Why More GPUs Won't Save Your AI Infrastructure

Every organization building with AI right now is focused on the same thing: ship the model, get it into production, show results. And that urgency is justified. But I keep seeing the same failure pattern repeat itself, and it has nothing to do with model quality or data pipelines. It comes down to capacity discipline, or rather, the complete absence of it. The Problem Nobody Wants to Own AI workloads are fundamentally different from traditional web services. A standard request/response API has a relatively predictable resource profile. You know your P99 latency, you know your memory footprint, you can forecast QPS growth and plan hardware accordingly. AI inference does not behave this way. A single LLM serving endpoint can swing from 2GB to 40GB of GPU memory depending on context length, batch size, and model configuration. Multiply that by the number of models your org is trying to serve, and you get an infrastructure environment where nobody actually knows how much capacity they need