Understanding and Coding the KV Cache in LLMs from Scratch

Sebastian Raschka publishes a from-scratch tutorial on KV caches — the inference optimization that stores intermediate key/value attention computations to avoid recomputing them on each token generation step. The article covers both conceptual explanation and working Python code, filling a gap left by his "Building a Large Language Model From Scratch" book. Essential reading for anyone deploying or building on top of LLMs who wants to understand the memory/speed tradeoffs driving production inference systems.