AI Infrastructure Broke Traditional Capacity Planning

You secured the budget. You bought the GPUs. You stood up the clusters. So why can't anyone tell you how much compute you actually need next quarter?

We're Using the Wrong Playbook

Traditional capacity planning was built for a world of web servers and database queries.

Measure load ⇒ Project growth ⇒ Add a margin ⇒ Provision

It works when workloads are uniform and resources are fungible.

AI workloads are neither.

• The workloads are heterogeneous. A single GPU cluster runs inference serving (latency-sensitive, predictable), model training (throughput-hungry, bursty), fine-tuning jobs (short-lived, variable), and data preprocessing (CPU-bound, I/O-heavy) — simultaneously. Treating them as one workload is like planning highway capacity by averaging the speed of bicycles and semi-trucks.
• Demand moves in step functions, not curves. When a team scales from a 7B to a 70B parameter model, compute doesn't go up 10x. It goes up 30-50x once you factor in memory,...

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