Benchmark storage with the I/O patterns your AI deployment will actually generate—not a single peak-bandwidth test. Training reads, checkpoint writes and restores, inference-time KV-cache activity, and vector search stress storage differently, so test each relevant workload and compare systems only under the same workload, software, access path, client topology, and configuration.
Choose tests that match the AI workload
Start by mapping the data paths in the deployment. Training commonly needs sustained reads; checkpointing adds large writes and later reads; inference may read model weights and features, access a KV cache, or query a vector index. Fine-tuning can add its own data and weight-update patterns. A result for one path cannot stand in for another.
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NVIDIA’s storage certification documentation lists different I/O profiles by use case, including sequential training reads, checkpoint writes, inference reads, random KV-cache I/O, and random vector lookups. NVIDIA-Certified Storage is a useful checklist when identifying the paths to include.
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MLPerf Storage v3.0 organizes its suite around training, checkpointing, vector database, and KV-cache tests. Its command reference describes training and checkpointing tests using DLIO, with separate test paths for the other families. Consult the mlpstorage command reference for the described families, but verify the release rules you plan to use: the reference warns that its version is not final and may change.
#1 Best Overall
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Pick metrics that expose the bottleneck
Training data supply
For training reads, report aggregate read bandwidth and how many simulated accelerators stay at or above the workload’s accelerator-utilization threshold. MLCommons defines these as key results: each simulated accelerator demands roughly a fixed data rate, so more bandwidth can support more accelerators above threshold. The MLPerf Storage results page gives the test-specific definitions and thresholds.
Checkpointing, metadata, and inference
For checkpointing, measure both save and restore/read behavior. If saves are synchronous, record how long the training job is blocked while they complete. For small-file or metadata-heavy tests, include operations per second, tail latency, and namespace behavior where the test supports them. For cache and retrieval workloads, measure the actual read/write or query behavior under the expected concurrency. These metrics complement suite results; not every MLPerf test reports all of them.
Rank #2
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Do not rank systems across unlike workloads. MLCommons explicitly cautions that results are comparable within a workload, not across workloads, because each stresses storage differently. Keep workload, accelerator type, client count, data size, access layer, and configuration constant for a comparison.
Keep the benchmark measuring storage, not client RAM
For the MLPerf Storage v3.0 training measurements described by MLCommons, the dataset must be at least five times the aggregate DRAM of the client nodes, and a run must process at least 500 batches per accelerator. These are controls for those suite tests, not universal rules for every custom benchmark; they are intended to reduce the chance that the test measures client memory rather than storage.
Rank #3
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The v3.0 training test uses simulated accelerators. Each reads real data through PyTorch at the intensity of a training job, then simulates compute by sleeping for the measured per-batch compute time. The arithmetic is skipped, but the storage-to-client data path remains real. This isolates storage data supply; it does not measure GPU arithmetic, model quality, or end-to-end training time.
Record dataset size relative to client memory, cache state, client count, and whether the path uses POSIX or S3. MLPerf v3.0 supports an S3 access layer alongside POSIX for training, checkpointing, and some vector database tests—not every workload. Check the rules for the exact test rather than assuming S3 support is universal.
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Make runs repeatable and comparisons fair
- Freeze the test definition. Record the benchmark release and workload, software and framework versions, access path, data-generation seed, accelerator configuration, clients, network topology, and storage configuration.
- Use stable conditions. Keep storage and the client/network setup stable across runs. Document cache state and any storage tuning that could affect results.
- Repeat and report results. MLPerf rules require multiple runs for statistical significance and say results that cannot be replicated are invalid. The rules prescribe replication within five percent across five tries. The v3.0 results page describes five consecutive measured training runs being averaged; that detail applies to the described results, so follow the rules for the release and workload you run.
- Disclose the submission class. MLPerf defines CLOSED and OPEN classes. CLOSED favors comparability and restricts most benchmark or framework changes while allowing storage tuning. OPEN allows documented changes but sacrifices direct comparability; it still cannot fundamentally change the workload. Label the class and disclose the code, configuration, client, network, and storage details needed for reproduction.
The MLPerf Storage Benchmark Suite rules state: “Results that cannot be replicated are not valid results.” Publishing run-to-run variation, rather than only a best result, helps readers judge whether a measured difference is dependable.
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Interpret MLPerf Storage v3.0 results in context
MLCommons announced v3.0 on September 1, 2026, adding KV-cache and vector database tests and S3 object access alongside POSIX for supported workloads. In the round, MLCommons reported that 19 organizations submitted results, including 11 first-time submitters. Those counts describe that round, not the broader storage market.
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For on-premises submissions in that round, MLCommons reported median checkpointing write efficiency of 14 GB/second per watt and a maximum of 201 GB/second per watt. For on-premises UNet3D read submissions, it reported a median of 34 GB/second per watt and a maximum of 277 GB/second per watt. These are submitted results for the v3.0 round, not performance guarantees for other systems or workloads. See the MLCommons v3.0 results announcement for their scope.
For a useful comparison, retain the workload family and version, test division, bandwidth or operations per second, utilization or completion behavior, latency and variability where measured, data size relative to client memory, client and network topology, access layer, checkpoint behavior, power efficiency if measured, and the completeness of system disclosure. Compare only results produced under the same workload and ruleset.
Pair storage results with an application-level test
Storage benchmarking answers how well the system supplies data under a defined test; it does not establish model time-to-result or serving latency. Because the MLPerf training test skips arithmetic, run a separate end-to-end training or serving benchmark if the decision depends on those outcomes. Use storage results to locate and compare the storage contribution, then validate the full application path.
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