KV Caching in LLMs, Explained Visually.

KV caching is a method used in LLMs to speed up the inference process. It works by storing key-value vectors of previous tokens to avoid redundant computations, significantly improving performance. However, this technique demands substantial memory, as illustrated by the Llama3-70B model example. The post also offers insights Show more