"marks":35360,"value":35361,"nodeType":1906},{},[],"When all the proposed AI data centers ultimately fill up, they should be operating as optimally as possible. 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