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For most production AI training, use both: keep raw, immutable, and versioned datasets in object storage, then cache the active shards, indexes, and transformed batches on local NVMe SSDs beside the GPUs. Choose NVMe alone only when the working set fits the machine and you have an independent backup plan. Choose object storage alone when durability, sharing, elastic capacity, or retention matters more than the lowest possible I/O latency.
That split follows the way the two technologies are designed. Local NVMe behaves like a very fast, temporary working filesystem; object storage is a durable, broadly shared repository. The right answer depends on access pattern, throughput, latency, growth, update frequency, recovery requirements, and the costs of storage requests and data movement.
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What is the difference between NVMe SSDs and object storage for AI data?
NVMe SSDs are block devices attached directly to a workstation, server, or virtual machine. An operating system formats them with a filesystem, and training code reads ordinary files or database-like indexes with very low local latency. Object storage exposes a service endpoint instead: data is stored as objects in buckets or containers and retrieved through an API or a filesystem and caching layer.
The distinction matters because a training job rarely performs one kind of I/O. It may scan large sequential shards, make random reads for samples, write temporary preprocessing results, save checkpoints, and let many workers consume the same dataset. A device that is excellent for one pattern can be inefficient for another.
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| Decision factor | Local NVMe SSD | Object storage |
|---|---|---|
| Primary role | Hot cache, scratch space, staging, or active filesystem | Durable source of truth, archive, and shared repository |
| Latency | Lowest latency when attached to the worker | Network and service latency; usually higher for small or random reads |
| Throughput | Very high, but dependent on device, VM size, queue depth, and I/O block size | Scales with service, client, network, and request pattern |
| Capacity | Bound by the worker or workstation’s installed storage | Designed to grow without provisioning a disk in each worker |
| Sharing | Normally local to one host; copies are needed for other workers | Shared access for many workers through the service API |
| Durability | Not stated as a universal figure; depends on the device, host, and backup design | AWS documents 99.999999999% (11 nines) durability for its object-storage service |
| Failure behavior | May be lost with a failed or replaced host, especially for ephemeral instance storage | Objects are stored redundantly across multiple devices, systems, and regions according to the service design |
| Cost exposure | Hardware or VM storage cost, plus replacement and backup | Storage-class, request, retrieval, and possible egress charges |
Microsoft guidance recommends a filesystem for active AI data and specifically points to local NVMe or SSD filesystems when performance is the top priority. It describes that tier as temporary, high-performance storage for deep-learning training and real-time inference. The same guidance recommends moving inactive job data to Azure Blob Storage for long-term, cost-effective storage with scalable access tiers.
AWS makes a similar distinction: block storage is direct-attached, high-performance, low-latency storage, while EC2 instance store is appropriate for ephemeral, pod-local data. Object storage is intended to scale broadly and remain resilient by keeping objects across multiple devices, systems, and regions.
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Start with local NVMe when the active working set needs consistently low latency, preprocessing is I/O-bound, and the data fits on the worker or workstation. This is the sensible choice for a single researcher, a bounded experiment, or a production job whose input is already staged on each training node.
Low-latency input pipelines
If GPUs are waiting while workers decode, shuffle, or transform samples, a local filesystem can remove network round trips from the hottest part of the loop. NVMe is especially useful for random reads, frequent metadata operations, and repeated passes over the same shards. The gain is workload-specific: a large sequential scan may already saturate the network, while millions of small reads can expose latency much sooner.
Preprocessing and feature generation
Use NVMe as scratch space for decompression, tokenization, image transforms, temporary joins, and intermediate indexes. Keeping these writes local avoids turning every temporary file into an object-storage request. Give the scratch area a cleanup policy; it is not a substitute for retaining the original data.
Bounded datasets and independent backup
NVMe-only can be reasonable when the complete dataset and its derived artifacts fit the machine, workers do not need a shared copy, and an independent backup or source repository exists. Without that second copy, replacing a host can erase the only usable version of the data.
Important NVMe caveat: local does not always mean durable
A physical enterprise SSD, a persistent cloud volume, and a cloud instance-store NVMe device have different failure and replacement behavior. AWS identifies EC2 instance store as ephemeral and pod-local. Treat any instance-store or similarly temporary device as disposable: keep manifests, source data, and checkpoints elsewhere before starting a long run.
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When should object storage be the primary tier?
Make object storage the first destination when data must survive machine replacement, be shared by many workers, grow unpredictably, or comply with retention and versioning policies. It is also the natural landing zone for raw captures and immutable training releases.
Durability and recovery
AWS states that object storage can provide virtually unlimited scale and resilience by storing objects across multiple devices, systems, and regions. Its current product documentation specifies 99.999999999% (11 nines) durability. That figure describes the service’s durability design, not a guarantee that a training job will never fail: credentials, permissions, corrupted uploads, bad manifests, and application errors remain possible.
Many workers and changing fleets
Object storage lets workers start with the same logical dataset without pre-installing a disk in every host. This is useful when jobs scale out, workers are replaced, or training runs move between machine types and regions. Workers can fetch only the shards they need, while a local cache absorbs repeated reads.
Unpredictable growth and retention
Large video, image, audio, and telemetry collections often expand faster than local disks can be replaced. Object storage lets the repository grow and supports different access tiers for active, infrequent, and archival data. Microsoft specifically recommends transferring inactive job data to Azure Blob Storage for long-term storage and scalable access tiers.
Versioned and immutable releases
Keep raw inputs and released dataset versions in object storage so a later experiment can reproduce the exact inputs. Store a manifest with each release that records shard names, checksums, preprocessing configuration, and the code or container version used to create derived data. The manifest turns a bucket full of files into a reproducible dataset rather than an undocumented pile of objects.
Is object storage too slow for model training?
It can be too slow for a tight training loop if every sample is fetched remotely, but object storage is not inherently unsuitable for training. The result depends on request size, sequential versus random access, concurrency, network bandwidth, client behavior, and the framework’s data-loader design.
Why latency appears
A local read is handled by the host’s storage stack. An object read adds serialization, a network path, service processing, authentication, and response handling. Thousands of tiny objects amplify that overhead. A dataset packed into reasonably large shards and read with parallel workers presents a very different workload from one that performs a remote request for every small record.
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How to use object storage without starving GPUs
- Keep the canonical data in object storage, but stage the next training shards on each worker’s NVMe cache.
- Read large, sequential shards where the framework and file format support it, instead of issuing a request per sample.
- Prefetch asynchronously so data transfer overlaps GPU computation.
- Pin frequently reused indexes, metadata, and transformed batches locally.
- Use a cache eviction rule that protects the current epoch and removes the least useful older data first.
- Measure cache-hit rate, worker wait time, throughput, and failed or retried requests; do not infer performance from a storage product’s headline limit.
For a one-pass job over data that will never be reused, downloading once to local NVMe can be faster than repeatedly streaming objects. For a fleet of workers sharing a changing corpus, direct object access with a well-designed cache may avoid the cost and operational burden of maintaining a full copy on every node.
Do you need local SSD scratch space?
Most multi-worker training systems benefit from some local scratch capacity, even when object storage is the source of truth. The scratch tier absorbs temporary writes and gives the data loader a low-latency place to hold hot shards.
What belongs on scratch
- Recently used dataset shards and indexes.
- Decoded or transformed batches that are expensive to regenerate.
- Temporary files created by preprocessing and shuffle operations.
- Local logs and intermediate artifacts that are copied to durable storage at job completion.
What should not be scratch-only
- The only copy of raw data.
- The only copy of a released dataset manifest.
- The only checkpoint from a long-running training job.
- Credentials, configuration, or metadata needed to reconstruct the run.
Size the cache for the active working set and the amount of data that must be prefetched, not for the entire historical repository. If a cache fills, the usual symptoms are failed writes, stalled workers, or aggressive eviction that causes repeated downloads. Leave headroom for temporary expansion and monitor free space during the run.
Why is a hybrid design the usual production choice?
A hybrid layout assigns each tier the job it performs best. Object storage holds raw, immutable, and versioned data; local NVMe holds the hot working set close to the GPUs. This arrangement separates durability from latency instead of forcing one system to provide both.
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- Upload raw files to an object-storage bucket and write a manifest for the dataset release.
- Validate object counts, sizes, checksums, and metadata before publishing the release to workers.
- At job start, download or stream the first shards into each worker’s NVMe cache.
- Run preprocessing and training from the local filesystem while asynchronously fetching upcoming shards.
- Write checkpoints and final metrics to object storage during the run, not only when the job exits normally.
- Retain transformed datasets only when they are reproducible, expensive to regenerate, or needed by later jobs; otherwise let the cache expire.
- When a job ends, remove scratch data and keep the manifest, checkpoints, and selected artifacts in the durable tier.
This design also limits blast radius. Losing one worker invalidates its cache, not the dataset. A replacement worker can repopulate the cache from the canonical objects and resume from a durable checkpoint.
How should you decide for a specific dataset?
Use the following sequence before committing to a storage layout. AWS summarizes the relevant dimensions as access method, random versus sequential access, required throughput, frequency of access, update pattern, and availability and durability constraints.
- Describe the access pattern. Record whether the framework performs sequential scans, random sample reads, repeated epochs, frequent small metadata lookups, or large checkpoint writes.
- Define the active working set. Estimate how much data must be available during one step, one epoch, and the full job. Include decoded and transformed intermediates, not just compressed source files.
- Set the durability boundary. Decide which objects must survive host loss, accidental deletion, or a new training run. Put those objects in the durable repository and document retention and versioning.
- Map sharing requirements. If several workers or teams need the same release, favor a shared object repository. If each worker owns an independent copy and the dataset is bounded, local staging may be simpler.
- Check growth and fleet changes. A disk plan that works for today’s dataset may fail when worker count, region, model size, or retention period changes.
- Account for the complete cost. Include storage, API requests, retrieval and egress, network transfer, NVMe hardware or VM charges, backup, and the engineering time required to operate caches and restore data.
- Benchmark the real pipeline. Test the framework, file format, compression, worker count, and target hardware together. Choose the design that keeps the accelerator fed while meeting recovery and sharing requirements.
How do you benchmark NVMe and object storage fairly?
Microsoft notes that NVMe performance depends on VM size and I/O block size, so advertised limits are not a substitute for testing the intended workload. Compare end-to-end training behavior, not only a synthetic read number.
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Test the same data and software
Use identical shards, compression, sampler, batch size, preprocessing code, and number of data-loader workers. Run one test from a warm NVMe cache, one that populates the cache, and one that reads directly from object storage. Record the region and machine type so the result is reproducible.
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- Samples or batches delivered per second.
- GPU utilization and time spent waiting for input.
- Read and write throughput at the filesystem and application layers.
- Tail latency for random reads and metadata operations.
- Cache hit and miss rates, object requests, retries, and failed transfers.
- Time to restore a worker and resume from a durable checkpoint.
Repeat after changing shard size, concurrency, compression, VM size, or region. There is no universal NVMe-versus-object-storage benchmark or total-cost figure that applies across frameworks, formats, concurrency levels, and pricing models.
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The cache fills during a run
Keep only the active working set, remove stale transformed data, and reserve free space for temporary files. If the workload repeatedly evicts and redownloads the same shards, enlarge the cache, increase shard locality, or reduce the number of concurrently active datasets.
A worker disappears and loses its local data
Assume local or instance-store NVMe can vanish. Rebuild the cache from object storage and resume from a checkpoint that was written to the durable tier. A job that depends on an unreplicated local checkpoint must restart from an earlier state.
Object requests dominate runtime
Look for tiny objects, serialized reads, insufficient prefetching, or a region mismatch. Consolidate records into training-friendly shards, parallelize independent reads, and cache indexes and hot data locally. Validate that retries are not multiplying request volume.
Different workers see different dataset contents
Publish immutable dataset releases and pass a release identifier or manifest to every worker. Do not let a running job mix files from an actively changing directory unless the framework explicitly supports that behavior.
Costs exceed the storage estimate
Separate capacity from activity. Object-storage bills can include requests, retrieval, and egress in addition to stored bytes; NVMe designs carry hardware, VM, replacement, and backup costs. Review access frequency and data movement before changing tiers, because a cheaper byte price can be offset by frequent reads or transfers.
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Which option is cheapest for a large AI dataset?
Object storage is usually the more practical durable home because it avoids buying and synchronizing a complete disk copy on every worker, but a universal cheapest answer cannot be stated. Pricing varies by provider, storage class, region, request volume, retrieval policy, egress, hardware availability, and contract terms.
Compare a full lifecycle rather than a monthly capacity line. For NVMe, include the disk or VM, provisioning, backup, replacement, unused capacity, and the cost of copying data to additional workers. For object storage, include upload and download requests, retrieval from lower-cost tiers, network transfer, caching hardware, and the engineering effort to manage manifests and prefetching.
A common economic pattern is to keep infrequently accessed or historical data in object storage and pay for a smaller NVMe cache that turns the active training set into local reads. Recalculate when the access pattern, region, model, or retention policy changes; cloud prices and hardware availability are volatile.
What should you use for your next training job?
Use NVMe first when low latency is the immediate constraint, preprocessing is I/O-bound, and the working set fits locally with an independent backup. Use object storage first when the dataset must be durable, shared, versioned, or able to grow beyond one host. For most production training, keep the canonical dataset in object storage and use local NVMe as a disposable, measured cache and scratch tier.
Frequently Asked Questions
Can multiple training workers share one local NVMe drive?
Usually not directly: a local NVMe device is attached to one host, so other workers need their own staged copy or a separate shared filesystem. Treat the object-store manifest as the coordination point when workers populate independent caches.
Should raw and transformed datasets have the same retention period?
Not necessarily. Raw data is often retained for auditability and future preprocessing, while transformed data can be regenerated and may be deleted sooner when its recipe and release manifest are preserved.
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What happens if a worker is replaced halfway through an epoch?
A replacement can rebuild its local cache from the durable dataset, but it needs a checkpoint and data-position metadata that were written outside the lost worker. Without those, the job may repeat part of the epoch or restart from an earlier checkpoint.
When should I rerun the storage benchmark?
Rerun it after changing the model framework, file format, compression, worker count, VM size, region, or dataset access pattern. Each change can alter request concurrency, block size, cache behavior, and the balance between network and local I/O.
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