How to Size GPUs for AI Inference and TCO Without Overspending

GPU capacity for an AI service depends on simultaneous requests and the work inside each request, not simply its daily user count. NVIDIA's sizing guide emphasizes model memory, input and output lengths, response-time targets and reusable cached input. Reusing previously processed input can reduce the work needed before an answer starts, changing the capacity required for the same traffic. The guide proposes reserving a baseline for steady demand and adding flexible cloud capacity for bursts. This connects infrastructure commitments to the workload's predictable portion instead of treating peak demand as the permanent requirement. Sizing framework.