AI Practitioner

KV Cache in Large Language Models: Design, Optimization, and Inference Acceleration Transformers are the backbone of modern large language models (LLMs), but inference over long contexts becomes computationally expensive. The KV Cache —short for Key/Value Cache—is one of the most important optimizations that enable scalable and low-latency LLM deployment, particularly for autoregressive decoding. This article explores how KV Cache works, how it's designed, and how to implement and optimize it based on the seminal paper Efficiently Scaling Transformer Inference . 1. What is KV Cache? In Transformer-based models, each attention layer computes attention weights over the entire input sequence. When generating one token at a time during inference, this results in quadratic computational complexity. KV Caching stores the intermediate key and value tensors of past tokens so they do not need to be recomputed at each step. This enables linear time generation and is especiall...