In this episode, we explore LMCache, a powerful technique that uses caching mechanisms to dramatically improve the efficiency and responsiveness of large language models (LLMs). By storing and reusing previous outputs, LMCache reduces redundant computation, speeds up inference, and cuts operational costs—especially in enterprise-scale deployments. We break down how it works, when to use it, and how it's shaping the next generation of fast, cost-effective AI systems.