GPU Cluster Capacity Planning Checklist for AI Workloads

A GPU cluster capacity planning checklist covers four steps: model demand per workload, aggregate with utilization and headroom, size storage and network alongside GPUs, and validate before buying — the method that prevents the twin failures of overprovisioning (paying for idle GPUs) and underprovisioning (queues and missed deadlines). For the full sizing method, see how to size AI infrastructure capacity. For the training vs inference split, see capacity planning for training vs inference.

The Four-Step Checklist

1. Model demand per workload: for each workload, compute the resources a single instance needs (GPUs, memory, storage throughput), determine concurrency or traffic pattern, and model peak and average separately. 2. Aggregate with utilization and headroom: sum demand across workloads, subtract the sharing benefit from scheduling, add headroom for failure and growth. Utilization headroom accounts for the gap between raw peak and realistic utilization. 3. Size storage and network: storage throughput must keep GPUs fed; network must handle collective operations and multi-node communication at the GPU scale planned. Sizing only GPUs and defaulting storage and network is the most common sizing error. 4. Validate before buying: run representative workloads on a slice of the proposed cluster and measure whether they hit performance targets at planned concurrency. Validation catches the assumptions that fail in practice. For the test methodology, see evaluating AI cluster networking.

FAQ

How do I plan GPU cluster capacity correctly?

Model demand per workload, aggregate with realistic utilization and headroom, size storage and network alongside GPUs, and validate on a slice before buying. Sizing GPUs alone and defaulting the rest is the most common error. See the four steps above.

Summary

GPU cluster capacity planning is demand modeling, aggregation, supporting infrastructure sizing, and validation. For the full method, see how to size AI infrastructure capacity.