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Deep Dive: Optimizing LLM inference

Open-source LLMs are great for conversational applications, but they can be difficult to scale in production and deliver latency and throughput that are incompatible with your cost-performance objectives. In this video, we zoom in on optimizing LLM inference, and study key mechanisms that help reduce latency and increase throughput: the KV cache, continuous batching, and speculative decoding, including the state-of-the-art Medusa approach. Slides: https://fr.slideshare.net/slideshow/j... ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos. Follow me on Medium at   / julsimon   or Substack at https://julsimon.substack.com. ⭐️⭐️⭐️ 00:00 Introduction 01:15 Decoder-only inference 06:05 The KV cache 11:15 Continuous batching 16:17 Speculative decoding 25:28 Speculative decoding: small off-the-shelf model 26:40 Speculative decoding: n-grams 30:25 Speculative decoding: Medusa