大规模 LLM 推理服务：KV Cache 的隐藏优势
原标题：Presentation: Serving LLMs at Scale: The Hidden KV Cache Advantage
AI 摘要
Momento 联合创始人 Khawaja Shams 在 InfoQ 演讲中介绍大规模 LLM 推理服务，重点讲解 KV Cache 的作用。他将推理拆分为计算密集的 prefill 阶段和内存密集的 decode 阶段，并指出首 token 延迟取决于 prefill 完成。他强调在 GPU 稀缺背景下，分布式系统工程师可通过提升缓存与利用率来改善推理性能和经济性。
正文节选
Transcript Khawaja Shams: How many of you find yourselves to be experts in inference? My name is Khawaja. I will try to keep this light-hearted, and there will be a little bit of repetition because one of the things I learned as a teacher was sometimes repetition really helps to internalize the concepts. These concepts are interesting, but once they click they make a lot of sense. I started my career building cameras for the Mars rovers. I was at NASA for eight years doing distributed systems. T