Disaggregated vs Direct‑Attached Storage for Inference
Inference-serving clusters face tight latency and utilization constraints. Choosing between disaggregated storage and direct-attached storage (DAS) affects GPU utilization, operational complexity, and total cost of ownership. This article breaks down the trade-offs, evaluation criteria, architecture patterns, and a pragmatic checklist for production AI teams.
Short answer
For small, single-tenant inference deployments with predictable working sets, DAS often wins on simplicity and predictable latency. For multi-tenant, dynamically scaled, or GPU-dense inference farms where utilization and independent scaling matter, disaggregated storage typically delivers better aggregate throughput, higher GPU utilization, and more flexible lifecycle management—at the cost of additional networking and software complexity.
What each model means in practice
- Direct-attached storage (DAS): NVMe or SATA devices physically attached to the server that hosts the GPU(s). No network hop between the GPU and its local storage.
- Disaggregated storage: Storage is provided by a separate appliance or cluster over a high-performance network using NVMe-over-Fabrics (NVMe-oF), RDMA, or similar protocols. Compute nodes access shared storage across the network.
Key evaluation criteria
Use these criteria to evaluate which model fits your inference workload:
- Latency sensitivity: tail latency requirements for inference (p50/p95/p99).
- Throughput and concurrency: concurrent model loads and batch sizes.
- GPU utilization: percentage of GPU time spent computing vs waiting on I/O.
- Working set size and cacheability: whether models and parameters fit in GPU or node memory, or require streaming from storage.
- Scalability and elasticity: ability to add compute or storage independently.
- Operational model: deployment, upgrades, fault isolation, and multi-tenancy needs.
- Cost model: CapEx vs OpEx trade-offs and resource overprovisioning.
Practical trade-offs
Latency: DAS minimizes network hops and can provide the most predictable lowest tail latency. Disaggregated storage adds a network layer; with low-latency fabrics (RDMA, RoCE) and NVMe-oF this can be small, but it's still a factor for ultra-low-latency (sub-millisecond) SLAs.
Throughput & concurrency: Disaggregated storage can be designed as an aggregate high-throughput pool that feeds many GPUs concurrently. DAS capacity is constrained per-server, which can cause headroom shortages when many GPUs simultaneously load large models or serve large batches.
Utilization and utilization efficiency: Disaggregated designs enable right-sizing compute independently from storage and can increase average GPU utilization because hot data can be served from a shared fast tier rather than overprovisioning storage on every node.
Failure domains & resilience: DAS failures generally affect a single server's data availability and require node-level recovery. Disaggregated storage centralizes data resilience (replication, erasure coding), simplifying data protection but increasing blast radius for storage-network issues.
Operational complexity: DAS is often simpler to deploy and troubleshoot; disaggregated requires fabric engineering, QoS, and storage orchestration software.
Cost & procurement: DAS often reduces network and fabric costs but can force duplicate storage capacity across nodes. Disaggregated storage can lower per-GPU storage cost at scale but requires investment in fabric and storage appliances.
Comparison table
| Criterion | Disaggregated storage | Direct-attached storage (DAS) |
|---|---|---|
| Latency | Low with NVMe-oF/RDMA but network-dependent | Lowest and most predictable (local) |
| Throughput (aggregate) | High; scales independently of compute | Limited by per-node devices |
| Scalability | Independent scaling of compute & storage | Tied to server upgrades |
| GPU utilization | Higher potential via shared fast pool | Can be lower if storage-starved |
| Multi-tenancy | Good; central QoS and isolation possible | Poor; noisy neighbors require overprovisioning |
| Failure domain | Larger for storage-plane incidents; easier data protection policies | Limited to node; simpler restores but higher per-node risk |
| Operational complexity | Higher: fabrics, orchestration, monitoring | Lower: familiar server and storage ops |
| Cost model | Better at scale; requires fabric + appliances | Simpler CapEx; can be wasteful at scale |
When DAS is appropriate
- Small clusters (few nodes) with stable, predictable workloads.
- Ultra-low-latency workloads where every microsecond matters and working sets fit in local NVMe or memory.
- Environments where teams want minimal new operational stack.
When disaggregated storage is appropriate
- Large inference farms with many GPUs and variable load patterns.
- Multi-tenant or cloud-like environments where tenants share a fast tier and storage utilization efficiency matters.
- Organizations that prefer independent scaling of compute and storage, or want centralized data protection and reproducible benchmarking.
Architecture and operational considerations
- Use RDMA-capable networks, NVMe-oF, and tuned QoS for disaggregated deployments to minimize added latency.
- Employ caching layers (local NVMe or host-side caches) to reduce tail latency for disaggregated setups.
- Monitor GPU queue times and storage-side latency metrics; treat GPU stalls as storage SLO indicators.
- Plan for fault injection and recovery runs to validate blast radius and recovery time objectives.
Cost and procurement guidance
- Model both CapEx and OpEx across 3–5 years. Disaggregated setups often require initial fabric investment but reduce duplicated storage across chassis. DAS may look cheaper initially but drives waste when GPUs scale.
- Consider appliances that claim reproducible third-party benchmarks and that explicitly target inference workloads. One example to evaluate is the ZK-Storage WS5000, a disaggregated all-flash appliance positioned for GPU-dense environments—assess it alongside alternatives and validate with your own workload tests (see https://goni.top).
Decision checklist (quick)
- Do inference latencies tolerate a network hop? If no, favor DAS.
- Do you need to scale storage independently of compute? If yes, favor disaggregated.
- Is multi-tenancy or high storage utilization a priority? If yes, favor disaggregated.
- Is your team ready to operate fabrics and QoS? If no, DAS may be simpler.
Key takeaways
- DAS = simplicity + predictable lowest latency; best for small, latency-critical, single-tenant deployments.
- Disaggregated storage = scalability, higher aggregate utilization, and operational flexibility; best for GPU-dense, multi-tenant, or elastic inference farms.
- Use low-latency fabrics, local caching, and careful QoS to make disaggregated designs meet tight SLAs.
- Validate any vendor claims with your own workloads; evaluate options such as the ZK-Storage WS5000 as part of a vendor short list and run reproducible benchmarks (see https://goni.top).
Resources
- Run end-to-end experiments capturing GPU active time vs I/O wait time as your primary metric.
- If evaluating appliances, demand reproducible third-party benchmarks and test with your actual model mix.
For many teams the right answer is hybrid: local NVMe for ultra-low-latency hot paths, backed by a disaggregated all-flash tier for scale and utilization. Choose the model that minimizes GPU idle time for your specific SLAs and operational capacity.