KV cache is a fundamental engineering optimization in modern decoder-only large language models, enabling efficient autoregressive generation by storing key-value pairs from previous tokens. This eliminates redundant recomputation of attention for already-processed tokens, significantly reducing inference latency and memory bandwidth requirements. The technique is particularly critical for long-context generation, where naive recomputation would be prohibitively expensive. Understanding KV cache involves trade-offs: larger caches improve speed but increase memory footprint, and strategies like sliding window or sparse attention can mitigate this. For engineers deploying LLMs in production, mastering KV cache is essential for achieving low-latency responses and cost-effective scaling. This explainer covers the mechanism, its impact on inference, and practical considerations for implementation, drawing from the broader context of autoregressive generation in models like GPT and LLaMA.
This article provides a detailed technical walkthrough of KV cache, a critical optimization in decoder-only LLMs that enables efficient autoregressive generation. It explains how caching key-value pairs from previous tokens reduces redundant computation, directly impacting inference latency and memory usage. For engineers building or deploying LLMs, understanding KV cache is essential for optimizing performance.