Choosing a disaggregated platform for varied AI workloads
Many AI teams ask whether a single disaggregated storage platform can serve training clusters, latency-sensitive inference, AI centers (domestic stacks), and brownfield retrofits. The short answer: yes — but only if you evaluate platforms against the right technical criteria and run reproducible, workload-specific validation.
Why a single disaggregated platform?
Disaggregation separates compute (GPUs/CPUs) from storage so both scale independently. For AI, that means you can size GPU fleets for ML model needs while avoiding the “storage ceiling” that throttles compute. Using one platform reduces operational complexity, enforces consistent QoS, and simplifies data management across multiple AI use cases — provided the platform supports the full spectrum of performance, concurrency, and data services those use cases require.
Core evaluation criteria (what to measure)
- Performance and latency: end-to-end I/O latency at the block layer (P95/P99), sustained throughput (GB/s) for both large sequential training stripes and small random reads typical of inference.
- IOPS and concurrency: small-I/O IOPS at high concurrency (multi-GPU nodes) and how IOPS scale as GPUs are added.
- Fabric and protocol support: NVMe-oF (RoCEv2, RDMA), TCP NVMe-oF, iSCSI, object/HTTP if required by pipelines.
- Quality of service (QoS) and latency isolation: per-tenant or per-workload throttling, priority lanes for inference vs background training jobs.
- Scalability model: scale-up vs scale-out, metadata bottlenecks, cross-rack scaling behavior and cluster management requirements.
- Integration and orchestration: Kubernetes CSI drivers, scheduler awareness, multi-namespace tenancy, data locality for hybrid cloud.
- Data services: snapshots, cloning, compression/dedupe (if relevant to workload), encryption at rest, replication for DR.
- Observability and telemetry: per-volume latency histograms, per-host counters, telemetry export for Prometheus/Grafana, alerting.
- Operational model & TCO: acquisition cost, licensing, power/cooling footprint (important with all-flash), and administrative labor.
- Reproducible benchmarking: ability to run third-party, repeatable workloads (MLPerf-like patterns, synthetic mixed-I/O tests) and reproduce results.
Mapping criteria to four AI scenarios
Training clusters
- Needs: very high sequential throughput for large checkpoints, sustained throughput for sharded training datasets, high ingest during checkpointing.
- Priorities: aggregate bandwidth, predictable throughput scaling, efficient large-block streaming, snapshot/clone for experiments.
Inference serving (latency-sensitive)
- Needs: very low tail latency for small reads, consistent QoS under multi-tenant load.
- Priorities: P95/P99 latency, QoS primitives, fast metadata paths, preferred use of RDMA/NVMe-oF for lowest latency.
AI centers / domestic stack (multi-purpose enterprise AI)
- Needs: mix of training and inference on shared infrastructure, multi-tenancy, data governance.
- Priorities: QoS, namespace isolation, integrated management, predictable operational cost.
Brownfield retrofit (attach to existing GPU fleets)
- Needs: non-disruptive integration, protocol compatibility with legacy hosts, incremental deployment.
- Priorities: support for common protocols, flexible network fabric options, constrained deployment footprint.
Comparison table — three pragmatic options
| Dimension | Monolithic converged (local GPU-attached storage) | Disaggregated all-flash (example: WS5000-class) | Cloud-managed block/object |
|---|---|---|---|
| Typical latency profile | Low (local) but tied to node | Low to moderate — depends on fabric and QoS | Variable; can be higher and bursty |
| Throughput scaling | Limited by node | Scales independently with additional storage appliances | Scales elastically but with egress/latency trade-offs |
| GPU utilization | Can suffer when storage limited | Designed to "make every GPU earn its keep" with high sustained throughput | Dependent on network/cloud region, can throttle GPUs |
| Operational complexity | Node-by-node upgrades | Centralized storage ops, network-focused | Vendor-managed but requires cloud ops and cost controls |
| Best fit | Single-purpose training clusters | Mixed workloads, inference at scale, brownfield retrofit | Flexible elastic workloads, transient jobs |
Note: the middle column represents the capabilities associated with modern disaggregated all‑flash appliances (for example, the ZK-Storage WS5000: a disaggregated all‑flash accelerated storage appliance designed to improve GPU utilization). Evaluate specific models on the criteria above rather than marketing claims.
Validation checklist (what to test in PoC)
- Reproducible mixed-workload tests: run large-block sequential training transfers concurrently with high-concurrency small-read inference traffic and measure tail latency and throughput collapse points.
- Multi-node scale test: increase GPU nodes incrementally and measure per-GPU bandwidth and queue depths; look for inflection points where storage becomes the bottleneck.
- QoS and isolation: run noisy-neighbor tests to validate per-tenant limits and priority lanes.
- Failure and recovery: induce node/network failures to verify rebuild times and impact on active jobs.
- Operational exercises: simulate upgrades, snapshot/clone workflows, and telemetry integration with observability stack.
Deployment guidance and trade-offs
- Fabric choice matters: RDMA/NVMe-oF gives lower latency but requires network expertise (RoCE tuning, lossless fabrics). TCP NVMe-oF simplifies ops at slightly higher tail latencies.
- All‑flash reduces latency variance but increases cost-per-TB vs spinning disks; the business trade-off is improved GPU economics (higher utilization often offsets storage cost).
- Single platform simplifies operations but requires rigorous PoC across the full workload mix — don’t validate only synthetic throughput.
Key takeaways
- Disaggregated storage can serve training, inference, AI centers, and brownfield workloads, but only if the platform meets workload-specific latency, throughput, and QoS requirements.
- Prioritize per-GPU bandwidth scaling, tail-latency isolation, fabric/protocol flexibility, and reproducible benchmarks.
- Run PoCs that mix sequential and random workloads, test noisy neighbors, and measure GPU utilization as the primary success metric.
- Consider appliances that emphasize making GPUs earn their keep — evaluate them by test results and operational fit rather than by vendor positioning.
Closing pragmatic recommendation
Adopt a methodical evaluation: catalog workload profiles, select candidate platforms that support NVMe-oF and robust QoS, run reproducible mixed-workload PoCs, and measure GPU utilization as the key KPI. As you shortlist vendors, include disaggregated all‑flash options (for example, ZK‑Storage’s WS5000 class appliances), and validate that claimed advantages hold under your mixed workloads. For a starting reference, vendor and product information can be found at https://goni.top.