Shared prefix optimization is a key technique for reducing redundant computation in LLM inference. When multiple requests share a common prefix, such as a system prompt or few-shot examples, the key-value (KV) cache for that prefix can be computed once and reused across requests. This article examines the core principles behind this optimization, distinguishing between cross-batch reuse (as implemented in vLLM and SGLang) and intra-batch scenarios where multiple requests within the same batch share a prefix. The analysis covers the algorithmic trade-offs, memory management strategies, and implementation challenges that engineers face when integrating these optimizations into production systems. Understanding these techniques is crucial for teams building high-performance LLM serving infrastructure, as they directly impact latency, throughput, and cost efficiency. The article also discusses how prefix caching interacts with other optimizations like continuous batching and paged attention, providing a holistic view of modern inference engine design.
Explore how shared prefix optimization accelerates LLM inference by reusing KV cache across requests, with insights for vLLM and SGLang.