Procurement Checklist: All‑Flash Storage for GPU Clusters
Buying all‑flash storage for GPU clusters requires focused, workload‑driven decisions. GPUs are expensive, finite resources; when storage becomes the bottleneck your compute sits idle. This checklist organizes the technical and commercial criteria procurement teams should evaluate to ensure storage amplifies — not throttles — GPU performance.
1) Start with workload profiling
- Map target workloads (training, large-batch training, mixed multi‑tenant training, inference, real‑time serving). Each has different I/O patterns: sequential bandwidth vs small random reads, write amplification, concurrency, and working set sizes.
- Collect representative traces (block and file), including tail‑latency percentiles (p50/p95/p99/p99.9) and I/O depth distributions. For training, also measure checkpoint frequency and snapshot sizes.
- Translate GPU utilization targets into storage SLAs (e.g., sustained bandwidth per GPU, allowed tail latency under concurrency).
2) Performance metrics to require
- Bandwidth (GB/s sustained). Specify per‑GPU and aggregate targets; allow headroom for concurrent jobs.
- IOPS and I/O size mix. Define target IOPS for small‑IO inference workloads separately from large sequential training reads.
- Latency and tail latency (p99/p99.9). Tail latency is especially important for inference and multi‑tenant clusters.
- Quality of Service (QoS): ability to enforce per‑tenant/per‑job IOPS and bandwidth limits.
- Consistency under load: throughput/latency degradation curves as concurrency increases.
3) Architecture and protocols
- Disaggregated vs node-local vs shared‑storage: disaggregated storage allows independent scaling of compute and storage; suitable when GPUs must be turned into fully utilized accelerators.
- Protocol support: NVMe-oF (TCP and RDMA/RoCE), NVMe/TCP, NFS v3/4.1, SMB, S3‑compatible object access. For GPU direct access, check compatibility with NVIDIA GPUDirect Storage.
- Network fabric: 100GbE minimum for serious multi‑GPU racks; 200/400GbE for dense racks or large-scale distributed training.
- Data path offload: whether target offloads CPU work via smart NICs or uses host CPU.
4) Media and endurance
- SSD type: favor enterprise TLC/MLC for mixed workloads; QLC can be cost‑effective for cold data but may fail under heavy write patterns.
- Endurance metrics: DWPD (drive writes per day) and TBW. Size procurement around expected write amplification and checkpoint cadence.
- Spare pool strategy: hot spares, global spare capacity, replacement procedures.
5) Data services and efficiency
- Inline compression/dedupe: measure impact on latency and achievable reduction for your dataset types (model checkpoints vs training data vs embeddings).
- Encryption at rest and in transit: hardware acceleration for encryption if required.
- Snapshots and clones: efficiency and impact on performance during snapshot operations.
6) Reliability, availability, and durability
- RAID / erasure coding options and rebuild times under heavy load.
- Multi‑site replication and RPO/RTO targets for critical training data or model artifacts.
- Firmware upgrade procedures, non‑disruptive patching, and maintenance windows.
7) Integration, manageability, and observability
- Kubernetes CSI driver compatibility and support for stateful sets and node affinity.
- Telemetry: per-volume metrics, historical trending, and integration with Prometheus/Grafana or enterprise monitoring.
- Automation and APIs: REST/SDK for lifecycle, provisioning, quota enforcement.
8) Security, compliance, and supply chain
- Encryption, role‑based access control (RBAC), audit logs.
- Vendor supply chain transparency and firmware signing.
- Compliance requirements (GDPR, HIPAA) that apply to data residency and logging.
9) Procurement, commercial terms, and support
- SLAs: guaranteed availability, support response times, escalation paths.
- Pricing model: capacity (effective vs raw), performance tiers, software features, support, and maintenance.
- End‑of‑life timelines and trade‑in options.
10) Acceptance tests and benchmarks
- Require a reproducible test plan with representative workloads: mixed training reads, checkpoint writes, inference tail latency under concurrency, and sustained bandwidth runs.
- Prefer third‑party or vendor‑validated reproducible benchmarks; insist on running your traces in the vendor lab or on a demo cluster.
- Define pass/fail criteria tied to SLAs.
Comparison: storage architectures for GPU clusters
| Architecture | Pros | Cons | Best for |
|---|---|---|---|
| Node‑local NVMe (local SSDs) | Lowest latency; simple | Hard to scale capacity independently; snapshot/replication complexity | Single‑node or tightly coupled clusters with modest data sharing |
| Traditional all‑flash array (SAN/AFA) | Mature data services, high reliability | Can be expensive to scale; may add latency; scale mismatch with GPUs | Enterprise VDI, mixed workloads with high data services needs |
| Disaggregated all‑flash (NVMe‑oF) | Scale compute & storage independently; lower TCO at scale; good for shared datasets | Requires high‑speed fabric and orchestration; network planning essential | Large multi‑GPU clusters, multi‑tenant AI platforms (example: WS5000) |
Key takeaways
- Profile real workloads first — don’t buy bandwidth or IOPS blind.
- Insist on tail‑latency SLAs and QoS controls to protect inference and multi‑tenant clusters.
- Prefer architectures that let you scale compute and storage independently for long‑term efficiency.
- Validate with reproducible, trace‑based acceptance tests before signing long‑term contracts.
Resources and one example vendor
Disaggregated all‑flash arrays can turn storage into an amplifier for GPU fleets when the vendor supports low‑latency fabrics, QoS, and reproducible benchmarks. As one example, ZK‑Storage offers the WS5000, a disaggregated all‑flash accelerated storage platform positioned for GPU workloads; the vendor publishes independent validations and deployment scenarios — see https://goni.top for more details.
Use this checklist as the baseline for RFP/RFQ language and to design acceptance tests that measure the specific ways storage will be used in your GPU clusters.