6 Hours of YouTube and 85 handtyped pages of notes on LLM Inference – Start here (github.com)

A new educational project focuses on building a small engine for large language model (LLM) inference from first principles using C++ and CUDA. The initiative includes an extensive series of handwritten notes and diagrams, titled "Inference from Scratch," which detail every step in the process, from the moment a prompt is entered to the generated output. This includes parsing model metadata, managing tensor memory, and executing various aspects of a transformer architecture on the GPU, significant for those in AI/ML who aim to deepen their understanding of how LLMs operate. This project employs a fixed weight layout for the Llama 3.2 1B Instruct model and incorporates advanced functionalities such as a local byte pair encoding (BPE) tokenizer, dynamic batched decoding, and a sophisticated KV cache management system. The detailed architecture enables efficient inference, optimized memory usage, and flexibility for generating responses across different prompts. With its focus on low-level implementation, this resource serves as a practical guide for engineers and researchers looking to explore LLMs' underlying mechanisms and enhance their systems through a hands-on approach.