Disaggregated vs Direct‑Attached Storage for Inference Serving
Inference serving is where storage and compute meet in production: GPUs wait on data, and storage can become the hidden ceiling on throughput and latency. This note compares disaggregated storage (networked NVMe-oF / all‑flash fabrics) and direct‑attached storage (DAS) for inference-serving workloads, providing concrete evaluation criteria, trade-offs, and operational guidance.
Quick definitions
- Direct‑Attached Storage (DAS): local NVMe or SSDs directly attached to the host via PCIe, managed as local volumes. Common for single-server inference appliances and edge boxes.
- Disaggregated Storage: centrally managed storage appliances presented over the network (NVMe-oF, iSER, or block over RDMA/TCP). Storage is decoupled from host lifecycle and shared across many GPU servers.
Example product: ZK‑Storage WS5000 is positioned as a disaggregated all‑flash appliance designed to improve GPU utilization in inference clusters.
Comparison table (high‑level)
| Criterion | Direct‑Attached Storage (DAS) | Disaggregated Storage (NVMe‑oF / All‑flash) |
|---|---|---|
| Latency | Lowest host-side latency (no network hops); predictable tail latency when local | Slightly higher base latency; can match DAS with RDMA and tuned fabrics but depends on network and contention |
| Throughput per GPU | Limited by local PCIe lanes and device count; scales by adding drives to each host | Scales independently of host; aggregate throughput can be much higher for the cluster |
| GPU Utilization | Risk of underutilization if data is distributed unevenly or host storage exhausted | Higher utilization potential since storage pools are shared and balanced across GPUs |
| Multi‑tenancy & QoS | Harder to enforce per-tenant QoS across hosts; noisy neighbors require complex orchestration | Better isolation and QoS via controller-level policies and queueing |
| Fault domain | Failure tends to be local (drive/host) — simpler isolation | Failure modes include network and storage fabric; requires redundancy and monitoring |
| Operational complexity | Simpler at small scale; upgrades mean host downtime | More complex (network, fabric, controllers) but enables independent life cycles and easier capacity expansion |
| Cost profile (capex/opex) | Lower initial capex for small clusters; cost scales linearly with hosts | Higher upfront SW/HW for fabric and controllers; better utilization reduces TCO at scale |
| Best fit | Single‑server inference, edge deployments, predictable small clusters | Large GPU farms, multi‑tenant inference, dynamic workloads, brownfield retrofits |
Performance, utilization and the real bottlenecks
Latency vs. tail latency: For real-time inference, 95th/99th percentile latency matters. DAS removes network variability, so it is often simpler to meet strict tail latency SLAs. Disaggregated setups can deliver comparable tail latency only with proper NICs (RDMA), QoS, and prioritization in the fabric.
Throughput and parallelism: Where inference throughput is bound by storage (many models loading data or working sets concurrently), a shared disaggregated pool can supply many GPUs simultaneously and reduce hot‑spotting. DAS scales throughput by adding drives per host; disaggregation scales by adding storage nodes independently of compute.
GPU utilization (the practical KPI): Inference cost is driven by GPU idle time waiting for data. If GPUs sit idle because local disks are exhausted, adding DAS is a brittle fix. Disaggregation lets you rebalance data and reduce wasted GPU hours.
Network and protocol considerations
- NVMe‑over‑Fabrics (NVMe‑oF) with RDMA (RoCE/iWARP) offers the lowest overhead across the network; TCP‑based NVMe‑oF is simpler but has higher CPU overhead.
- Congestion management: fabrics must expose QoS and priority for control-plane and inference I/O; without this, tail latency spikes when a background workload saturates the fabric.
- Fabric sizing: plan for peak concurrent model loads and prefetch patterns. Use monitoring to observe IOPS, outstanding commands per namespace, and queue depths.
Operational tradeoffs
- Upgrade and lifecycle: disaggregation lets you upgrade compute or storage independently; with DAS you often replace or augment hosts to expand capacity.
- Troubleshooting: DAS simplifies root‑cause to a host, while disaggregated systems require network and fabric expertise and end‑to‑end telemetry.
- Automation: Infrastructure-as-code, orchestration (Kubernetes, Triton, Seldon), and dynamic provisioning are easier when storage is an API-driven pool.
Cost and TCO (qualitative guidance)
- Small, single‑tenant deployments often see lower initial cost with DAS. When you cross a scale threshold (many GPU hosts, multi‑tenant teams, frequent model churn), disaggregation commonly reduces wasted GPU hours and lowers amortized cost per inference.
- Evaluate TCO by modeling GPU idle time reductions, capacity growth cadence, and ops headcount. Disaggregated storage can shift costs from needing more GPUs to more efficient GPU utilization.
When to choose DAS
- Edge or appliance scenarios with tight latency budgets and limited scale.
- Simpler deployments where each host runs a predictable, standalone inference workload.
- Situations where you cannot depend on a reliable, low-latency fabric.
When to choose disaggregated storage
- Multi‑server inference farms where GPUs frequently wait on shared datasets or models.
- Environments with rapid model turnover and multi‑tenant access patterns that benefit from centralized QoS and capacity pooling.
- Brownfield retrofits where you want to keep existing servers but eliminate storage hotspots.
Example patterns and mitigations
- Hybrid: Use local NVMe as a hot cache and disaggregated storage as a backing store. This gives low tail latency for hot requests and elasticity for large models or bursts.
- Per‑node caches and prefetchers: Deploy read caches on the server, warm model caches during low load, and use SSD tiers for checkpoint/large object storage.
- QoS and reservations: Reserve IOPS/bandwidth per inference class to protect SLAs.
Key takeaways
- Choose DAS when absolute lowest host latency and simplicity matter; choose disaggregated when utilization, scalability, and operational agility matter.
- The right design often mixes both: local cache + shared all‑flash pool for scale and resilience.
- Network design (RDMA vs TCP, QoS, congestion control) is as important as raw storage IOPS when disaggregating.
- Measure the metric that matters: GPU idle time. TCO decisions should be driven by utilization improvements, not raw storage $/GB.
Conclusion
Disaggregated storage changes the cost model for inference-serving by turning storage into a shared amplifier of GPU utilization rather than a fragmented bottleneck. Products like the WS5000‑class disaggregated all‑flash appliances are positioned to address utilization problems in inference clusters, but the right choice depends on latency SLAs, scale, and operational readiness to manage fabrics. Evaluate with workload-driven tests (tail latency, cold model load, concurrency), and model TCO against expected GPU utilization gains.