KV cache is a critical optimization technique for LLM inference that stores previously computed key and value vectors to avoid redundant calculations during text generation. The technique provides significant speed improvements (up to 5x in examples) by caching intermediate attention computations and reusing them for subsequent tokens. Implementation involves modifying the attention mechanism to store and retrieve cached values, though it increases memory usage and code complexity. The article provides a complete from-scratch implementation with performance comparisons and optimization strategies for production use.
Table of contents
OverviewWhat Is a KV Cache?How LLMs Generate Text (Without and With a KV Cache)Implementing a KV Cache from ScratchA Simple Performance ComparisonKV cache Advantages and DisadvantagesOptimizing the KV Cache ImplementationConclusionSort: