{"resourceId":"nvidia-snapshot-gpu-checkpoint-restore-benchmarks-20261110","versions":[{"version":"external-34a70bbc17fc01b4903f1ff3efc4036a05eca7dcaac81313ded13402745afe28","resource":{"id":"nvidia-snapshot-gpu-checkpoint-restore-benchmarks-20261110","title":"Start and Restore GPU Workloads in Seconds with NVIDIA Snapshot","organization":"NVIDIA","sector":"AI inference infrastructure software","geography":"Global","publishedAt":"November 10, 2026","publicationDate":"2026-11-10","eventDate":"2026-11-10","sourceName":"NVIDIA Technical Blog","sourceLabel":"NVIDIA Technical Blog (vendor benchmarks)","sourceUrl":"https://developer.nvidia.com/blog/start-and-restore-gpu-workloads-in-seconds-with-nvidia-snapshot/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["infrastructure","developers-agents"],"finding":"NVIDIA Snapshot is an open source checkpoint and restore system for GPU workloads on Kubernetes. On NVIDIA B200 GPUs with VAST NFS storage, NVIDIA reports restores of 3.6 to 42.7 seconds, 8x to 36x faster than cold starts of about 87 seconds to 18.5 minutes across vLLM, SGLang and TensorRT LLM, including models with checkpoints up to about 1 TB over eight GPUs. Checkpoint creation took 15 seconds to 5.5 minutes, and checkpoint plus restore was shorter than one cold start in every tested configuration. Design partners report Crusoe cutting GLM-5.3 readiness from about eight minutes to 31 seconds and Saturn Cloud cutting vLLM startup from 2 to 8.5 minutes to 6 to 34 seconds, both on HGX B300.","sledRelevance":"Limited and indirect. Faster inference scale-up and recovery matters to operators of shared GPU services, such as university research computing or state-run AI platforms, but the post offers no SLED deployment evidence.","evidence":"Vendor post with detailed tables for one GPU type (B200), one storage backend (VAST NFS) and specified engine versions; excludes container image pull and provisioning from the measured path. Partner numbers are summarized without test conditions in the post.","architectureImplications":"Uses Kubernetes custom resources (PodSnapshot, SnapshotJob) and a restore annotation; restore requires nodes with the same GPU model and driver; workloads need a small pause and resume handshake; examples exist for vLLM, SGLang and TensorRT LLM. Storage speed and GPU state copy dominate large restores.","governanceImplications":"Checkpoint artifacts of hundreds of GB are new data assets that need retention, access control and versioning decisions.","securityPrivacyImplications":"Snapshots capture CPU and GPU memory; the post does not discuss whether artifacts could contain sensitive prompts, keys or user data. This needs separate review before use with regulated workloads.","caveats":"Vendor-run benchmarks on one GPU model and one storage backend; same-GPU-and-driver restore requirement; workloads must meet compatibility requirements; image pull and provisioning time excluded; not independently reproduced.","streamIds":["nvidia"],"roles":{"sales":"Lead with the operational problem, not the speed multiple: GPUs that are available but not yet serving, during demand spikes or after a failure. Qualify whether the customer runs shared inference services at all. Quote the numbers only as NVIDIA-reported results on B200 with VAST storage, and ask for their own model and storage details. Do not promise similar gains elsewhere. A fair next step is a time-boxed trial on one model. This is interpretation; the post provides no SLED customer evidence.","engineering":"Engineering should test Snapshot on the exact engine, model, GPU model, driver and storage you run, because the post shows speedups ranging from about 8x to 36x and notes that GPU state copy and storage dominate large restores. Confirm the same-GPU and same-driver restore requirement fits your fleet. Include image pull and provisioning in your own end-to-end timing, since they are excluded here. Review what memory contents are written to artifacts and how those are secured and retired.","delivery":"Delivery would run a measured pilot on one inference service: baseline cold start, checkpoint, restore, and failure recovery. Dependencies are Kubernetes access, compatible GPUs and drivers, fast shared storage and a Dynamo or engine integration. Assign ownership for artifact storage and lifecycle. Acceptance criteria should compare end-to-end ready time, not only restore time, and include a security review of snapshot contents. Operations staff need Kubernetes and inference-engine skills."},"roleDetails":{"sales":{"customerProblem":"Inference capacity is slow to come online after demand spikes or failures even when GPUs are free.","stakeholders":["Research computing or AI platform lead","Infrastructure architect","Security reviewer","Budget owner"],"discoveryQuestions":["How long does a replica take to become ready today?","Which engines, models and storage do you use?","How often do scale events or failures occur?"],"valueHypothesis":"Restoring initialized replicas may cut startup and recovery time and allow less idle capacity, if conditions resemble the tested setup.","potentialEngagement":"Time-boxed Snapshot pilot on one model with baseline and restore measurements.","unsupportedClaims":["That speedups apply to other GPUs or storage","That Snapshot lowers cost","Partner results under unstated test conditions"]},"engineering":{"fit":"Fits Kubernetes inference fleets using vLLM, SGLang or TensorRT LLM on identical GPU and driver combinations.","architectureIntegration":"PodSnapshot and SnapshotJob resources, nvidia.com/restore-from annotation, Dynamo integration, shared storage for artifacts.","prerequisites":["Same GPU model and driver on restore nodes","Fast shared storage","Compatible inference engine workload","Kubernetes cluster"],"constraints":["Tested on B200 with VAST NFS only in the tables","Checkpoint sizes up to about 1 TB","Image pull and provisioning excluded"],"security":"Artifacts contain CPU and GPU memory; assess for sensitive content, encrypt and control access.","proofOfValue":["Measure end-to-end ready time cold versus restore","Test restore on a different node with identical GPU and driver","Test failure recovery under load"]},"delivery":{"work":"Pilot checkpoint and restore on one inference service with measured baselines.","dependencies":["Compatible GPUs and drivers","Shared storage","Kubernetes admin access"],"ownership":"Platform team owns artifacts and restore runbook.","skillsAdoption":"Kubernetes and inference-engine operations skills required.","governanceCheckpoints":["Security review of snapshot contents","Artifact retention policy","Pilot results review"],"acceptanceCriteria":["Proposed: end-to-end ready time compared with baseline","Proposed: successful restore after simulated failure"],"risks":["Results not transferable","Artifact sensitivity","Driver or GPU drift"]}},"retrievedAt":"2026-11-10T21:59:05-06:00","enrichedAt":"2026-11-10T22:02:24-06:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Benefits accrue mainly to teams already operating inference platforms; it does not directly change accessibility, and adoption needs Kubernetes and inference-ops skills.","procurementImplications":"Open source reduces license friction, but value depends on GPU, driver and storage choices already purchased; ask vendors for reproducible benchmarks on your configuration rather than relying on the published B200 table.","operatingModelImplications":"Introduces snapshot artifacts as managed assets and shifts capacity planning from keeping warm replicas toward restoring on demand, which needs clear ownership and monitoring.","sourceVerification":{"openedUrl":"https://developer.nvidia.com/blog/start-and-restore-gpu-workloads-in-seconds-with-nvidia-snapshot/","referenceExcerpt":"Across all tested configurations, Snapshot brings a replica back in under 45 seconds, 8x to 36x faster than a cold start.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}