KV Cache Implementation Inside vLLM

The key-value (KV) cache is a fundamental optimization in transformer-based LLM inference. It stores intermediate attention states, i.e., keys and values computed during the prefill phase, so that subsequent tokens can reuse them instead of recomputing from scratch. This significantly reduces compute cost and latency, especially for long context or multi-turn agentic workloads. KV caching has been extensively discussed across several blogs and documentation [1, 2, 3, 4, 5]. In this article, instead of revisiting those well-known concepts, vLLM (v0.20.