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Most deployments need more than one storage class: a fast tier close to compute for active data and scratch work, scalable object storage for authoritative or retained datasets, and a checkpoint path sized to the time your recovery plan allows. The best design balances performance, consistency, durability, growth, interoperability, power and total cost.
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Start with the workload, not the storage product
AI jobs create different I/O patterns. A storage system that is excellent for a large sequential dataset can still leave GPUs waiting if the application performs many small metadata operations or repeatedly rereads samples from a cold tier. Write down the workload roles you will run before comparing platforms.
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Training
Distributed training commonly rereads the same dataset for every epoch. NVIDIA’s DGX SuperPOD B200 architecture states that “The key I/O operation in DL training is re-read.” That makes sustained read delivery, cache behavior and consistency across workers more important than a single peak-write number. Training also produces checkpoints, logs and intermediate artifacts, so write performance and recovery reads matter as well.
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Fine-tuning and evaluation
Fine-tuning may use smaller datasets than pretraining but can still have high parallelism and frequent checkpointing. Evaluation jobs often launch many readers at once, creating bursty metadata and small-file traffic. Test the actual framework, worker count and file layout rather than extrapolating from the size of the model.
Inference
Batch inference can stream large inputs and model weights, while online inference is usually more sensitive to latency, predictable tail behavior and fast model-load or restart times. If models are loaded from object storage through a mount or adapter, measure startup and cache-warm behavior; an API-compatible path is not automatically equivalent to a local filesystem.
Data preparation
ETL, feature generation, shuffling, tokenization and format conversion can create a mix of large sequential transfers, random reads, temporary files and metadata-heavy directory operations. These jobs may compete with training for bandwidth. Include them in the benchmark or isolate them on a separate tier.
Checkpointing and restoration
A synchronous checkpoint can pause workers while data is persisted. Restoration is a high-throughput read workload that directly determines how quickly a failed or interrupted job resumes. Record both save and restore time, not merely the checkpoint file size.
| Workload | Typical storage pressure | Measurements to capture |
|---|---|---|
| Distributed training | Repeated dataset reads, concurrent clients, checkpoint writes | Sustained and tail read latency, bandwidth per worker, cache hit rate, metadata rate, pause time |
| Fine-tuning | Parallel reads with frequent saves | Read throughput at target worker count, checkpoint interval and duration, restore time |
| Online inference | Model-load bursts and latency-sensitive reads | Cold and warm start latency, p95/p99 latency, concurrent request behavior |
| Batch inference | Large scans and output writes | Streaming bandwidth, queue depth, temporary-space capacity |
| Data preparation | Mixed sequential/random I/O and metadata operations | Small-file performance, rename/stat rates, scratch bandwidth and spill behavior |
Compare performance as a vector, not one headline number
Storage vendors often advertise a maximum throughput measured under favorable conditions. An application sees a combination of bandwidth, IOPS, latency, metadata service, network behavior and cache state. Compare those dimensions at the concurrency and file sizes your workload actually uses.
- Read and write bandwidth: Measure sustained aggregate throughput and per-client throughput. Record both steady-state and burst behavior.
- IOPS and request size: Small random operations can exhaust IOPS or CPU long before a sequential bandwidth limit is reached.
- Average and tail latency: p95 and p99 delays expose stalls hidden by an average. Tail latency can synchronize workers behind the slowest request.
- Metadata operations: Count open, stat, create, rename, list and delete operations. A system can deliver high bulk bandwidth while struggling with millions of files.
- Concurrency: Test the full GPU and worker count, not one client. Include simultaneous training, preparation and checkpoint traffic if that will occur in production.
- Cache state: Run cold-cache and warm-cache trials. A result that depends on data fitting in cache may not represent the first epoch or a recovery after eviction.
- Network and protocol overhead: Measure the complete path from the application to storage, including the client library, mount layer, network and encryption.
Track GPU idle time, data-loader stalls and job completion time next to storage counters. If GPUs are waiting for input, storage may be the bottleneck even when a dashboard reports unused theoretical capacity elsewhere.
Size capacity and bandwidth together
Build a sizing inventory
Record these inputs for each environment:
- Dataset size today, expected growth and the number of retained versions.
- File or object size distribution, directory depth and format.
- Sequential versus random read/write mix and the number of epoch rereads.
- GPU count, worker count, parallel clients and expected concurrent jobs.
- Checkpoint size, interval, save target and maximum acceptable restoration time.
- Cache assumptions, temporary scratch space and space needed for failed-job retries.
- Availability, durability, backup, retention and deletion requirements.
- Required APIs, filesystem semantics, identity integration and geographic placement.
- Network transfer limits, power budget, maintenance windows and spending limit.
Model steady-state demand and peaks. Add headroom for dataset growth, rebuild traffic, maintenance, degraded operation and jobs that overlap unexpectedly. A capacity plan that leaves no room for a rebuild can meet today’s benchmark while failing during a disk or node replacement.
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Use reference figures only as starting points
NVIDIA’s B200 reference architecture provides configuration-specific examples. Its table lists 40 GB/s aggregate system read and 20 GB/s write for a “Standard” single-SU configuration, and 125 GB/s read and 62 GB/s write for “Enhanced.” At four SUs, the listed figures are 160/80 GB/s and 500/250 GB/s respectively. These are reference-architecture values, not a universal minimum or an application guarantee.
The same NVIDIA guidance says some high-resolution computer-vision workloads need 4 GB/s of read performance per GPU and suggests at least half of the recommended read performance as write performance for LLM and large-model use cases. Treat those statements as workload guidance and validate them with your own clients, data layout and concurrency. NVIDIA explicitly recommends characterizing workloads and organizational needs before finalizing performance and capacity requirements.
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A 2024 SNIA educational presentation describes general-purpose training as potentially requiring up to 1 GB/s per high-end GPU for reads. That estimate is tied to that presentation and date; it is useful context, not a current requirement for every GPU or model. A practical calculation is to divide the data that must be delivered during a target interval by that interval, then verify the result in an end-to-end run.
Separate application results from product limits
Google Cloud documents service-specific limits including up to 15 TiB/s for Rapid Bucket, up to 2.5 TiB/s for Rapid Cache and up to 10 TiB/s for Managed Lustre, with sub-millisecond latency described in specified product contexts. Those are claims for the named Google Cloud services and configurations; they are not directly comparable, vendor-neutral benchmarks. A product limit also does not prove that your framework, network or client can achieve it.
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Choose the storage class for each job
| Storage class | Good candidate use | Questions to test before buying |
|---|---|---|
| Object storage | Large datasets, model weights, bulk inference, retained or inactive data, asynchronous or tiered checkpoints | Can the application use the API or mount layer efficiently? What are request, retrieval and transfer costs? How do locality, caching and consistency behave? |
| Parallel file system | Active training data, POSIX-dependent applications, metadata-intensive jobs and synchronous checkpoints | Does it sustain required bandwidth and tail latency at target concurrency? How do capacity and metadata services scale? |
| Block storage pools | Dedicated, provisioned capacity and throughput for instances or node roles | Are capacity, IOPS and throughput reservations sufficient? How are volumes shared, snapshotted and protected? |
| Local NVMe or SSD | Job-local scratch, staging, cache and extreme-performance temporary work | What are form factor, endurance, firmware, failure, rebuild and restart implications? Is data replicated or disposable? |
| HDD or hybrid tiers | Large, infrequently accessed or less latency-sensitive datasets and archives | Do hot-data placement and cache behavior meet measured requirements? What retrieval delay and lifecycle charges apply? |
Object storage
Object storage is attractive for virtually unlimited-scale datasets and retention, but its API, consistency model and request pattern must fit the application. A filesystem adapter may add caching and parallelism, yet can also change semantics for locking, rename, listing and small files. Test the exact adapter and data pipeline, including cold starts and cache eviction.
Parallel file systems
Parallel filesystems are a natural fit when applications require POSIX behavior, shared views and high concurrent metadata activity. Benchmark metadata scaling separately from bulk bandwidth. Verify behavior when clients increase, when a namespace contains many small files and while the system is rebuilding or servicing another workload.
Block storage pools
Block volumes can provide predictable, provisioned performance for defined compute roles. Confirm whether the design permits the sharing pattern your framework needs, how snapshots affect performance and how a detached or failed volume is recovered.
Local NVMe or SSD
Local NVMe/SSD can deliver very low latency for scratch, staging and cache. Microsoft’s Azure guidance identifies local NVMe/SSD filesystems as an option when performance is the top priority. This tier is commonly ephemeral: validate enterprise interface and form factor, endurance rating, firmware and server support, and decide what happens when a node is replaced. Do not place the only copy of a dataset or checkpoint on disposable local media.
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HDD or hybrid storage can be economical for archives and cold data when retrieval latency is acceptable. Keep frequently reread training data and latency-sensitive model artifacts on a faster tier, and measure whether cache misses create unacceptable stalls.
Design a layered data lifecycle
A practical architecture often keeps active data close to compute in a parallel filesystem, local cache or high-performance block tier; stores the authoritative large dataset in object storage; and moves completed, inactive or historical data to a lower-cost tier. The exact arrangement depends on retention and recovery objectives, but the responsibilities should be explicit.
- Ingest and validate: Land source data in the authoritative store, verify checksums and record version or lineage metadata.
- Stage active data: Copy or cache the subset needed for current jobs near the compute clients. Account for transfer time before the first epoch.
- Run and checkpoint: Write checkpoints to a path that meets the pause and recovery targets. Keep temporary shuffle and scratch data separate from durable artifacts.
- Promote results: Copy final models, logs and metrics to durable storage with the required access controls and retention policy.
- Expire or archive: Apply documented lifecycle rules to old checkpoints and intermediate data. Verify that deletion and versioning do not leave unexpected retained copies.
When using multiple systems, define which copy is authoritative and how workers obtain a consistent view. Azure guidance recommends consistency between Managed Lustre and Blob Storage for distributed jobs and describes transferring inactive data to Blob tiers. Test that synchronization behavior under concurrent reads and writes rather than assuming that a completed copy is immediately visible everywhere.
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Make checkpoint performance a first-class requirement
For each training job, specify checkpoint size, frequency, maximum pause, maximum data loss after a failure and restoration target. Then measure:
- Time from checkpoint request to a durable completion signal.
- Whether workers can continue computing while data is copied.
- Read throughput and total time to restore the complete job state.
- Behavior when a checkpoint overlaps with another job or a degraded storage component.
- Space consumed by retained versions, partial files and failed attempts.
Synchronous writes are simple to reason about but can stop training. Asynchronous or tiered checkpointing can reduce pauses by first writing to a fast local or shared tier and later persisting to object storage, but it changes failure semantics. Test the actual framework, staging path and recovery procedure; an asynchronous design is only useful if the recovery objective still holds.
Verify reliability, availability and degraded operation
Ask the supplier to explain data protection, replication or erasure coding, rebuild traffic, upgrade paths and performance during degraded operation. Simulate a failed disk, storage node, network interruption and planned maintenance. Confirm that the resulting recovery time and data-loss window fit your stated targets.
Durability and availability can conflict with latency, capacity efficiency and power consumption. Meta’s July 1, 2026 engineering account of its internal BLOB and Tectonic evolution illustrates those trade-offs in a specific first-party environment; it should be read as a case study, not as a universal benchmark or design prescription.
Document backup scope separately from replication. Replication can preserve availability while a logical deletion, corrupted file or bad training output is propagated. Versioning, immutable retention and tested restore procedures address different failure modes.
Check compatibility before committing to a platform
Confirm support for the APIs, filesystem semantics and client libraries used by your training framework, orchestrator and data pipelines. Check identity and access integration, encryption, tenant isolation, quota behavior and audit logging. For hybrid or cloud migration, run the same representative job in every target environment, including data transfer and staging time.
NVIDIA notes that equivalent GPU horsepower does not ensure equivalent completion time when storage classes differ. Compare end-to-end job duration, GPU utilization and checkpoint recovery, not just the compute instance specification. Include locality, egress or transfer charges, API request costs and the effect of data being in another region.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Calculate total cost, not media price
The least expensive byte can be the most expensive choice if it extends every epoch or requires a large cache. Build a total-cost model that includes:
- Usable capacity after protection overhead, spare capacity and reserved growth.
- Performance reservations, cache devices, network links and client or protocol fees.
- Object requests, retrieval, inter-region transfer and egress where applicable.
- Power and cooling for flash, servers and networking.
- Backups, replicas, snapshots, version retention and disaster-recovery capacity.
- Software licensing, support, monitoring, operations and replacement hardware.
- Productivity cost from GPU idle time, longer checkpoints and slower recovery.
Buy the performance the measured workload needs rather than the maximum available. Flash, hybrid, HDD and cloud tiers can all be appropriate when their latency and access frequency match the data lifecycle.
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Run a representative acceptance benchmark
Do not accept a vendor’s peak figure as an application result. Use a benchmark that resembles production:
- Prepare the data: Use representative file or object sizes, directory structure, compression and version layout. Include both hot and cold datasets.
- Match the topology: Use the target network, client software, mount or API path, GPU count, worker count and placement.
- Exercise concurrency: Run the expected number of readers and writers, then add overlapping preparation, inference or checkpoint traffic.
- Measure the job: Record throughput, IOPS, average and p95/p99 latency, metadata rates, cache hits, queue depth, GPU idle time and data-loader stalls.
- Test checkpoints: Measure synchronous pause, asynchronous staging, durable completion and full restoration.
- Repeat failure cases: Evict caches, interrupt a client, remove a storage component if safe, and measure degraded performance and recovery.
- Model growth: Repeat with projected capacity, more clients and a parallel job. Check that performance remains within the service objective.
Publish the test conditions with the result: dataset layout, cache state, concurrency, software versions, network, protection scheme and whether the number is sustained or a short burst.
Common buying mistakes and their fixes
Choosing by capacity alone
Symptom: The system has ample free space but GPUs regularly stall. Fix: Measure read bandwidth, tail latency, metadata and cache misses at full client concurrency; add a faster active tier or change data layout if those measurements identify the bottleneck.
Comparing only sequential throughput
Symptom: A benchmark looks excellent, but small-file preparation or model loading is slow. Fix: Add random I/O, metadata and cold-cache tests with production-like file sizes.
Ignoring checkpoint restoration
Symptom: Saving appears acceptable, but a failed job takes hours to resume. Fix: Set a restoration target and benchmark the complete read path, including object retrieval, mount caching and framework initialization.
Putting the only copy on local scratch
Symptom: A node replacement deletes the dataset or checkpoint. Fix: Treat local NVMe/SSD as disposable unless the architecture replicates it, and automate promotion to durable storage.
Assuming API compatibility means identical behavior
Symptom: An object-backed filesystem produces locking, listing or consistency surprises. Fix: Test application semantics, visibility delays, rename behavior, cache invalidation and parallel access with the real adapter.
Under-sizing for rebuilds and growth
Symptom: A component failure consumes all spare bandwidth and training slows during repair. Fix: Include degraded-mode benchmarks, spare capacity, rebuild traffic and future datasets in the capacity and performance model.
A practical decision sequence
- Classify each job as training, fine-tuning, inference, preparation, scratch or retention.
- Set measurable targets for throughput, p95/p99 latency, metadata rate, checkpoint pause and restore time.
- Choose where authoritative, active, temporary and archived data will live.
- Shortlist object, parallel-file, block, local-flash and hybrid tiers against their required semantics.
- Run the same end-to-end benchmark on each viable design, including cold-cache and degraded cases.
- Price usable capacity, protection, data movement, power, operations and GPU idle time.
- Approve only a design whose measured result meets today’s objective and remains inside the growth and recovery plan.
Bottom line
Buy storage for the I/O behavior your AI jobs actually produce. A layered design usually works best: fast, concurrent active storage near compute; scalable object storage for authoritative and retained data; and a checkpoint path engineered around both save pauses and restoration time. Validate bandwidth, tail latency, metadata, consistency, failure behavior and total cost in a representative end-to-end benchmark before committing to a platform.
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