Use shared scale-out file storage for active AI workloads that depend on file-system semantics, metadata-heavy access, or low-latency synchronous checkpoints. Use object storage for scalable dataset repositories and durable retention, especially when checkpoints can be archived asynchronously. Many teams need both: keep active data and the latest checkpoint on a fast file tier, then copy completed checkpoints to object storage.
There is no category-wide speed winner. “Scale-out NAS” and “parallel file system” are related but not interchangeable: NAS describes network file access, while parallel file systems are designed to aggregate I/O across clients and storage resources. The right choice depends on the actual implementation, access protocol, workload, network, and recovery requirements.
How do scale-out NAS and object storage differ for AI workloads?
The practical difference is how applications access and coordinate data. A shared file system presents files and directories, which can suit applications built around file-system operations. Object storage is accessed through an object API or a file-like adapter, and is often used as a scalable repository for datasets and retained checkpoints. A mount or cache can change how an object store behaves for a workload, but does not automatically give it the same latency, metadata behavior, or rename semantics as a native shared file system.
| Workload consideration | Shared scale-out file storage | Object storage |
|---|---|---|
| File-system access and metadata | Can suit workloads needing POSIX-style access, many metadata operations, or small-file handling. Performance depends on the system and client configuration. | Access is object-based unless a mount, cache, or workload-specific service provides a file-like layer. Validate metadata behavior and application compatibility. |
| Active training data | A candidate for metadata-heavy pipelines or workloads that need a shared file-system path. | A candidate for dataset repositories, particularly when the chosen access layer and data locality suit the training pipeline. |
| Checkpoint writes | Can suit synchronous, low-latency writes or workflows requiring a shared path. | Can suit asynchronous checkpointing and retention. Performance depends on the service, checkpoint pattern, and integration. |
| Retention and recovery | Useful for keeping active data or recent checkpoints close to compute; capacity and recovery design still matter. | Useful for durable retention when access tiers, retrieval time, lifecycle rules, and recovery objectives fit. |
These are workload tendencies, not guarantees about every product. NVIDIA’s DGX storage guidance emphasizes that applications’ requirements should determine the storage architecture.
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Where should training datasets live?
Use shared file storage when metadata and small files dominate
Many small files can turn metadata operations into a bottleneck even when the storage system has substantial bandwidth. Google Cloud’s TPU VM guidance identifies Managed Lustre as an option for files under 1 MB or high metadata concurrency. NVIDIA also cautions that direct access to many small files can reduce performance and advises benchmarking the application.
Where practical, consolidating examples into formats such as HDF5, LMDB, or TFRecord can reduce file-system metadata access. These formats have their own memory and memory-mapping considerations, so validate them with the framework and data pipeline rather than treating them as universal fixes.
Use object-backed datasets when the access layer fits
Object storage can be a suitable dataset repository, but assess the service and access layer rather than assuming all object endpoints behave alike. Google Cloud describes different TPU and GKE workload profiles involving Cloud Storage FUSE, regional Cloud Storage buckets with Rapid Cache, Rapid Bucket, and Managed Lustre. Its guidance positions regional buckets with Rapid Cache as a lower-cost option, Rapid Bucket for performance and scale, and Managed Lustre for teams standardizing on Lustre for metadata-heavy workloads. These are Google Cloud-specific recommendations, not general rankings of storage categories.
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Keep data locality in the design: the path between storage and compute, the number of concurrent clients, and the pipeline’s read pattern can affect accelerator utilization. Benchmark with the actual training input pipeline at the intended scale.
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Choose the write path based on checkpoint semantics
Before selecting a tier, establish the checkpoint format, which ranks or workers write shards, whether workers coordinate, and how restart reads the state. A single checkpoint file written by one process has different access and coordination needs from a distributed checkpoint assembled from many shards.
For synchronous checkpointing, where training waits for writes to finish, low-latency shared file storage can be a good fit. Google Cloud recommends Managed Lustre for low-latency synchronous checkpoints on TPU VMs. In one Azure Managed Lustre example configuration, Microsoft reports about 64 GB/s write throughput for a 500 tier configured with 128 TiB and about 15 seconds to commit an approximately 912 GiB checkpoint. Those are example-specific figures, not a general file-system benchmark.
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Asynchronous checkpointing can overlap storage work with later training iterations, but it changes the design requirements: the system must know when a checkpoint is complete and safe to archive or restore. Google Cloud recommends Rapid Bucket for high-throughput asynchronous and multi-tier checkpointing on TPU VMs. On Azure, Microsoft describes writing active checkpoints to Managed Lustre and exporting completed checkpoints to Blob Storage asynchronously.
Check service-specific checkpoint behavior
Checkpoint recommendations can depend on the platform’s workflow. AWS SageMaker’s model-parallel documentation says FSDP checkpoints require a shared network file system, such as Amazon FSx, in the described workflow, and describes asynchronous local checkpoints that overlap I/O with subsequent training iterations. This is guidance for that SageMaker implementation, not a universal requirement for every FSDP setup.
SageMaker’s general checkpoint feature synchronizes files from a local container directory to S3: objects already in the specified S3 location are copied into the container at job start, and new checkpoint files are synchronized during training. Its documentation warns that a high-level S3 location does not automatically add per-instance suffixes or prefixes. Distributed workers that write to a common location therefore need distinct paths or filenames where necessary to prevent overwrites.
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When does a tiered checkpoint design make sense?
A tiered design separates the training write path from long-term retention. Write active checkpoints to a fast shared file tier, then asynchronously export completed checkpoints to object storage. Retain the latest checkpoint on the fast tier when quick restarts matter; older checkpoints can move to object storage according to retention policy and recovery objectives.
Microsoft’s Azure example says, “Archival is decoupled from the training loop, so it doesn’t impact write throughput to GPUs.” That separation helps avoid making the training loop wait for an archive transfer, but it does not eliminate the need to plan archive and restore throughput. In the same Azure documentation, the default data-mover throughput between Managed Lustre and Blob Storage is approximately 7.5 GB/s, aligning with the default Blob account ingress limit; Microsoft directs users to support for higher sustained archive throughput.
Azure’s tiered-checkpoint guidance says deletes, renames, and moves on the Managed Lustre side do not propagate to Blob Storage. Treat the active and archive namespaces as separate: define stable naming, retention, and cleanup rules so an operation on one tier does not create an unintended mismatch on the other.
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What should you verify before choosing?
- I/O shape: Measure large sequential reads, random reads, writes, and mixed traffic using the actual training pipeline.
- File and metadata pattern: Record file sizes, file counts, directory operations, and metadata concurrency, not just total dataset size.
- Application semantics: Confirm required POSIX behavior, object API support, rename or atomicity needs, and cache consistency. Google Cloud’s TPU guidance describes hierarchical namespace as supporting atomic directory renames for checkpoint finalization; that behavior is specific to the configured bucket feature.
- Concurrency and locality: Test the expected number of clients and aggregate throughput, with compute and storage placed as they will be in production.
- Checkpoint and recovery: Measure checkpoint commit time and restore time. Define which workers write which paths, how completion is identified, and how versions are retained.
- Operations and cost: Include management effort, capacity, access and transfer charges, lifecycle rules, and retrieval behavior. Confirm current regional availability, service limits, security settings, and pricing before procurement.
- End-to-end impact: Measure accelerator idle time and step-time impact as well as storage throughput. Reliability, resiliency, and manageability matter alongside peak performance.
How should you interpret published performance figures?
Published numbers describe particular services, configurations, and workloads; they are not a like-for-like comparison between “NAS” and “object storage.” For example, NVIDIA’s DGX Best Practices storage guidance gives 150–200 MB/s per GPU for 1080p files and says to consider more for 4K or uncompressed files. That is workload guidance, not a universal storage requirement.
Google Cloud says hierarchical namespace can provide up to 8 times higher initial QPS for reads and writes than buckets without it. This is a claim about a Google Cloud bucket configuration, not a file-system comparison. Its Cloud Storage Rapid documentation, last updated 2026-07-10, lists sub-millisecond latency, up to 15 TB/s aggregate throughput, and up to 20 million queries per second for Rapid Bucket. Those are Rapid Bucket product claims, not generic object-storage characteristics.
The Azure Managed Lustre example and Google Cloud Rapid figures describe different products and conditions, so comparing their headline values directly would not establish which option is faster for a particular AI job. Run a representative benchmark at target scale and include checkpoint commits and restores, not just peak throughput.
What is the practical decision?
For active training, favor a shared file tier when the application needs file-system semantics, high metadata concurrency, small-file handling, or low-latency synchronous checkpointing. Favor object storage for dataset repositories and checkpoint retention when the access path, durability, lifecycle behavior, and restore time fit. If both active performance and economical retention matter, use a fast file tier for the active workload and asynchronously archive completed checkpoints to object storage.
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