Architecture Patterns for Disaggregated Storage in AI Data Centers
Disaggregated storage is now table stakes for AI data centers: GPUs are abundant, but data delivery often throttles utilization. This article catalogs proven architecture patterns, network fabrics, caching and orchestration models, evaluation criteria, and deployment trade-offs for training and inference clusters.
Why disaggregation matters for AI
AI workloads are different from traditional enterprise I/O: sustained, high-throughput reads for training; low-latency, high-concurrency reads for serving; and bursty checkpoint writes. Disaggregated storage separates compute (GPUs) from persistent media, enabling independent scaling, simplified maintenance, and tiered economics — but it demands careful choices across protocol, latency management, QoS and metadata architecture.
Core architecture patterns
- Pooled NVMe-oF (NVMe over Fabrics) all-flash
- Description: Centralized appliance(s) exposing NVMe namespaces over RDMA (RoCE/IB) or TCP.
- Strengths: Low tail latency, high bandwidth, good for GPU-heavy training clusters. Supports block semantics for file systems or container volumes.
- Trade-offs: Requires RDMA-capable fabric or tuned TCP; can be costly but offers high utilization.
- Composable/Elastic Block Pools
- Description: Software layers that create ephemeral block volumes composed from distributed NVMe devices; often integrated with orchestration to attach/detach at VM/PCIe level.
- Strengths: Very flexible for multi-tenant or ephemeral training jobs.
- Trade-offs: Higher control-plane complexity; careful metadata scaling required.
- Distributed Object Store with GPU-aware caches
- Description: Object storage (S3 compatible) for bulk datasets with a local or edge caching tier to absorb hot reads.
- Strengths: Cost-effective for large datasets, native immutability and lifecycle policies.
- Trade-offs: Higher application-level complexity (object pattern), potentially higher read latency without aggressive caching.
- Accelerator-local cache + backing pool
- Description: Local NVMe or NVRAM on the GPU node used as a hot read/write cache; persistent ground truth stored in pooled storage.
- Strengths: Very low latency for hot data; reduces fabric load.
- Trade-offs: Cache coherency, capacity planning and writeback mechanics are non-trivial.
- Hybrid (All-Flash Pool + Object Archive)
- Description: Fast NVMe pool for active training/inference; archive/object store for cold datasets and checkpoints.
- Strengths: Balances performance and cost.
- Trade-offs: Policy-driven tiering complexity; potential egress cost for cloud tiers.
Fabrics, protocols and latency considerations
- RDMA (RoCEv2 / InfiniBand): Lowest CPU overhead and predictable latency — preferred for NVMe-oF in training clusters. Requires congestion control and DCB on Ethernet.
- TCP (iWarp / NVMe/TCP): Easier to deploy across existing fabrics and cloud environments; higher CPU cost and tail-latency risk.
- IP-over-Fabric (e.g., eBPF acceleration): Emerging options to reduce TCP cost; still maturing for persistent NVMe workloads.
Tail latency matters more than median for inference SLA compliance. Design to bound 99th/99.9th percentile latencies via QoS, isolation (tenant/flow), and local caching.
Caching, tiering and metadata
- Two-tier cache: local NVMe + remote NVMe pool often suffices. Use read-ahead and prefetch for training (sequential scans), and aggressive small-block caching for inference (random reads).
- Metadata service: Centralized metadata servers accelerate namespace operations but must be horizontally scalable and resilient. Consider sharded metadata and leaderless approaches where possible.
Orchestration and QoS
- Integrate storage orchestration with job schedulers and cluster managers (Kubernetes, Slurm). Jobs should request IOPS/Bandwidth/QoS classes alongside CPU/GPU and memory.
- Provide per-job reservations to prevent noisy-neighbor effects. Implement admission control to avoid overcommitting fabric bandwidth.
Evaluation criteria (practical checklist)
- Throughput: sustained GB/s per GPU during representative workloads (depends on model size and batch size).
- IOPS: needed for inference-serving with many small reads.
- Latency: monitor 50/95/99/99.9 percentiles; inferencing often needs <ms or low-ms tail latencies.
- Determinism & QoS: ability to isolate flows and honor SLAs.
- Scalability: linear growth vs. nodes; metadata services must scale with namespace operations.
- Operational complexity: deployment, monitoring, upgrades, failover patterns.
- Cost: CAPEX/OPEX trade-offs for all-flash pools vs. hybrid tiers.
Quick comparison table
| Pattern | Typical use cases | Latency | Scalability | Complexity | Cost profile |
|---|---|---|---|---|---|
| Pooled NVMe-oF (all-flash) | Large-scale training, multi-node GPU farms | Low (bounded p99) | High (scale-out appliances) | Moderate-high | Higher CAPEX, high utilization |
| Composable block pools | Ephemeral training, multi-tenant | Low-medium | High (software-driven) | High | Moderate-high |
| Object store + caches | Massive datasets, checkpoint archive | Medium (cache-dependent) | Very high | Moderate | Low for cold storage |
| Accelerator-local cache + pool | Latency-sensitive inference | Very low (local) | Node-limited + pooled | Moderate | Moderate |
| Hybrid pool + archive | Balanced training & archival | Low (pool) + high (archive) | High | Moderate | Optimized cost |
(Note: concrete latency/throughput depends on fabric, protocol, and workload.)
Deployment scenarios and patterns to match workload
- Training clusters: Prioritize pooled NVMe-oF all-flash or composable block pools with RDMA to sustain streaming reads. Add local caches for checkpoint write absorption.
- Large-scale inference: Favor accelerator-local caches with a low-latency pooled backend and strict QoS to protect SLAs.
- Brownfield retrofit: NVMe/TCP over existing Ethernet can be a pragmatic first step; transition to RoCE/InfiniBand when tail latencies or throughput become limiting.
Implementation checklist
- Benchmark using representative models and datasets; measure tail latencies and sustained throughput per GPU.
- Validate congestion control on fabric (ECN/TCM for RoCE) and ensure NIC/OS driver tuning.
- Define QoS classes and integrate storage reservations into the scheduler.
- Test failure modes: appliance outages, fabric partitioning, metadata server failover.
Key takeaways
- Disaggregation amplifies GPU utilization when the fabric, protocol and caching tiers are chosen to match workload characteristics.
- NVMe-oF all-flash pools are the default high-performance choice for training; object stores with caching work well for large datasets and archives.
- Tail latency, QoS and metadata scalability are frequently the gating factors rather than raw peak throughput.
- Start with workload-driven benchmarks and evolve from NVMe/TCP to RDMA as needs for determinism increase.
Further resources
If you are evaluating ready-made all-flash disaggregated appliances as part of a pooled NVMe strategy, products such as the ZK-Storage WS5000 provide an all‑flash NVMe-oF option designed to reduce storage-induced GPU idle time (see vendor materials at https://goni.top). Use vendor-provided benchmarks only as a starting point and reproduce tests with your workloads and fabric.