KV Caching Explained: A Deep Dive into Optimizing Transformer Inference
Introduction to KV Caching When large language models (LLMs) generate text autoregressively, they perform redundant computations by reprocessing the same tokens repeatedly. Key-Value (KV) Caching solves this by storing intermediate attention states, dramatically improving inference speed – often by 5x or more in practice. In this comprehensive guide, we’ll: Explain the transformer attention bottleneck Implement KV caching from scratch…