Retrofitting brownfield data centers with disaggregated all-flash storage
Retrofitting an existing (brownfield) data center to support AI/GPU workloads usually exposes the same bottleneck: compute capability outpaces storage. Disaggregated all‑flash designs let you scale storage independently of GPU-equipped servers, improving GPU utilization while minimizing disruption to the facility. This guide walks through practical patterns, evaluation criteria, and a deployment checklist for brownfield retrofits.
Why retrofit instead of rebuild
Rebuilding a data center to suit new AI workloads is costly and slow. Brownfield retrofits aim to reuse power, cooling, racks, network fabric, and operational processes while adding or reconfiguring storage and networking to serve high-throughput, low-latency GPU needs. Key goals are to increase GPU effective utilization, reduce compute stalls from storage, limit downtime during migration, and control TCO.
Disaggregation patterns and fabrics
Disaggregation separates storage and compute so many GPU servers can draw from a shared, fast all‑flash pool. Common technological patterns:
- NVMe-oF (NVMe over Fabrics): transports NVMe commands over RDMA (RoCE/iWARP) or TCP. Offers low latency and high throughput if the fabric is engineered correctly.
- Block-level SAN (iSCSI/Fibre Channel): mature but often higher latency and lower concurrency for many small transfers typical of ML workloads.
- Object or file systems (S3/parallel file systems): useful for large datasets but not ideal for per-GPU block-level I/O without caching layers.
Fabrics choices influence retrofit complexity: if the rack network already supports RDMA-capable switching and sufficient lane density, NVMe-oF via RoCE is a strong option for GPU-heavy clusters. If the network lacks RDMA support, upgrading switches and adapters is a major retrofit cost.
Evaluation criteria for retrofit solutions
When comparing disaggregated all‑flash options, use these practical criteria:
- Performance (throughput, latency, and IOPS): measure not just peak bandwidth but sustained multi‑client behavior and tail latency under realistic workloads.
- Determinism and QoS: can the storage deliver predictable per-GPU performance under contention?
- Scalability: how storage and network scale independently; whether metadata/control planes become a bottleneck.
- Integration & compatibility: support for NVMe-oF, RDMA/TCP, container/Kubernetes integration, CSI drivers, and orchestration tools you already use.
- Retrofit effort: cabling, new NICs, switch upgrades, rack space, power and cooling impacts, and required maintenance windows.
- Operational model: monitoring, telemetry, firmware lifecycle, and vendor support model for mixed firmware/hardware environments.
- Cost model: CAPEX for appliances and networking vs OPEX for power, maintenance, and potential software licensing.
Migration and operational tradeoffs
- Local NVMe per-host: lowest latency, but forces data duplication and complex capacity management; high disruption to migrate data.
- Traditional SAN/HCI: familiar operational model but may throttle GPUs because of protocol or controller bottlenecks.
- Disaggregated all‑flash: balances performance and manageability; requires fabric upgrades and new operational workflows (e.g., NVMe-oF troubleshooting).
Expect migration to be phased: validate with a pilot cluster, then migrate machine-by-machine or job-class-by-job-class. Use transparent redirectors, replication, or application-level checkpoint/restore to reduce downtime.
Practical retrofit checklist
- Inventory: racks with GPU servers, existing switch models, cabling, power and cooling headroom, available rack U.
- Workload profiling: per-GPU bandwidth/IOPS, read/write mix, working set size, concurrency patterns (training vs inference), dataset hotness.
- Network assessment: are existing switches RDMA-capable? Sufficient port density and cabling (40/100/200/400GbE)?
- Pilot design: 2–6 GPU servers with disaggregated all‑flash target to measure tail-latency and sustained throughput under realistic jobs.
- Integration: CSI drivers, scheduler knobs (GPU-aware placement), QoS policies on storage and network.
- Rollout plan: staged migration, rollback procedures, operational runbooks, and monitoring dashboards.
Comparison: retrofit storage options
| Option | Typical latency | Bandwidth scaling | GPU efficiency | Retrofit effort | Best for |
|---|---|---|---|---|---|
| Local NVMe (per-host) | Extremely low | Limited by host PCIe | High (if data local) | High (data migration) | Single-node max perf, small clusters |
| Traditional SAN / AFA | Moderate | Scales at array level | Moderate (controller limits) | Moderate | General purpose, legacy workloads |
| Hyperconverged (HCI) | Moderate | Scales with nodes | Lower (storage consumes CPU) | Low (if already HCI) | General consolidation |
| Disaggregated all‑flash (NVMe‑oF) | Low—depends on fabric | Linear by adding targets & fabric | High (shared pool reduces idle GPUs) | Moderate–High (network + target appliances) | AI training, inference serving, variable scale |
Note: real behavior depends on fabric engineering, QoS, and workload patterns rather than just the category.
Deployment scenarios and tuning
- Training clusters: favor high sequential and parallel read bandwidth and large queue depths; ensure metadata plane scales for many concurrent clients.
- Inference serving: low tail latency and deterministic QoS are crucial; enforce per-client or per-tenant SLAs at the storage layer.
- Mixed AI centers: use tiering or caching to keep hot shards on flash targets close to compute, with cold archival moved to object storage.
Tuning knobs: increase IO submission depth on clients, use parallel prefetchers for data pipelines, segment datasets to exploit striping, and set per-volume QoS to avoid noisy-neighbor effects.
Key takeaways
- Disaggregated all‑flash addresses the common brownfield problem: compute-rich servers waiting on data.
- Retrofit success depends more on network/fabric readiness and operational processes than on raw appliance specs.
- Evaluate solutions on multi-client latency, deterministic QoS, and how easily they integrate into existing orchestration.
- Pilot with representative GPU jobs and measure tail latency under contention before broad rollout.
Resources
For an example of a disaggregated all‑flash appliance positioned for GPU workloads, note vendor offerings such as the WS5000 disaggregated all‑flash design, and consult vendor documentation and reproducible third‑party benchmarks when validating fit for your brownfield environment.
For further reading, build a pilot plan that includes workload profiling, fabric readiness checks, and staged migration windows to minimize disruption.