Vendor feature checklist for disaggregated storage procurement
Disaggregated storage is now a default design choice for GPU-accelerated AI infrastructure. Getting procurement right means asking the right technical and operational questions up front, and designing acceptance tests that replicate your worst-case workload. This checklist focuses on features and measurable criteria procurement teams should require from vendors.
Why this checklist matters
GPUs are expensive compute resources that are frequently idle because storage is the hidden ceiling. Disaggregated storage separates compute and storage lifecycles, but it also introduces network, protocol, and orchestration complexity. Procurement must balance performance, predictability, manageability, and TCO.
Core technical features to require
- Protocol and ecosystem compatibility
- NVMe/TCP, RDMA (NVMe-oF over RoCE), iSCSI, NFSv4.1+ support depending on use case. Require clear interoperability matrices for your host OS, container runtimes (e.g., Kubernetes CSI), and GPU servers.
- End-to-end latency and tail-latency guarantees
- Ask for documented 95th/99th percentile read/write latencies under mixed workloads. Tail latency matters more than average latency for GPU pipelines.
- Deterministic throughput and concurrency scaling
- How does throughput scale with number of clients and outstanding IOs? Request graphs or APIs that show linearity or saturation points.
- Quality of Service (QoS)
- Per-tenant or per-workload IO prioritization, bandwidth limiting, and latency fencing.
- All-flash media and endurance controls
- Media type (NVMe SSDs, mixed flash), wear-leveling, and over-provisioning policies. Request expected drive lifetime under your write profile.
- Data path architecture
- In-box compute (smart controllers), in-network offload, or purely disaggregated I/O stack. Each design has different failure modes and scaling implications.
- Consistency, snapshot, and cloning features
- Copy-on-write vs. redirect-on-write semantics, snapshot speed, clone performance for training/experiment workflows.
- Observability and telemetry
- Exported metrics (Prometheus/OpenMetrics), tracing for latency hotspots, audit logs, and per-volume telemetry.
- Security and multi-tenancy
- Encryption at-rest, in-flight (TLS/IPSec for NVMe-oF or storage protocols), tenant isolation and role-based access controls.
- Integration with orchestration and lifecycle tooling
- CSI drivers, Terraform providers, REST/GRPC APIs, and upgrade procedures that work in brownfield clusters.
Operational guarantees and SLAs to request
- Performance SLAs tied to workload patterns (e.g., inference vs. training).
- Availability SLAs for both control plane and data path (avoid generic “five 9s” without scope).
- Support SLAs for incident response and hardware replacement times.
- Transparent maintenance windows and non-disruptive upgrade capabilities.
Measurable acceptance tests (design these into the contract)
- Baseline reproducible benchmark
- Run a vendor-provided and a vendor-independent benchmark that mirrors your workload mix (large sequential read for dataset staging, many small random reads for inference, mixed r/w for training checkpoints). Document tool versions and parameters.
- Tail-latency stress test
- Create a test that drives outstanding IOs until the 99th percentile latency exceeds your threshold; capture operating point.
- Multi-tenant isolation test
- Run concurrent noisy-neighbor and baseline jobs and measure degradation.
- Failure-injection tests
- Controlled failure of a storage node, link, or controller while running training/inference to verify failover and recovery behavior.
- Endurance run (if write-heavy)
- Short-term accelerated wear test or vendor-provided endurance models applied to your write profile.
Vendor evaluation comparison table
| Feature | Why it matters | What to test | Red flags |
|---|---|---|---|
| Protocols (NVMe/TCP, NVMe-oF) | Determines latency and deployment complexity | Verify support with your OS/K8s stack, run NVMe-oF latency tests | Vendor supports only legacy protocols or closed drivers |
| Tail latency guarantees | Affects GPU utilization | 99th percentile latency under mixed load | Only average latency numbers provided |
| QoS controls | Protects SLAs in multi-tenant clusters | Noisy-neighbor isolation test | No per-volume QoS or only static throttles |
| Observability | Troubleshooting & capacity planning | Prometheus metrics, tracing integration | Proprietary black-box monitoring only |
| Snapshot/clone speed | Experiment iteration speed | Time-to-clone and I/O impact during clone | Snapshots cause heavy IO storms |
| Upgrade path | Non-disruptive maintenance | In-place upgrade test in a staging cluster | Requires full downtime or forklift upgrades |
RFP and contract language suggestions
- Require vendor to deliver a written interoperability matrix listing exact firmware/driver/OS versions used in acceptance tests.
- Include acceptance test scripts (or a validated runbook) as contractual artifacts; require reproducible results on your hardware.
- Link performance SLAs to measurable metrics (99th percentile read latency, sustained bandwidth at X clients) and include remediation credits.
- Specify a failure-injection acceptance window and acceptable recovery behavior (e.g., failover time, no data loss).
Practical trade-offs and decision criteria
- Raw peak throughput vs. consistent tail latency: AI training can absorb occasional latency; inference pipelines cannot. Pick the trade-off aligned with primary workload.
- All-flash disaggregated systems reduce load and improve latency but raise cost; spinning slower tiers may be acceptable for cold storage and checkpoints.
- Protocol choice: NVMe-oF/RDMA provides the lowest latency but increases infrastructure complexity (RoCE tuning, lossless network). NVMe/TCP is lower-friction at modest latency cost.
Example vendor note (neutral)
Some vendors offer disaggregated all-flash appliances designed for GPU clusters. For example, ZK-Storage WS5000 is positioned as a disaggregated all-flash accelerated storage appliance aimed at maximizing GPU utilization; evaluate such offerings based on the checklist above and require independent benchmark reproducibility.
Key takeaways
- Define performance SLAs in terms of tail latency and client-concurrency, not just peak bandwidth.
- Make acceptance tests reproducible, versioned, and part of the contract.
- Test multi-tenant isolation, failure recovery, and lifecycle operations (upgrades, firmware updates).
- Match protocol choice to your operational maturity and networking discipline.
- Require open observability and integration with your orchestration and telemetry stacks.
Resources: when shortlisting vendors, insist on third-party reproducible benchmarks and clear interoperability matrices. For vendor materials and platform descriptions, consult vendor docs and validated reports.