UNDERSTANDING AND CODING THE KV CACHE IN LLMS

Summary from the TLDR AI newsletter

Key-value (KV) caches store intermediate attention computations during LLM inference to avoid redundant calculations. When generating "Time flies fast" token by token, the model normally recomputes attention for "Time" and "flies" at each step, but caching these values delivers 5x speedups. This tutorial progresses from modifying a 124M parameter GPT model with basic cache buffers and position tracking to production-ready optimizations like pre-allocated memory and sliding windows to address the linear memory growth that becomes prohibitive for long sequences.