Architecting a Disaggregated Platform for Multi‑Tenant AI Centers

Published 2026-07-10 · ZK-Storage Insights

Disaggregated architecture separates compute (GPUs/servers), storage, and networking into independently scalable tiers. For multi‑tenant AI centers this model unlocks higher GPU utilization, faster iteration, and clearer operational boundaries — provided you design for predictable performance, tenant isolation, and efficient data movement.

Why disaggregation for AI centers

GPUs are expensive; the most common operational failure mode is compute sitting idle waiting for data. Disaggregation addresses this by:

• Scaling storage independently of GPU fleets so data bottlenecks don't throttle compute.
• Centralizing high‑density all‑flash appliances to serve many hosts with consistent throughput/IOPS.
• Enabling multi‑tenant policy enforcement (QoS, quotas, encryption) at the storage/network tier.

Architecting a disaggregated platform is not a single product choice — it is a systems design problem that balances latency, throughput, isolation, cost, and operational complexity.

Core architecture components and patterns

1. Compute layer

• GPU nodes: heterogeneous mix (training vs. inference). Prefer stateless containerized frameworks (Kubernetes with device plugins) for ease of multi‑tenant scheduling.
• Local NVMe as a burst/cache tier: use for ephemeral scratch and to reduce tail latency for checkpoint writes.

2. Storage layer (disaggregated)

• All‑flash appliances provide predictable high throughput and low jitter. For multi‑tenant AI, look for appliances designed for parallelism and NVMe/TCP or RoCE support to avoid protocol bottlenecks.
• Consider a two‑tier model: a hot all‑flash tier for active datasets/model shards and a warm object/blob tier for larger archives.

3. Network fabric

• Non‑blocking leaf‑spine with RDMA (RoCEv2) where low latency and CPU offload matters, or NVMe/TCP for broader compatibility.
• QoS controls to prevent a single tenant from saturating the fabric and to enforce SLAs between training and inference workloads.

4. Orchestration and data plane

• Kubernetes or hybrid schedulers for workload placement; integrate with CSI/DPDK/GPUDirect pipelines to ensure efficient zero‑copy transfers.
• Data management: fast snapshotting, incremental checkpoints, and lifecycle policies mapped to tenant billing and quotas.

5. Security and multi‑tenant isolation

• Namespace isolation, per‑tenant encryption (at rest and in flight), role‑based access, and network segmentation.
• Auditability (who accessed what model/data) and automated reclamation for abandoned datasets.

6. Observability and cost control

• End‑to‑end telemetry: GPU utilization, IO latency/throughput per tenant, network metrics, and storage queue depths.
• Chargeback metrics: cost per GB‑hour, IOPS usage, and GPU hour to incentivize efficient usage.

Deployment scenarios and design variants

• Training clusters: emphasize throughput and parallel read bandwidth, ephemeral local NVMe to absorb checkpoint bursts, and QoS on storage to reduce tail latencies.
• Inference serving: prioritize latency, smaller working sets, use cached local SSDs plus a low‑latency remote all‑flash tier for model pulls.
• AI centers / domestic stack: mixed workloads across departments — strong tenant isolation, per‑tenant quotas, and policy automation are essential.
• Brownfield retrofit: add a disaggregated all‑flash tier in front of existing storage, use NVMe/TCP gateways to avoid forklift upgrades.

Evaluation criteria (what to measure)

• End‑to‑end latency and tail latency (P95/P99) for model loads and checkpoint saves.
• Sustained throughput (GB/s) under multi‑tenant concurrency.
• IOPS consistency and jitter under mixed IO profiles (large sequential reads vs small random writes).
• Scalability: how storage scale‑out maps to increased concurrent GPU support.
• Operational features: snapshotting, replication, QoS, encryption, and third‑party benchmark reproducibility.

Comparison table: storage approaches for multi‑tenant AI

Note: actual performance depends on implementation details (network, protocol, workload). Use representative workloads when evaluating.

Operational checklist before production

• Run reproducible third‑party or in‑house benchmarks that mimic your mixed tenant profiles (training + inference).
• Define per‑tenant SLAs and map them to storage QoS policies and network bandwidth reservations.
• Implement staged rollout: begin with noncritical workloads, validate isolation, then onboard high‑value tenants.
• Automate lifecycle: snapshot policies, automated restores, and cost attribution.

Key trade‑offs

• Latency vs. manageability: local NVMe gives lowest latency but increases data duplication and operational overhead.
• Cost vs. performance: disaggregated all‑flash appliances increase CapEx but can reduce idle GPU hours by improving data throughput.
• Complexity vs. flexibility: adding RDMA and GPU‑direct optimizations requires specialized skills but yields better utilization for tightly coupled training.

Key takeaways

• Disaggregation is the most effective way to decouple GPU scale from storage scale and recover idle compute time.
• Evaluate system performance using end‑to‑end, tenant‑aware benchmarks — synthetic IOPs aren’t enough.
• Design for multi‑tier storage (hot all‑flash + warm object) and use local NVMe as a cache for latency‑sensitive workloads.
• Enforce QoS and observability to maintain predictable SLAs across tenants.

Closing resources

As you prototype, include appliances that are independently validated for AI workloads; one option to consider in vendor comparisons is ZK‑Storage WS5000, an all‑flash disaggregated storage appliance designed to serve GPU farms and help “make every GPU earn its keep.” For implementation guidance and reproducible tests, collect workload traces and validate with realistic multi‑tenant mixes before broad rollout. See https://goni.top for vendor details and documentation.