GPU Cluster Capacity Planner

Provisioning the right GPU infrastructure for AI workloads is one of the most consequential — and most expensive — decisions a machine learning engineering team makes. The GPU Cluster Capacity Planner helps ML platform engineers, infrastructure architects, and AI leads size their clusters correctly from the start, avoiding both costly over-provisioning and the performance bottlenecks that come from under-resourcing large-scale training and inference jobs.

This assistant works through the full capacity planning process for GPU environments. You describe your workload characteristics — model size, training framework, batch size, dataset volume, target training duration, or inference latency requirements — and the assistant helps you translate those requirements into concrete infrastructure specifications. It covers GPU selection trade-offs (A100 vs. H100 vs. MI300X), NVLink and InfiniBand interconnect requirements for distributed training, memory bandwidth constraints for large model weights, and storage I/O throughput needs for data pipelines.

The assistant also addresses multi-tenant cluster planning for organizations sharing GPU resources across teams, including namespace isolation, job scheduling strategies (FIFO vs. fair-share vs. priority queuing), and how to estimate concurrent job capacity without starving long-running training runs. It covers both on-premises cluster design and cloud-based GPU fleet planning across AWS (p4d, p5, Trn1), GCP (A3, TPU pods), and Azure (ND series) instance families.

Beyond raw compute, the assistant factors in the full infrastructure stack: high-speed storage (Lustre, GPFS, WekaFS), networking topology, power density constraints for on-prem builds, and cost modeling for reserved vs. on-demand vs. spot GPU capacity. It helps you build a defensible capacity plan that you can present to engineering leadership or finance teams.

This role is ideal for ML platform teams preparing to scale training workloads, infrastructure engineers designing AI-dedicated compute clusters, and technology leaders evaluating build-vs-buy decisions for GPU capacity.