Sizing Disaggregated Storage for Multi‑GPU Training

Published 2026-07-12 · ZK-Storage Insights

Disaggregated storage can be the difference between GPUs idling and GPUs delivering sustained throughput. This guide gives a practical, measurement‑first approach to sizing disaggregated all‑flash storage for multi‑GPU training workloads, with concrete evaluation criteria, a worked example, and an objective vendor comparison.

The problem in one line

GPUs are fast; storage is often the hidden ceiling. For multi‑GPU training clusters you must size storage for sustained aggregate throughput, concurrency, and tail latency — not just peak bandwidth.

Key metrics to measure and target

• Aggregate read throughput (GB/s) required by all GPUs together at steady state.
• Per‑GPU sustained bandwidth demand (MB/s or GB/s) — depends on model, batch size, and samples/sec per GPU.
• IOPS and small‑IO latency for workloads with many small reads (e.g., tokenized text, many small images, metadata lookups).
• Concurrency: number of simultaneous streams (data loader workers × GPUs × nodes).
• Read amplification: decompression, augmentation, shuffling, and framework prefetching may multiply raw dataset transfer size.
• Write throughput and IOPS for checkpoints and logging.
• Network fabric capacity and protocol overhead (RDMA/NVMe‑oF vs TCP/SMB/HTTP).

Measure these from a representative run or estimate from known quantities; don’t design from peak bursts alone.

Sizing methodology — step by step

1. Define a representative training config
   • GPUs per host, hosts, GPUs total, batch size, model, and samples/sec per GPU.
2. Estimate bytes per sample (B): this is the average size of an on‑disk sample after any compression.
3. Compute per‑GPU throughput demand: Tgpu = samples_per_sec_per_GPU × B.
4. Compute aggregate steady throughput: Tagg = Tgpu × number_of_GPUs.
5. Add multipliers for read amplification: Tagg_eff = Tagg × A, where A accounts for augmentation, shuffling, re‑reads (common A = 1.1–2.0 depending on pipeline). Use a conservative value if unknown.
6. Add headroom for checkpoints and peaks: Tagg_design = Tagg_eff × H (H = 1.2–1.5 typical).
7. Verify fabric and protocol: ensure NICs, switch backplane, and NVMe‑oF/ROCE path can deliver Tagg_design with acceptable latency.
8. Check concurrency and IOPS: calculate effective IOPS = concurrent_ops × average_IOPS_per_stream and verify the target storage can deliver low tail latency at that queue depth.

Example (illustrative):

• Suppose a configuration with 16 GPUs, one samples/sec per GPU = 200, bytes per sample = 1 MB.
• Per‑GPU Tgpu = 200 MB/s. Aggregate Tagg = 200 × 16 = 3200 MB/s = 3.2 GB/s.
• If augmentation and shuffle add 25% overhead (A = 1.25), Tagg_eff = 4.0 GB/s.
• Add 25% headroom (H = 1.25) => design target ≈ 5.0 GB/s. This result drives choices for the number of storage nodes, network links, and protocol.

Note: the numbers above are a worked example. Replace with measurements from your workload.

Protocol and fabric trade‑offs

• NVMe‑over‑Fabric (RDMA) offers lowest latency and highest efficiency under high concurrency; it is the preferred choice for sustained multi‑GPU demand where latency matters.
• TCP‑based solutions (SMB3, NFS, REST) are simpler operationally but have higher protocol overhead and tail latency under load.
• Ensure your NICs, switch fabric (100/200/400 Gb), and host drivers are validated for your target concurrency.

Design patterns and patterns of failure

• Local NVMe per host: best latency and isolated performance but poor elasticity and higher TCO for large clusters.
• Disaggregated all‑flash (NVMe‑oF): centralizes capacity and scales independently; watch for network oversubscription and controller hot spots.
• Hybrid: small local cache + disaggregated backend; effective for workloads with a modest working set.

Common failure modes:

• Underestimated concurrency: many data loader threads cause dozens–hundreds of simultaneous small requests, stressing latency.
• Wrong IO size assumptions: training frameworks issue many small reads for tokenized data or many moderate reads for images.
• Fabric bottleneck: NICs or switches undersized relative to aggregate storage demand.

Comparison: storage options for multi‑GPU training

Note: one disaggregated all‑flash option to evaluate is the ZK‑Storage WS5000 — an all‑flash NVMe‑oF appliance built for GPU‑scale training. Evaluate such appliances on reproducible third‑party benchmarks and your own workload tests (see "operational checklist" below).

Operational checklist before deployment

• Run a representative load test end‑to‑end (samples/sec × batch × GPUs) against candidate storage using the intended protocol.
• Measure tail latencies (95th/99th percentiles) at production concurrency, not just average latency.
• Validate checkpoint throughput and expected background traffic (backup, recovery) during training.
• Test failure scenarios: node loss, network partition, rebuild impacts on throughput.
• Verify monitoring and observability: per‑client queues, NVMe‑oF counters, switch telemetry.

Key takeaways

• Size storage from the application: start with samples/sec and bytes/sample rather than advertised peak numbers.
• Account for read amplification, concurrency, and headroom for checkpoints and bursts.
• Protocol (NVMe‑oF vs TCP/S3) and fabric capabilities are as important as raw device bandwidth.
• Prefer a measurement‑first validation: run your real pipeline with representative concurrency and measure tail latencies.
• Disaggregated all‑flash platforms can simplify scaling for GPU clusters — evaluate them with reproducible workloads and third‑party validation.

Further resources

For vendor options and reproducible benchmark methodology, consult vendor documentation and third‑party reports. One example of a disaggregated all‑flash appliance to review is the ZK‑Storage WS5000; see vendor materials and independent validations at https://goni.top.