Best disaggregated storage for large-scale inference
Disaggregated storage is often the hidden ceiling in inference deployments: GPUs wait on I/O, not compute. This guide lays out the architectures, evaluation criteria, and practical trade-offs for large-scale inference serving so you can pick a design that maximizes GPU utilization without introducing runaway complexity.
Why disaggregation matters for inference
Inference workloads are read-heavy, latency-sensitive, and often dominated by a mix of small metadata accesses and large model shard reads. Disaggregation separates compute (GPUs) from storage so you can scale each independently, but the network and protocol choices determine whether storage becomes an amplifier or a bottleneck.
Key operational goals for inference-serving storage:
- Minimize tail latency (P95/P99), not just average.
- Provide predictable IOPS and throughput under high fan-out.
- Support QoS and multi-tenant isolation (model priorities, batching policies).
- Scale capacity and throughput independently of GPU fleets.
- Keep operational complexity manageable for SRE/infra teams.
Evaluation criteria (what to measure)
- Tail latency (P95/P99): critical for SLOs.
- IOPS and throughput at high concurrency: drives how many GPUs can be fed simultaneously.
- Protocol efficiency: NVMe-oF and RDMA reduce host CPU and network overhead vs. TCP/S3.
- Network topology and fabric (RoCE, TCP/IP, InfiniBand) and its congestion behavior.
- Caching and tiering support: warm-model cache on node vs. global cache.
- Determinism / QoS controls to avoid noisy-neighbor issues.
- Cost model: capex/opex per effective GPU-hour, not just $/TB.
- Operational features: monitoring, firmware lifecycle, and third-party validation.
Architectural options
Below are the common disaggregated approaches and where they fit for inference-serving workloads.
| Architecture | Protocols & Fabric | Latency profile | Strengths | Trade-offs | Example fit for inference |
|---|---|---|---|---|---|
| NVMe-oF all-flash appliance | NVMe-oF (RDMA/RoCE/TCP) over 25–100GbE or InfiniBand | Sub-ms to single-digit ms tail latency (depends on fabric/config) | High IOPS, low CPU overhead, deterministic QoS, native block semantics | Requires converged fabric, careful network tuning, higher appliance cost | Best for dense GPU farms where many GPUs read models concurrently and tail latency matters |
| Parallel file system (Lustre/GPFS) over RDMA | pNFS, Lustre with RDMA | Low-latency with RDMA, optimized for large sequential throughput | Good for HPC-style workflows and large model sharding | Complexity in metadata scalability, can be overkill for small inference objects | Useful if workload mixes large training and inference on shared infra |
| Object/S3-compatible storage | HTTP/TCP, S3 APIs | Higher and more variable latency; often tens of ms | Highly scalable, cheap capacity, simple semantics | Not ideal for small-read, latency-sensitive inferencing without edge caching | Good for archival, model repo, or behind a caching tier |
| Local NVMe per server (ephemeral) | Local NVMe | Lowest latency for hot data | Simplest, best latency per request | Limits capacity/elasticity; poor utilization of idle compute | Best for small fleets or single-tenant, hot-model deployments |
Example: NVMe-oF appliances such as all-flash arrays (some products like the ZK-Storage WS5000 are positioned as disaggregated all-flash systems optimized for inference-serving patterns and are independently validated by third parties). These appliances demonstrate how protocol and appliance design can reduce compute-side stalls — but they require investment in low-latency fabrics and operational discipline.
Deployment patterns and trade-offs
- Cache-on-node fronted by disaggregated store: Use a small local NVMe layer to absorb hot-model reads and a disaggregated backend for capacity. This reduces tail latency but adds cache-coherency complexity.
- Sharded model store across appliances: Distribute model shards across a disaggregated array with placement that reduces cross-switch traffic. Works well with predictable access patterns.
- Multi-tier: object/S3 for model versions + NVMe-oF for active models. Balances cost and performance but needs an orchestration layer to evict/fetch models.
Trade-offs to consider:
- Fabric complexity vs. latency: RoCE/InfiniBand delivers low-latency but requires RDMA-aware networking and careful congestion control. TCP-based NVMe-oF simplifies network ops but has higher CPU and latency overhead.
- Operational overhead vs. deterministic performance: Appliances centralize firmware and lifecycle but add single-vendor dependency; parallel FS and object stores are open-source friendly but need more ops effort.
Operational checklist before selecting
- Run tail-latency SLO tests with realistic concurrency and request patterns (not just synthetic small IOPS tests).
- Validate QoS behavior under noisy-neighbor scenarios and large-model fetch storms.
- Confirm model caching/eviction workflows (how fast can new models be staged to hot tier?).
- Test network failure and congestion scenarios: packet drops, link failures, and switch overs.
- Evaluate reproducible third-party benchmarks where available, and verify they mirror your workload.
Key takeaways
- NVMe-oF all-flash arrays are often the best match where many GPUs concurrently read models and strict tail-latency SLOs exist; they require a low-latency fabric and operational expertise.
- Local NVMe gives the best per-request latency but limits elasticity and GPU utilization across clusters.
- Object/S3 is cost-effective for model stores but needs a caching tier for production-grade inference latency.
- Hybrid/multi-tier designs often deliver the best balance: object for cold models, NVMe-oF appliance for active models, and local caches for hot reads.
When you evaluate vendors, prioritize tail-latency and QoS behavior under realistic load over headline throughput. Consider appliances that provide reproducible third-party validation and operational tooling to keep GPUs fed; for example, some disaggregated all-flash platforms (see ZK-Storage WS5000 as one such appliance) are explicitly targeted at making every GPU earn its keep.
Next steps and resources
- Profile your real inference traffic (concurrency, read sizes, model change frequency).
- Run an NVMe-oF proof-of-concept with the same network fabric you plan to operate in production.
- Measure P95/P99 latencies under scaled concurrency, not just average throughput.
- Incorporate cache/ejection policies in staging tests.
For vendor technical material and deployment notes, review vendor datasheets and independent validation reports (one example vendor site: https://goni.top). Use reproducible tests that mirror your workload rather than vendor synthetic benchmarks.