How to Plan LLM Inference Capacity for a Shared Platform

Published: 2026-07-29 A recurring capacity-planning request for an internal LLM platform is: We have selected a model. Do we have enough capacity, and how many GPUs would we need for N users? The question is missing the information needed to answer it. The model and its serving configuration determine the minimum resources required to start one replica. They don't tell us how many requests that replica can handle while meeting the product's latency and reliability requirements. That depends on prompt and response lengths, arrival rate, concurrency, cache reuse, calls per user task, traffic bur

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