Buy storage for the jobs and data you actually run—not for a headline bandwidth number. Evaluate sustained and burst read/write throughput per compute node and across the system, metadata operations, latency under concurrency, cache behavior, checkpoint time, interface compatibility, and how the system scales. Then validate the shortlist with a proof of concept using representative data and clients.
Start with the workload and the shape of its data
Storage requirements vary with the mix of training, inference, and data-pipeline work, as well as with how each job reads and writes its data. Record these details before comparing systems:
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- Dataset size, file-size distribution, file count, and directory structure.
- Sequential versus random access, read/write mix, and whether jobs reread the same data.
- Training and inference job mix, concurrent jobs, and the number of clients.
- Checkpoint size, frequency, and whether checkpoint writes overlap with training reads.
- Local cache capacity, expected reuse, and whether the first pass is likely to miss cache.
- Required protocol, software compatibility, and expected data growth.
The distinction between cache-friendly work and I/O-heavy work matters. NVIDIA’s H200 SuperPOD storage guidance discusses workloads such as large video or image training, offline inference, ETL, generative image work, medical imaging, and genomics where datasets can exceed local cache and first-epoch I/O is substantial. Its B200 guidance likewise distinguishes compute-dominated cases from larger-scale or multimodal training where data I/O matters more. These are examples for specific reference architectures, not universal workload tiers or purchasing thresholds.
Data format matters along with total volume: a system that serves a few large files well may behave differently when an application has to discover, open, and read many small files. NVIDIA’s B200 architecture notes that format can affect access rate as well as volume.
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Compare bandwidth at node and system level
Ask vendors for both per-node and aggregate read and write results. A strong aggregate figure can conceal insufficient service to individual compute nodes; a strong single-client result can disappear at the concurrency you intend to run. For each result, require the client count, topology, cache state, usable capacity, workload, and test method.
H200 reference-architecture targets
NVIDIA’s H200 SuperPOD reference architecture gives the following example targets. “Good,” “Better,” and “Best” are its categories for that design; they are not universal minimums or guarantees for another system.
| H200 reference target | Good | Better | Best |
|---|---|---|---|
| Read per node | 4 GB/s | 8 GB/s | 40 GB/s |
| Write per node | 2 GB/s | 4 GB/s | 20 GB/s |
| One SuperPOD SU: aggregate read | 15 GB/s | 40 GB/s | 125 GB/s |
| One SuperPOD SU: aggregate write | 7 GB/s | 20 GB/s | 62 GB/s |
| Four SuperPOD SUs: aggregate read | 60 GB/s | 160 GB/s | 500 GB/s |
| Four SuperPOD SUs: aggregate write | 30 GB/s | 80 GB/s | 250 GB/s |
The same H200 guidance says its “Best” single-node read level should ideally approach the system’s 80 GB/s maximum network performance. That is a reference-design observation, not a promise that storage will deliver that rate to every node or workload.
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B200 reference-architecture targets
B200 uses different labels and presents these aggregate targets. NVIDIA ties the Enhanced level to cases where data I/O is materially important, datasets exceed local cache, and multimodal or larger models are used.
| B200 reference target | Standard | Enhanced |
|---|---|---|
| One SuperPOD SU: aggregate read | 40 GB/s | 125 GB/s |
| One SuperPOD SU: aggregate write | 20 GB/s | 62 GB/s |
| Four SuperPOD SUs: aggregate read | 160 GB/s | 500 GB/s |
| Four SuperPOD SUs: aggregate write | 80 GB/s | 250 GB/s |
These H200 and B200 figures describe separate reference architectures. Do not combine them into a single platform specification or treat either set as a target that applies automatically to a different cluster.
Measure metadata separately from data bandwidth
Throughput measures bytes moved; metadata performance measures operations on files and directories: creating, opening, closing, listing, renaming, and deleting them. A job can therefore stall during dataset discovery, namespace scans, or parallel startup even if large sequential reads look fast. This risk is especially relevant when a dataset consists of many small files.
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Amazon Web Services defines filesystem metadata IOPS as the rate at which files and directories can be created, listed, read, and deleted. For FSx for Lustre Persistent 2, AWS documents the following operation rates per provisioned metadata IOPS. The rates are specific to that product, and the supported rate depends on operation type; they are not a conversion formula for other filesystems.
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| FSx for Lustre Persistent 2 operation | Operations per second per provisioned metadata IOPS |
|---|---|
| File create, open, or close | 2 |
| File delete | 1 |
| Directory create or rename | 0.1 |
| Directory delete | 0.2 |
Ask for metadata benchmarks that match your file and directory counts, file sizes, operation mix, client concurrency, and warm or cold metadata-cache state. Include namespace scans and job startup—not only a synthetic metadata rate.
Test latency, cache behavior, and checkpoint impact
AI storage can face bursts as well as sustained traffic, variable I/O patterns, and sensitivity to slow operations. Meta Engineering described its AI workloads in those terms on July 1, 2026, including a need for predictable, bounded pMax latencies. Treat this as an operator’s account of its workloads, not a universal specification. In your own evaluation, request median and tail latency under the intended concurrency rather than relying on idle or single-client latency.
Test both a cold first pass and warm rereads. NVIDIA’s B200 reference architecture illustrates that cached reads can be an order of magnitude faster than remote-storage reads; actual results depend on data locality, cache size, hit rate, and implementation. NVIDIA’s H200 and B200 guidance also describes local NVMe as useful for cache or staging in DGX systems, not as a replacement for shared storage.
Checkpointing is another distinct test. Large synchronous writes can block forward progress, so measure completion time at the expected checkpoint size and interval, then observe whether those writes slow training reads. A high write-bandwidth result alone does not establish that checkpointing will fit the job’s timing requirements.
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Choose the interface and storage tiers the application needs
Storage interfaces are not interchangeable. Google Cloud’s AI/ML storage guidance distinguishes object storage for massive datasets and capacity, throughput, and durability needs from Managed Lustre, a POSIX parallel filesystem intended for specialized low-latency and high-concurrency metadata performance in training and inference. Choose according to whether applications require object APIs, POSIX semantics, shared-file access, or a staged combination; confirm the data movement costs and operating workflow for the environment you will deploy.
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A single tier need not serve every task. NVIDIA’s older DGX SuperPOD components description presents a pattern pairing high-performance storage for throughput and parallel I/O with user storage optimized for higher IOPS and metadata work. Treat it as a design pattern, not an assurance about current deployment requirements; check compatibility and certification for the system you plan to buy.
Check scaling, compatibility, and the operating model
Establish whether performance and capacity grow together or independently. Ask what expansion entails, whether it interrupts service, and how the design behaves during failures and recovery. Include data protection and recovery objectives, software compatibility, administration, and support in the evaluation—not just peak performance.
Compare cost at the usable capacity and performance level your workload needs. Account for networking, licenses or cloud charges, replication, snapshots, and staffing. A lower-cost capacity tier is not comparable to a higher-performance configuration if it cannot meet the job’s measured requirements.
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NVIDIA’s DGX SuperPOD FAQ lists DDN AI400X, Dell PowerScale, IBM Storage Scale, NetApp E-Series (BeeGFS), NetApp A90 (ONTAP), Pure Storage FlashBlade, WEKA, and VAST as certified storage for that program. The FAQ warns that using non-certified storage or changing fabric topology can affect SuperPOD qualification. The list is specific to the DGX SuperPOD program: certification does not show that one option is best for every workload, and current qualification should be confirmed against the design at procurement time.
Run a proof of concept that resembles production
Use representative data and the actual client software, not a substitute benchmark whose file layout or access pattern differs from production. Agree on test conditions in advance and retain them with every result.
- Load the production-like file-size distribution, directory shape, and dataset volume.
- Run the expected number of clients and concurrent jobs over the intended network topology.
- Test the real mixed read/write pattern, including a cold first pass and repeated warm passes.
- Exercise metadata-heavy startup and namespace scans with production-like file and directory counts.
- Write checkpoints at the expected size and interval while training reads are active.
- Run long enough to reveal limits beyond short bursts, then test the expected scale-out configuration.
- Observe failure recovery and the administrative effort needed to operate the system.
- Capture per-node and aggregate read/write throughput, metadata operations per second, median and tail latency, checkpoint completion time, and GPU idle or data-wait indicators.
Record cache state and test conditions alongside the results. This makes it possible to distinguish a system that meets a brief peak from one that sustains the required job mix at the scale and concurrency you intend to deploy.
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