Key-Value caching is a technique that accelerates transformer-based text generation by storing intermediate attention states — specifically keys and values — from previous computation steps so the model can reuse rather than recalculate them for each new token. Benchmarks show KV caching achieves approximately 5.2x faster inference compared to standard generation on a T4 GPU by eliminating redundant attention computations across sequential token generation. The article covers the full implementation, including how KV pairs are concatenated across steps and why the optimisation compounds for longer text sequences.
