One post tagged with "gpu-inference"

Most teams running LLM inference treat GPU provisioning like a guessing game. They see a model needs "140 GB at FP16," panic, requisition four A100-80GB cards, and call it done. What they don't calculate is how KV cache, concurrency, and quantization interact to determine the actual memory footprint — and that miscalculation typically means they're paying 3x more than necessary.

The math isn't complicated. But almost nobody does it before signing the cloud contract. This article walks through the exact formulas, shows where the hidden memory sinks live, and explains the bin-packing strategies that let you serve four models on hardware budgeted for one.