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Benchmark the storage data path and the GPU communication path separately, then relate both results to the workload you actually run. Use a workload-level storage test such as MLPerf Storage to measure whether data can reach client memory fast enough, NCCL collective tests to measure multi-GPU communication, and RDMA link tests such as ib_write_bw and ib_write_lat to isolate fabric bandwidth and latency. A benchmark is not a universal rating: it describes one topology, software stack, workload, scale and run procedure.
Why AI benchmarking needs two separate test paths
AI systems can be limited by at least two different data movements. The storage path reads training samples, writes checkpoints, builds vector indexes or serves key-value cache data. The communication path moves tensors between GPUs, nodes and processes during distributed execution. A fast result on one path cannot prove that the other path is healthy.
Storage tests usually exercise a storage device or service, the client operating system, a filesystem or object interface, the network used by clients, and transfers into client memory. GPU communication tests exercise the GPUs, their PCIe or switching topology, the host CPUs, the network adapters, the inter-node fabric and the collective library. Treating a storage-throughput number as a proxy for all-reduce performance, or treating a single-link bandwidth number as proof that training will scale, leads to wrong capacity decisions.
The useful question is not “Which system has the biggest peak number?” It is “Can this configured path sustain the access pattern, concurrency and scale of my job?” Every result should therefore carry its workload, topology, software versions, message sizes, affinity settings and measurement method.
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Start by defining the question and the workload
Write down the decision the benchmark must support before installing tools. The procedure differs depending on whether you are sizing a new platform, comparing storage systems, validating a network fabric or diagnosing a slowdown already seen in production.
Choose the path you need to measure
- Training input: repeated reads of examples, often with multiple workers per accelerator.
- Checkpointing: large, coordinated writes whose burstiness and durability behavior matter.
- Vector database work: index construction and query access with different mixtures of reads, writes and metadata operations.
- KV-cache traffic: inference-oriented access patterns that can be sensitive to latency, concurrency and working-set locality.
- Distributed collectives: all-reduce, all-gather, reduce-scatter, all-to-all or broadcast between GPUs and nodes.
Identify the approximate number of clients, nodes, GPUs, data-loader workers, simultaneous operations and expected message or object sizes. If the production job uses a particular access layer, preserve it in the test: MLPerf Storage configurations can use POSIX or S3 interfaces, and those paths do not impose identical software overheads.
Capture a baseline before tuning
Record the system as it exists before changing settings. Include node and GPU models, CPU and memory configuration, storage servers or targets, network adapters, link rates, topology, firmware, operating-system and driver versions, collective-library version, storage client software, filesystem or object configuration, and CPU/GPU/memory affinity. Change one variable at a time between comparison runs; otherwise an apparent improvement cannot be assigned to a cause.
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MLPerf Storage is designed to measure storage behavior for AI-oriented workloads rather than a synthetic device peak. Its workload families include training, checkpointing, vector database and KV cache cases. Select the family that matches the path you need to validate and use the same workload and benchmark round when comparing systems.
Understand what the training test measures
In the MLPerf Storage training workload, simulated accelerators request data through a real framework data-loading path. Data is transferred from storage to client DRAM, while the accelerator computation itself is emulated. The result answers whether the configured storage path can supply a modeled job; it does not measure GPU arithmetic, model convergence or complete end-to-end training time.
This distinction matters when interpreting a high score. A system can feed the modeled input stream effectively while a real job remains limited by GPU kernels, synchronization, preprocessing, checkpoint coordination or another service. Conversely, a low storage result is evidence that the input path cannot sustain the modeled demand under the tested conditions, not proof that every model will run at the same utilization.
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Prevent a client-cache false positive
A dataset that fits comfortably in aggregate client memory can make a test appear to measure storage when it is mostly measuring cached reads. MLPerf Storage rules use dataset-sizing and minimum-work controls to reduce that confound, but the exact criteria depend on the workload and benchmark version. Check the current rules and the selected workload configuration before running.
- Use the required dataset size and work amount for the selected workload.
- Document client memory capacity and how the dataset relates to aggregate DRAM.
- Run enough work to exceed a short warm-up and expose sustained behavior.
- Keep cache state and any cache-clearing procedure consistent across compared systems.
- Record whether the test uses POSIX, S3 or another supported access layer.
Storage test procedure
- Define the scenario. Select training, checkpointing, vector database or KV cache, then specify the expected client and accelerator scale, access layer and concurrency.
- Freeze the configuration. Record storage targets, client nodes, network paths, filesystem or object settings, dataset size, software versions and affinity. Do not tune midway through a comparison.
- Prepare the dataset and work amount. Follow the selected MLPerf Storage rules, including requirements intended to prevent client-memory caching from replacing storage access.
- Warm up and measure. Separate startup effects from measured intervals. Keep run duration or amount of work identical for compared systems.
- Repeat the run. Preserve the same concurrency and environment, and report variability rather than only the best result.
- Save the submission details. Keep the workload name, benchmark version, configuration files, system description and reported metric with the raw logs.
Report the right storage metric
Report the workload-level metric defined by the benchmark, along with throughput, latency or accelerator-supply information when the workload exposes it. Do not collapse training, checkpointing, vector-database and KV-cache results into one storage score. They exercise different request sizes, synchronization points and access patterns.
MLPerf Storage result pages can apply workload-specific acceptance criteria. For examples from version 3.0, the 3D U-Net training case used a 90% accelerator-utilization threshold and RetinaNet used an 85% threshold; those are criteria for those benchmark cases, not universal targets for every model. The same examples report averages of five measured runs. Quote such figures only with the workload and version attached.
Measure GPU communication with NCCL collectives
For distributed training, use NCCL tests across the same node and GPU topology that the application will use. Collective operations expose behavior that a storage benchmark cannot: synchronization, message splitting, GPU-to-GPU paths, inter-node transfers and the interaction between the collective algorithm and the fabric.
Select operations that match the job
- All-reduce: a practical starting point for data-parallel gradient exchange.
- All-gather: useful when each rank must assemble data or model fragments from every other rank.
- Reduce-scatter: relevant when reduction and partitioning are combined.
- All-to-all: important for workloads that redistribute different data among all ranks.
- Broadcast: useful for one-to-many distribution patterns.
NCCL’s benchmark tooling tests performance and correctness. Run a message-size range rather than one convenient size, because small messages reveal latency and launch overhead while large messages reveal sustained bandwidth and congestion. Test both single-node and multi-node cases when the application spans nodes.
Keep topology and affinity identical
Capture which GPUs participate, their bus or switch topology, node count, process placement, CPU affinity, GPU affinity, memory affinity and transport settings. NVIDIA’s HPC benchmark wrapper exposes operation choice, test parameters and GPU, CPU, memory and UCX affinity controls. A result without those controls is difficult to reproduce and may hide an accidental placement difference.
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- Use the same number and ordering of GPUs that the target job uses.
- Run the selected collective across one node, then across the intended node count.
- Cover the message-size range expected by the application, including small and large transfers.
- Record warm-up, measured iterations, algorithm or protocol choices when exposed, and process affinity.
- Repeat runs and retain correctness output as well as timing and bandwidth results.
Do not interpret a collective bandwidth number as raw wire rate. Collective algorithms move data multiple times and may report effective bandwidth according to the operation. Compare the same operation, message-size range, rank count and reporting convention.
Isolate the fabric with RDMA diagnostics
When NCCL results are lower or less stable than expected, test the network independently. NVIDIA’s troubleshooting guidance documents ib_write_bw for inter-node bandwidth and ib_write_lat for point-to-point latency. Use a pair of representative nodes and the same link layer, rate and adapter path intended for the application.
Bandwidth test with ib_write_bw
Run a server and client between two nodes, select the intended RDMA device and port, and sweep message sizes relevant to the job. Record achieved bandwidth, duration, link rate and whether the test uses the expected path. A result near the nominal link rate does not prove that a multi-node collective will scale, but a severely reduced or inconsistent pairwise result is strong evidence of a fabric, configuration or hardware problem.
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Latency test with ib_write_lat
Use the latency test to expose small-message and synchronization costs. Record the message size, percentile or summary reported by the tool, and the same device, port and node placement used for the bandwidth test. Latency can be acceptable at one size while larger transfers are constrained by bandwidth, so retain both tests.
Check link health and RDMA counters
- Verify the selected link layer and negotiated rate on every participating port.
- Inspect physical link state, flaps, symbol errors, retransmissions and other RDMA error counters.
- Confirm that the expected interface, subnet and route are being used rather than a fallback path.
- Check that firmware, drivers and user-space RDMA components are compatible across nodes.
- Repeat the pairwise test with another node pair to distinguish a single bad path from a fabric-wide issue.
A healthy single-link test establishes only that one path can perform acceptably under those conditions. Keep the diagnostic result beside the NCCL result; neither replaces the other.
Use a controlled test matrix
A matrix prevents a comparison from silently changing several variables at once. The following dimensions should be explicit in every run.
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| Dimension | What to record | Why it matters |
|---|---|---|
| Workload shape | Training input, checkpointing, vector database, KV cache or collective operation | Different access patterns stress storage and communication differently. |
| System scope | Storage-to-client path, pairwise link or multi-node GPU collective | A result from one scope cannot stand in for another. |
| Scale | Clients, nodes, GPUs, ranks and simultaneous operations | Contention and topology effects change with scale. |
| Metric | Workload throughput, bandwidth, latency or accelerator-supply criterion | Peak bandwidth and sustained workload behavior answer different questions. |
| Message or request size | Full tested range and units | Small transfers emphasize latency; large transfers emphasize throughput. |
| Software and rules | Benchmark round, library, driver, firmware and configuration | Version changes can alter algorithms, acceptance criteria and comparability. |
| Affinity and transport | GPU, CPU, memory, NIC and UCX or RDMA placement | Placement can change the path and effective performance. |
For each compared system, hold every row constant except the component being evaluated. If scale or topology must differ, present the results as separate scenarios instead of a single ranking.
Interpret results without overclaiming
Storage result versus application performance
A storage benchmark demonstrates behavior for its defined workload and run. It does not predict every model, preprocessing pipeline, checkpoint schedule or inference service. Validate an important purchasing or capacity decision with a pilot that uses the target framework, dataset shape and concurrency after the benchmark has identified likely limits.
Collective result versus training speed
NCCL performance shows communication efficiency for selected operations and sizes. End-to-end training also depends on kernel execution, data loading, synchronization frequency, optimizer work, gradient bucketing and job scheduling. Use the collective result to locate communication headroom, not to claim a fixed percentage improvement in model throughput.
Published results and dates
Published entries can be updated or invalidated. When quoting one, include the retrieval date, benchmark round, workload, submission notes and complete system description. Never compare a current result with an older entry merely because both display a number in the same unit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot by symptom
Low storage throughput but healthy pairwise network tests
- Check client concurrency, data-loader workers and filesystem or object-service limits.
- Verify that the dataset is not being served from an unintended client cache.
- Compare the access layer and request sizes with the production job.
- Inspect storage-server queue depth, metadata operations and backend contention.
Good storage test but low GPU utilization in training
- Measure NCCL collectives at the same rank count and message sizes.
- Check GPU, CPU, memory and NIC affinity for accidental cross-socket traffic.
- Run
ib_write_bwandib_write_latbetween representative nodes. - Profile preprocessing, kernel execution and synchronization before blaming storage.
Good pairwise bandwidth but unstable NCCL results
- Compare single-node and multi-node runs to locate the transition where instability begins.
- Inspect RDMA counters, link flaps and negotiated rates on every participating port.
- Confirm that all ranks use the intended transport and topology.
- Repeat with fixed affinity and a controlled message-size sweep.
Results vary widely between repetitions
- Separate warm-up from measured iterations and lengthen the measured phase.
- Check for competing jobs, background storage traffic, thermal throttling or link errors.
- Report all repetitions and their spread instead of selecting the best run.
- Keep cache state, node allocation and software configuration identical.
Reporting template for a reproducible result
Attach the following information to every chart or table:
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- Workload family, framework, dataset size, access layer and concurrency.
- Benchmark name and version or round, including applicable rules.
- Node, GPU, CPU, memory, storage, adapter and topology details.
- Participating clients, nodes, GPUs and ranks.
- Collective operation or storage workload, request or message-size range and transport.
- GPU, CPU, memory, NIC and UCX or RDMA affinity settings.
- Warm-up procedure, measured iterations or duration, repetition count and variability.
- Link rate, selected ports, RDMA counters and physical-health observations for fabric tests.
- Raw logs, configuration files and the date on which the result was collected or retrieved.
This record turns a headline number into an auditable measurement. It also makes a later regression diagnosable instead of forcing a new test from scratch.
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What a sound benchmark conclusion looks like
State conclusions at the same scope as the measurement. For example: “With eight clients, the tested POSIX configuration sustained the selected training workload without violating its accelerator-supply criterion,” or “At the tested rank count and message-size range, the multi-node all-reduce result was limited by a degraded pairwise link.” Avoid statements such as “the storage is fast” or “the network is ready” unless the evidence covers the full workload and scale that those claims imply.
MLPerf Storage working-group co-chair Brian Belgodere described the suite’s expansion as covering a larger range of AI inference workloads that drive storage needs in a September 1, 2026 MLCommons announcement. That breadth is useful only when the selected workload matches your own access pattern. The practical standard remains the same: measure the storage path and communication path separately, preserve the configuration, compare like with like, and validate the final decision with the target application.
Frequently Asked Questions
Can a storage benchmark prove that distributed training will scale?
No. It measures the defined storage workload and data path. Distributed scaling also depends on GPU collectives, computation, synchronization and scheduling, so pair storage results with NCCL and application-level validation.
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Why run both NCCL tests and RDMA microbenchmarks?
NCCL measures application-relevant collective operations, while ib_write_bw and ib_write_lat isolate pairwise fabric bandwidth and latency. Together they help distinguish a collective or affinity issue from a link or physical-health problem.
Should I compare the highest published benchmark number?
Only when workload, benchmark round, scale, topology, software, operation, message sizes and rules match. Otherwise the numbers describe different configured systems and are not a fair ranking.
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