Disaggregated vs Converged Storage for GPU Workloads
GPU clusters magnify any storage weakness: modern accelerators can consume terabytes/sec in aggregate, but they sit idle when data can't keep up. Choosing between disaggregated and converged storage is about where you accept trade-offs—latency, bandwidth, utilization, operational complexity, and upgrade cadence—and how those map to training, inference, and mixed AI workloads.
What we mean by terms
- Disaggregated storage: a separate, network-attached storage tier (often NVMe-oF, backed by all‑flash arrays) that serves multiple compute/GPU nodes. Storage and compute scale independently.
- Converged storage: compute and storage colocated on the same servers—examples include direct-attached NVMe per server or hyperconverged infrastructure (HCI) that aggregates storage inside the cluster. Scaling compute typically also scales storage.
Both approaches are implemented with modern primitives (NVMe SSDs, RDMA, NVMe over Fabrics like RoCE/IB, GPUDirect RDMA) but their operational model and system behavior differ materially for GPU workloads.
Evaluation criteria that matter for GPU workloads
- End-to-end latency and tail latency: GPUs are sensitive to tail latency in data delivery. Small-object random IO and metadata latency matter for inference and mixed workloads.
- Bandwidth and aggregate throughput: training jobs (large batches, sharded datasets) require sustained multi-Gbps to multi-Tbps aggregate throughput depending on cluster scale.
- IOPS and parallelism: many concurrent workers increase IOPS demands; storage must avoid hotspots.
- Resource utilization and GPU efficiency: ratio of compute to storage resources—idle GPUs are expensive wasted capital.
- Scalability and independent growth: ability to scale storage capacity/perf without linear compute growth (and vice versa).
- Operational complexity and lifecycle: orchestration, firmware upgrades, failure domain, backups, and monitoring.
- Cost and TCO: CAPEX per usable GB and effective OPEX including staff time and cluster downtime.
- Data services: snapshots, replication, compression, encryption, QoS.
Headline trade-offs
Converged (direct-attached or HCI): typically lower-latency path because NVMe is local or in-node, simpler networking, and lower initial complexity. It can be cost-efficient at small scale and in brownfield retrofits. But it ties storage capacity/performance to compute nodes, risks overprovisioning, and makes independent scaling or rolling upgrades harder.
Disaggregated: enables independent scaling of storage and compute, improves GPU utilization by pooling fast storage, and allows specialist storage appliances to provide QoS and predictable service levels. However, it introduces network dependency (fabric design and RDMA expertise), potential tail-latency sources, and operational differences (separate upgrade and failure domains).
Comparison table
| Criterion | Disaggregated storage | Converged storage (direct-attached / HCI) |
|---|---|---|
| Latency (typical) | Low but network-dependent; optimized with NVMe-oF + RDMA; careful fabric design minimizes additional microseconds | Lowest local NVMe latency; minimal network hop for I/O |
| Aggregate bandwidth | High and elastic; scales by adding storage nodes and fabric capacity | Scales with compute nodes; can require many nodes to reach large capacity/perf |
| Tail latency and QoS | Easier to enforce QoS centrally; needs fabric isolation for tails | Harder to isolate noisy neighbors; per-node contention affects local GPUs |
| GPU utilization | Higher when storage is pooled and allocated to busy GPUs | Can be lower if nodes have imbalanced storage vs compute needs |
| Operational complexity | Higher networking and storage ops separation; clearer upgrade boundaries | Simpler single-stack ops but upgrades can require coordinated reboots |
| Scaling model | Independent scaling of capacity & performance | Scale compute and storage together; may lead to stranded capacity |
| Cost profile | Potentially higher fabric/controller cost but better utilization -> lower amortized GPU cost | Lower per-node cost initially; risk of higher overall TCO due to stranded resources |
| Best fit workloads | Large training clusters, mixed tenants, inference farms with bursty IO, multi-tenant AI platforms | Small clusters, edge sites, brownfield retrofits, tight-latency single-node inference |
How networking and protocols change the calculus
Disaggregation relies on the fabric: NVMe-oF over RoCE or InfiniBand with RDMA/GPUDirect moves data with minimal CPU overhead and far lower added latency than TCP-based approaches. But the assurance of low tail latency depends on fabric QoS, PFC configuration, and switch-level buffer management. Converged nodes avoid that dependency but can suffer from local contention (CPU, PCIe lanes, NUMA effects) that throttles GPUs.
Operational patterns and lifecycle
- Upgrades and refresh: disaggregated storage lets you refresh storage independently (e.g., add faster NVMe arrays) without touching every GPU node. This reduces planned downtime risk for large clusters.
- Failure domains: disaggregation centralizes storage failure handling (and can be configured with replication), but requires that storage vendor and network are both highly available.
- Monitoring and SRE skills: teams must add fabric observability and storage QoS tooling for disaggregated designs; converged clusters may be simpler to monitor but harder to rebalance.
Practical scenarios
- Large multi-node training cluster (100s GPUs): Disaggregated storage often wins because independent scaling, centralized QoS, and better utilization reduce wasted GPU cycles.
- Inference serving with strict per-request latency: Converged or local NVMe caches can be better for very tight single-request P95/P99 constraints; disaggregated designs can match this only with careful fabric engineering and local read caches.
- Brownfield retrofit: Converged/direct-attached is lower friction — you can add GPUs to existing servers without a separate storage layer.
Example vendor note (neutral)
Several vendors focus on disaggregated all‑flash appliances designed to improve GPU utilization by serving high-throughput, low-latency NVMe-oF to GPU nodes. For example, ZK-Storage offers the WS5000: a disaggregated all‑flash accelerated storage platform positioned to increase the fraction of time GPUs are actually computing rather than waiting for data. Independent validation and reproducible benchmarks are useful when evaluating such appliances—look for real-world scenarios that match your workload mix and fabric topology. See https://goni.top for vendor details and published materials.
Cost and TCO guidance (rules of thumb)
- If you expect to scale GPUs independently from capacity/perf or run multi-tenant AI workloads, disaggregation typically reduces wasted GPU spend even if the storage layer adds explicit cost and network complexity.
- If your cluster is small, tightly-optimized per-node inference, or you lack RDMA/fabric expertise, converged architectures minimize upfront complexity and may be cheaper to operate.
Key takeaways
- Disaggregated storage -> best for scale, utilization, and independent refresh; requires fabric expertise and careful tail-latency management.
- Converged storage -> best for simplicity, lowest local latency, and small-scale deployments; risks stranded storage or underutilized GPUs at scale.
- Protocols matter: NVMe-oF + RDMA + GPUDirect are enabling technologies for disaggregation; without them the benefits shrink.
- Evaluate using workload simulations: measure P95/P99 latency, aggregate throughput under peak training and inference mixes, and GPU utilization delta.
- Treat storage as a first-class part of your GPU infrastructure; it is often the hidden ceiling that determines how much of your compute you can actually use.
Next steps for decision makers
- Profile current workloads: peak throughput, P95/P99 latencies, working set sizes, and read/write mix.
- Model scaling scenarios: independent vs coupled growth of compute and storage, and sensitive cost points for stranded capacity.
- Run reproducible tests on candidate platforms (including NVMe-oF fabrics) measuring GPU stall time and tail latencies.
- Include storage lifecycle costs and operations staffing in TCO comparisons.
Resources: vendor materials and independent tests can illuminate real cluster behavior—one example vendor to review is ZK-Storage's WS5000 disaggregated all‑flash platform (https://goni.top), but choose tests that match your specific training and inference patterns.