To benchmark pNFS for AI training, measure representative data-ingestion, checkpoint, and—when relevant—metadata-heavy workloads over time, at the client count and security settings you will actually use. Report throughput and latency by interval alongside any short-run peak. A synthetic streaming maximum shows what a system can deliver briefly under that test; it does not predict sustained training performance.
Why a peak-bandwidth test is not enough
Parallel NFS (pNFS) allows a client to use a server-provided layout to access file data on storage. In the flexible-file layout, metadata and data roles are separated. The layout type and implementation affect how data is accessed, so results from one configuration do not automatically describe another. The IETF’s RFC 5664 notes that bypassing the server for data access can increase performance and parallelism, while requiring client functionality that depends in part on the storage layout type.
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More parallel clients or jobs may raise bandwidth, but that alone does not show that a training pipeline can sustain the rate. Results depend on the test’s client count, concurrency, data path, cache state, and workload. A sequential peak can miss the variability and mixed I/O of actual training workflows. The authors of the 2026 PRISM preprint argue that peak-only storage benchmarks fail to capture the bursty, heterogeneous I/O patterns of AI research, and organize their evaluation around ingestion, checkpoint I/O, and developer workflows. Treat this as the authors’ framework, not a universal benchmark standard.
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Describe the system before presenting results. Include the pNFS layout type and NFS version; client and server software and versions; storage tier; network topology and link speeds; number of clients and data servers; and the security mode. Layouts determine how clients access data, so an unexplained difference in layout or implementation can make throughput comparisons misleading.
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Also record the mount options, dataset size, I/O pattern, block size, read/write mix, job and connection counts, and cache treatment. State the warm-up and measurement periods and the interval used for reporting. These details let readers distinguish a change in storage behavior from a change in workload or configuration.
Build a workload matrix around the training pipeline
Run separate jobs for distinct I/O phases rather than compressing them into one headline number. Choose parameters from the training pipeline you need to understand; there is no universal set of block sizes, read/write ratios, or runtimes that represents every AI workload.
Data ingestion
Measure the reads that feed training. Include sequential reads if data is consumed in large ordered runs, and randomized or mixed input if the loader actually accesses shards or records that way. Track throughput and latency over the run, not just the maximum rate.
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Checkpoint writes
Measure checkpoint creation as writes, including the flush or commit behavior relevant to the application and mount configuration. Record completion time and latency as well as write throughput: an apparently high transfer rate can obscure long waits before a checkpoint is safely completed.
Metadata-heavy and developer workflows
If the pipeline opens many files, discovers shards, or performs frequent metadata operations, include a workload that reflects those operations. PRISM includes developer workflows among its representative phases; the practical test should match the file-open and shard-handling behavior of your own environment rather than assume that a large sequential transfer covers it.
Sweep clients, jobs, and concurrency
Increase scale in measured steps and compare single-client scale-up with multi-client scale-out. Keep the workload and dataset consistent as you add clients, and record the number of jobs and connections so the point at which throughput stops scaling—or latency becomes unacceptable—is visible.
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Microsoft Learn’s Azure NetApp Files benchmark examples illustrate why configuration details matter: they distinguish jobs and TCP connections on one client from adding clients, and vary block sizes and read/write ratios. One scale-out example uses 32 clients, a 1-TiB dataset, and 4-KiB and 8-KiB random reads and writes at varying read/write ratios. Those are example configuration values, not universal recommendations, and the guide’s Azure configurations—including NFSv3 examples—are not a pNFS benchmark recipe.
Make cache state explicit
State whether client or server caching is part of the result. If you need to characterize cache-excluded behavior, describe how the cache was controlled; if you need warm-cache behavior, identify it as such. Make the dataset large enough for the cache policy you intend to test, and do not describe a cache-influenced run as storage-media throughput.
Microsoft’s benchmark documentation notes that one random-test configuration without randrepeat involved an indeterminate amount of caching and performed somewhat better than its cache-excluded counterpart. That example is a warning about interpretation: without a defined cache state, a result may reflect caching as well as storage behavior.
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Measure sustained throughput and latency over time
Use a warm-up period, then measure long enough to observe the behavior relevant to the intended training job. The cited sources do not establish a universal test duration; choose and justify one based on the workload and observed system behavior. A short burst may hide cache exhaustion, throttling, resource limits, or variability that appears later.
Publish throughput and latency for successive measurement intervals, as well as an aggregate. Include tail latency percentiles when available. Keep the peak as a separate statistic rather than treating it as the sustained result. If a rate falls or latency rises during the run, the interval series makes that change visible in a way a single maximum or overall average cannot.
Benchmark with production security settings
Use the authentication and encryption settings intended for production, or publish separate results for different security scenarios. Do not compare throughput from runs with different security modes as though the storage system alone changed.
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NetApp’s example reports 70% lower throughput for krb5p than krb5 for pNFS parallel reads on a RHEL 9.5 client. This is a result from that specific vendor configuration, not a general performance law or an expected penalty for every pNFS deployment. NetApp’s guidance is to test the actual workload.
Connect storage measurements to training outcomes
When possible, collect storage results during the training workload and record data-loader throughput, GPU input stalls or utilization, and checkpoint completion time. This connects filesystem behavior to what the training job experiences, rather than assuming that a storage MB/s figure translates directly into model speed. PRISM motivates workflow-level evaluation, but its cited findings do not establish a universal mapping from storage throughput to training speed.
Compare systems on more than their best number
When comparing pNFS implementations or storage services, use the same workload matrix and evaluate the dimensions that affect the training workflow:
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- Latency and variability during ingestion and checkpointing.
- Scaling efficiency from one client to many.
- Sensitivity to cache state and dataset size.
- Performance under the required security configuration.
- Interoperability and operational fit.
The available evidence supports comparing these dimensions, but it does not establish one universally fastest system. The useful result is a reproducible account of how each candidate behaves under the workload and operating conditions you care about.
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