Use shared scale-out file storage for the active training path when workloads depend on POSIX-style access, frequent metadata operations, many small files, or low-latency synchronous checkpoint writes. Use object storage for scalable dataset repositories and durable, lower-cost retention when the access pattern and restore target suit it. For many AI workloads, the practical design is tiered: write active checkpoints to fast shared storage, then archive completed checkpoints asynchronously to object storage.
What the terms mean—and why the label is not enough
“Scale-out NAS” usually refers to network-accessible file storage that can grow across nodes or appliances. A parallel file system is a related but distinct category designed to aggregate I/O across clients and storage resources. They are not interchangeable terms, and a parallel-file-system recommendation is not automatically a benchmark of NAS against object storage.
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Object storage presents data through an object API. A mount, cache, or workload-specific service can provide file-like access, but that does not make every object-backed path behave like a native shared file system. Validate the actual protocol, client behavior, network, consistency model, and application requirements rather than choosing by category name.
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| Workload or requirement | Usually the better starting point | What to verify |
|---|---|---|
| Active training with many small files or high metadata concurrency | Shared scale-out file storage or a parallel file system | Metadata rate at the intended client count, small-file behavior, and whether the data pipeline can reduce per-file operations. |
| Large dataset repository accessed through a supported object workflow | Object storage | Access layer, locality, cache behavior, request pattern, and end-to-end throughput for the framework and job size. |
| Synchronous checkpoint writes where training waits for durable commit | Low-latency shared file storage | Checkpoint commit time and the resulting impact on accelerator step time. |
| Asynchronous checkpointing and long-term retention | Fast active tier plus object storage | Archive throughput, consistency, retention/versioning, and time to restore a checkpoint. |
| POSIX-dependent application behavior, including rename or shared-path expectations | Native shared file storage, unless the object access layer explicitly supports the needed behavior | Atomicity, rename semantics, metadata performance, and cache consistency under concurrent clients. |
This is a starting point, not a universal speed ranking. NVIDIA, Google Cloud, and Microsoft guidance emphasizes workload-specific behavior; compare the actual implementation and benchmark the application at target scale.
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Choose storage for training datasets
When file storage helps
Training jobs that open many small files or issue substantial metadata traffic can benefit from a shared file system or parallel file system. Google Cloud’s TPU VM guidance lists Managed Lustre for files under 1 MB or workloads with high metadata concurrency. That recommendation is specific to the documented TPU VM context, not a general proof that every file system will outperform every object service.
NVIDIA advises reducing direct small-file operations where practical. It names HDF5, LMDB, and TFRecord as formats that can reduce file-system metadata access, while noting their own memory or memory-mapping considerations. Treat these as options to test with the framework and data pipeline, not universal format prescriptions.
When object storage fits
Object storage is a strong candidate for a large dataset repository when the training stack has a suitable access path and the locality and request pattern meet the job’s needs. Google Cloud’s TPU guidance discusses Cloud Storage FUSE and workload-specific profiles; its Rapid Bucket is a zonal object-storage option. Those are service-specific designs, not stand-ins for a generic object endpoint.
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For its TPU VM guidance, Google Cloud recommends regional Cloud Storage buckets with Rapid Cache for lowest cost, Rapid Bucket for performance and scale, and Managed Lustre when teams standardize on Lustre for metadata-heavy workloads. The appropriate choice depends on the described service configuration and the workload’s I/O shape.
Choose storage for checkpoints
Start with checkpoint behavior
Before selecting a tier, establish whether each training rank writes a shard, whether ranks coordinate, how the checkpoint is finalized, and how restart reads the saved state. Synchronous checkpointing puts storage latency directly on the training path; asynchronous checkpointing can move that work out of the immediate training loop, but still requires correct completion, naming, and recovery behavior.
Use a fast active tier for latency-sensitive writes
Google Cloud recommends Managed Lustre for low-latency synchronous checkpoints on TPU VMs, and Rapid Bucket for high-throughput asynchronous and multi-tier checkpointing. In Microsoft’s Azure example, GPU compute writes active checkpoints to nearby Azure Managed Lustre and completed checkpoints are exported asynchronously to Blob Storage. Microsoft states: “Archival is decoupled from the training loop, so it doesn’t impact write throughput to GPUs.” This describes that Azure tiered-checkpoint architecture, not a guarantee for all archive pipelines.
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For the example configuration in Microsoft’s Azure Managed Lustre tiered-checkpoint documentation, updated 2026-07-09, a 500 tier configured with 128 TiB is reported at approximately 64 GB/s write throughput, with an approximately 912 GiB checkpoint taking about 15 seconds to commit. These are example workload figures, not a like-for-like comparison with another provider’s service claim.
Keep recovery time in the design
Object storage can hold completed checkpoints for longer retention, but archive capacity alone does not establish restart performance. Microsoft’s Azure guidance describes rehydrating archived checkpoints with import jobs and recommends retaining the latest checkpoint on Managed Lustre for the fastest restart. Its documentation reports approximately 7.5 GB/s default data-mover throughput between Azure Managed Lustre and Blob Storage, aligning with the default Blob account ingress limit; it directs customers to support for higher sustained archive throughput. Include the restore path and measured recovery time in the design, not only the write path.
Account for service-specific checkpoint workflows
AWS SageMaker documentation describes separate behaviors that should not be generalized to every FSDP or object-storage deployment. In the documented SageMaker model-parallel workflow, FSDP checkpoints require a shared network file system such as Amazon FSx, and asynchronous local checkpoints can overlap I/O with later training iterations. SageMaker’s general checkpoint feature instead synchronizes checkpoint files from a local container directory to S3: existing S3 objects are copied into the container when the job starts, and new checkpoints are synchronized during training.
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Prevent checkpoint collisions and consistency errors
- Give distributed writers unambiguous destinations. AWS SageMaker warns that its high-level S3 location does not automatically add per-instance prefixes or suffixes. Multi-instance jobs need distinct paths or filenames where workers would otherwise overwrite one another.
- Define finalization behavior. Google Cloud’s TPU guidance describes hierarchical namespace as supporting atomic directory renames for checkpoint finalization. This is a specific bucket feature and configuration; do not assume identical rename behavior from every mounted object store.
- Set synchronization and versioning rules. Microsoft recommends synchronization between Azure Managed Lustre and Azure Blob Storage for consistency across distributed AI workloads, and Blob versioning for reproducibility. Its Azure integration documentation also states that deletes, renames, and moves on the Lustre side do not propagate to Blob Storage, so naming and retention policies must account for that behavior.
- Test cache and file-semantics assumptions. For a mounted object store or adapter, verify rename and atomicity behavior, metadata performance, cache consistency, and the operations the training framework expects.
Benchmark the full training and recovery path
Do not compare headline bandwidth figures from different APIs, providers, configurations, or test conditions as though they were one controlled benchmark. Google Cloud’s Cloud Storage Rapid documentation, last updated 2026-07-10, claims sub-millisecond latency, up to 15 TB/s aggregate throughput, and up to 20 million queries per second for Rapid Bucket. Those are product claims for that service, not generic object-storage performance. Google Cloud’s TPU storage guidance separately says hierarchical namespace can provide up to 8 times higher initial QPS for reads and writes than buckets without it; this is a bucket-configuration claim, not a file-system comparison.
NVIDIA’s DGX Best Practices guidance gives 150–200 MB/s per GPU for 1080p files and says to consider more for 4K or uncompressed files. This is guidance for its described workload context, not a universal storage requirement. NVIDIA also cautions: “As always, it is best to understand your own applications’ requirements to architect the optimal storage system.”
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- dataset read throughput and request behavior, including metadata-heavy and small-file phases;
- accelerator idle time and training step-time impact while data is read;
- checkpoint write and commit time for synchronous and asynchronous paths;
- restart and restore time from the tier that will actually hold the recovery copy;
- behavior under concurrent clients, network contention, and the expected failure or recovery conditions.
Include reliability, resiliency, and manageability alongside performance, as NVIDIA’s guidance recommends. Also account for locality, operational effort, capacity, access and transfer charges, retention, and current regional service limits before procurement; cloud capabilities and pricing can change.
A practical architecture decision
- Describe the I/O shape. Record whether the job does large sequential reads, random reads, mixed reads and writes, frequent small-file opens, or metadata-heavy traversal.
- List application semantics. Identify POSIX or object API requirements, rename/finalization behavior, shared paths, cache expectations, and how each worker names its checkpoint output.
- Set the checkpoint contract. Decide whether writes are synchronous or asynchronous, which rank writes which state, how a completed checkpoint is identified, and the required restart time.
- Place each data class on a tier. Keep latency-sensitive training data or active checkpoints on a suitable shared file tier when measurements support it; put repository or completed-checkpoint data on object storage when its access and restore properties fit.
- Test with the real pipeline and target scale. Benchmark data loading, checkpoint commits, and restore rather than relying on isolated storage bandwidth claims.
- Document consistency, retention, and recovery. Specify synchronization, versioning, archive lifecycle, restore procedure, and who operates each layer.
As Microsoft’s Azure storage recommendations put it: “Ensure data consistency across distributed AI workloads by setting up synchronization between Azure Managed Lustre and Azure Blob Storage.” That guidance is specifically about the Azure tiers it documents; other systems need their own verified consistency plan.
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