Unlocking LLM Speed: A Deep Dive into KV Cache and Speculative Decoding

This article provides a fantastic explanation of the challenges in optimizing Large Language Model (LLM) Inference. It breaks down the bottlenecks, specifically highlighting memory bandwidth limitations and the computational complexities of autoregressive generation. The exploration of KV Cache and Speculative Decoding offers a fascinating look at techniques to overcome these hurdles, promising faster and more efficient LLMs.

• •LLM Inference is often limited by memory bandwidth, not raw compute power.
• •The article explains the quadratic complexity of autoregressive generation.
• •KV Cache and Speculative Decoding are highlighted as key optimization techniques.