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For most AI platforms, use object storage as the durable home for datasets, checkpoints, and model artifacts, then add caching or a parallel file system only where measured latency, metadata activity, or I/O demand justifies it. The goal is not to choose one storage type for everything: it is to keep data flowing through preprocessing and into accelerators, while making checkpoint recovery practical.
How to design the storage path for AI workloads
Think of storage as a path from durable data to compute and back again—not as a single capacity decision. A training job reads samples, preprocesses them, updates model parameters, and periodically writes checkpoints. If any stage cannot keep pace, accelerator utilization and end-to-end training time can suffer.
- Keep the durable copy in object storage. Store raw or prepared datasets, checkpoints, and trained models in a scalable object repository. Google Cloud describes Cloud Storage as a central repository for training and bulk-inference datasets, checkpoints, and final models.
- Choose an access layer that matches the workload. Use an object API or a mounted interface where its semantics and performance fit. A mount over object storage does not automatically provide the behavior of a full POSIX parallel file system.
- Add a cache or staging tier for demonstrated reuse or locality needs. Frequently reused data may benefit from caching close to compute. Local NVMe can serve as a cache or staging tier; it is not a replacement for the central durable repository.
- Evaluate a parallel file system for demanding phases. Consider one when low latency, metadata concurrency, full POSIX behavior, small-file access, or intensive training and checkpoint I/O are requirements.
- Measure the complete path and tune it with representative jobs. Track input and checkpoint performance alongside compute, network, and request behavior. Do not size from a vendor reference number alone.
Decide from the workload, not the storage label
Before choosing tiers, characterize both the data and the job. A large sequential read workload can behave very differently from a job that opens many small files or repeatedly requests random data. Include preprocessing and network transfer in the assessment: device bandwidth alone does not describe what reaches the GPU.
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- Sequential versus random access, plus the distribution of object or file sizes.
- Concurrent readers and writers, metadata-operation intensity, and whether jobs share data.
- Reuse across epochs or jobs, and the working-set size relative to available cache.
- Training or inference phase, checkpoint size and frequency, and recovery objectives.
- Distance between storage and compute, including the relevant geographic or zonal placement.
- Latency, per-node and aggregate throughput, capacity growth, durability, compatibility, operating effort, and total cost.
Compare alternatives against those requirements. Object storage is a strong scalable repository, but it does not inherently meet every low-latency or metadata-heavy access pattern. A cache can help when it captures useful repeated reads; a parallel file system can help where its access semantics and concurrency suit the workload. Neither is a universal winner.
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Match each storage tier to its job
| Tier or interface | Best-fit role | Key design consideration |
|---|---|---|
| Object storage | Durable repository for large datasets, checkpoints, and model artifacts | Use an appropriate object client and deliberately tune concurrency, locality, and request behavior. |
| Mounted object interface | File-oriented access to object-backed data when the interface fits the application | A mount does not make object storage equivalent to a full POSIX file system; validate semantics and the workload’s latency and I/O pattern. |
| Cache or local staging | Hot or repeatedly accessed data placed closer to compute | Confirm that working-set size and reuse justify the extra tier; measure cache behavior. |
| Parallel file system | Workloads needing low latency, high metadata concurrency, POSIX behavior, or intensive I/O | Evaluate it for the specific workload phase and platform rather than treating it as a universal replacement for object storage. |
For example, Google Cloud identifies Managed Lustre for workloads that need a parallel file system and cautions that Cloud Storage FUSE is not appropriate for some low-latency, small-file, or random-I/O workloads. Those recommendations are specific to the named Google Cloud services, not a general benchmark of all mounted object interfaces or parallel file systems.
Improve object-store throughput without guessing
For object-based access, performance depends on how clients issue requests as well as on the storage service. Amazon Web Services’ S3 performance guidance says, “We recommend starting with a single request at a time.” Begin simply, measure the limiting resource, and increase concurrency only when the measurements support it.
Rank #2
For large objects, test parallel reads
Test concurrent byte-range or multipart downloads for large objects. Where appropriate, align byte-range reads with the object’s original multipart boundaries. Start with low concurrency and observe whether the limit is the client, CPU, network, request rate, or storage path before increasing parallelism.
For repeated reads, validate caching
A cache can reduce direct S3 requests and improve access to hot content, but only if the data is reused enough to offset the added tier. Check the working set, cache hit behavior, and transfer path under actual jobs rather than assuming that a cache will help.
Rank #3
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For REST clients, reuse connections and handle throttling
Reuse HTTP connections to avoid repeated connection setup. Follow SDK retry behavior or use exponential backoff for 503 responses. AWS advises implementing high-request-rate strategies when request rates are consistently above 3,500 PUT/COPY/POST/DELETE requests per second or 5,500 GET/HEAD requests per second per prefix. It also notes that scaling occurs gradually and temporary 503 responses can arise while the system adapts, so ramp request rates gradually and watch request and error metrics.
Keep data and compute close where the platform allows
Placement can affect the path between storage and accelerators. Google Cloud’s Rapid products use zonal placement; that is a service-specific placement characteristic. Measure the actual network path and cache behavior in the deployment rather than assuming proximity from a configuration label.
Treat checkpoints as a first-class I/O workload
Checkpoint writes and recovery reads can affect training time as directly as dataset input. NVIDIA notes that checkpoint operations can be synchronous, pausing training while they run. AWS guidance says the right checkpoint storage depends on model size, cluster scale, checkpoint frequency, and access pattern.
Measure checkpoint duration, write throughput, restore time, and the impact of checkpointing on the job. Set checkpoint frequency against both the cost of interruptions and the amount of work that would need to be repeated after a failure. Keep the durable recovery copy in an appropriate repository even if a faster tier is used during the job.
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AWS describes a large-scale scenario involving thousands of accelerators and concurrent loading of terabyte-sized checkpoints that took 10–15 minutes end to end. That is an observation for that scenario, not a general loading-time guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use vendor performance figures only within their stated scope
The following are vendor-published examples or service claims. The accessed documentation did not state a publication year for these figures. They are not cross-vendor benchmarks and should not be treated as interchangeable sizing targets.
| Source and figure | Stated scope | How to use it |
|---|---|---|
| Google Cloud: up to 15 TB/s | Stated maximum throughput for Rapid Bucket compared with other Cloud Storage products and bucket locations | A service claim for the stated comparison; validate the actual workload and configuration. |
| Google Cloud: up to 2.5 TB/s | Stated throughput for Rapid Cache | A product claim, not a general cache sizing target. |
| Google Cloud: more than 1 TB/s | Achievable throughput claim for the described Cloud Storage FUSE architecture with proper planning | Applies to the described architecture and planning conditions. |
| Google Cloud: 8 times higher queries per second | Hierarchical namespace comparison for object reads and writes versus buckets without hierarchical namespace | A scoped product comparison, not a guarantee for every workload. |
| AWS: 3,500 PUT/COPY/POST/DELETE or 5,500 GET/HEAD requests per second per prefix | Consistent request rates at which AWS advises implementing high-request-rate strategies; adaptation can be gradual, with temporary 503 responses | Use as AWS guidance for S3 request behavior, not as a universal object-store limit. |
| NVIDIA: 1.25 GB/s standard and 3.91 GB/s enhanced single-node aggregated system read; 0.63 GB/s standard and 1.95 GB/s enhanced single-rack aggregate system write; 90 GB/s standard and 281.25 GB/s enhanced full-SU system read; 45 GB/s standard and 140.63 GB/s enhanced full-POD system write | Named DGX SuperPOD Rubin NVL8 reference levels and the specified system scopes | Do not generalize these reference-architecture values to other systems. |
| NVIDIA: 4 GB/s per GPU | Stated read-performance requirement in cited automotive and computer-vision cases using high-resolution images, including uncompressed datasets that can exceed 30 TB | A workload example, not a default GPU-training target. |
Size the architecture with a repeatable measurement loop
- Build a representative test. Use realistic object sizes, access patterns, reader counts, preprocessing, cache state, and checkpoint behavior—not only a synthetic sequential read.
- Trace the full data path. Measure storage request latency and rate, client and preprocessing throughput, CPU use, network throughput, and the rate at which data reaches accelerators.
- Find the active bottleneck. Distinguish a storage limit from client-side processing, network transfer, metadata overhead, request throttling, or cold-cache effects.
- Change one factor at a time. Test connection reuse, request concurrency, range size, cache placement, or storage tier and compare results under the same workload.
- Test recovery as well as training. Record checkpoint write and load duration and verify that the intended recovery path meets operational objectives.
- Compare operating consequences. Include capacity and scaling, durability, platform compatibility, operational complexity, locality, and total cost alongside throughput and latency.
An enterprise NVMe SSD may be appropriate as local cache or staging when the platform supports it. Verify interface, endurance, capacity, and compatibility for the intended role; the cited guidance does not identify a universal hardware model.
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