The Memory Trick That Made Modern LLM Inference Practical

LLM inference is inherently expensive because autoregressive generation requires recomputing full attention over the entire sequence for every new token—a bottleneck that makes production deployment nearly impossible without optimization. KV Cache solves this fundamental memory-compute tradeoff by caching key-value pairs, enabling engineers to achieve the performance required for practical LLM deployment. Understanding KV Cache deeply is essential for ML engineers optimizing inference systems, and newer techniques like vLLM's PagedAttention, quantized caching, and speculative decoding build on this core insight.