LLM Development Bundle

This book is a complete guide to understanding and building large language model inference engines in Rust. It takes you from the mathematical foundations of the transformer architecture through tokenization, attention mechanisms, KV caching, quantization, batching strategies, GPU and CPU optimization, distributed serving, and production deployment. Along the way, you will see idiomatic Rust code examples, performance benchmarks, comparisons with leading frameworks like vLLM, TGI, llama.cpp, Candle, Burn, and mistral.rs, and practical insights for shipping inference systems that rival the best open-source and commercial offerings. Whether you are a systems programmer interested in AI infrastructure or an ML engineer curious about the Rust ecosystem, this book gives you the depth to build, optimize, and understand.

This book takes you from zero to production with llama.cpp, the C/C++ inference engine that has become the backbone of local AI. You will learn how to build it from source on Linux, macOS, and Windows; understand the GGUF file format and quantization trade-offs; master every command-line tool; deploy a production-ready API server; and tune performance across CUDA, ROCm, Vulkan, Metal, and CPU backends. Whether you are running models on a laptop, a gaming PC, or a data center GPU, this book gives you the knowledge to extract maximum value from your hardware.

Large language models have become too big to run on a single GPU. Quantization is the technology that makes them deployable, and it has evolved from an academic curiosity into an engineering discipline with its own algorithms, toolchains, and best practices. This book explains how LLM quantization works, from first principles through production deployment. You will learn the internals of GPTQ, AWQ, GGUF, NF4, and other methods; understand their trade-offs in quality, speed, and memory; and find clear guidance for choosing the right approach for your hardware and use case. Along the way you will encounter real benchmarks, reproducible code examples, and a decision framework for navigating one of the most rapidly evolving areas of applied machine learning.

Large language models power some of the most powerful software tools ever built, yet their inner workings remain mysterious to most developers. This book changes that. Starting from raw text and ending with a live inference API, you will build a complete LLM training pipeline from scratch using PyTorch. Every concept is explained clearly, every code listing is production-quality, and every chapter ends with hands-on exercises that cement your understanding. By the time you finish, you will understand tokenization, attention, positional encoding, transformer architecture, data curation, distributed training, fine-tuning, alignment, evaluation, and deployment at a level that no API wrapper can provide.

This book is a complete, practical guide to running large language models entirely on your own Mac. If you have an M1, M2, or M3 MacBook and want to deploy open-source LLMs locally for privacy, cost savings, or independence from cloud APIs, this book takes you from zero to mastery. You will learn the hardware architecture that makes Apple Silicon uniquely suited for this work, master every major inference framework (llama.cpp, Ollama, MLX, and Hugging Face Transformers), understand quantization strategies that fit billion-parameter models into your unified memory, and build real applications with local AI. Every technique described is fully open-source, reproducible on macOS, and grounded in real benchmarks and working code.