I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.

Sebastian Raschka's Blog offers insights, tutorials, and research updates on machine learning, deep learning, and artificial intelligence. Covering topics such as neural networks, data science, and Python programming, Sebastian Raschka's Blog provides resources for students, researchers, and practitioners in the field of AI. Developers can learn about  algorithms, research methodologies, and practical applications of machine learning through Raschka's blog posts and publications.

Sebastian Raschka

KV cache is a critical optimization technique for LLM inference that stores previously computed key and value vectors to avoid redundant calculations during text generation. The technique provides significant speed improvements (up to 5x in examples) by caching intermediate attention computations and reusing them for subsequent tokens. Implementation involves modifying the attention mechanism to store and retrieve cached values, though it increases memory usage and code complexity. The article provides a complete from-scratch implementation with performance comparisons and optimization strategies for production use.

Understanding and Coding the KV Cache in LLMs from Scratch

How LLMs Generate Text (Without and With a KV Cache)