"GGUF Model Packaging: Managing Quantized LLM Artifacts for Local Inference"
As local LLM deployment matures, the challenge is no longer just running a model—it is packaging, validating, and governing the artifact so it behaves predictably across rapidly changing runtimes. This book is written for experienced practitioners who already work with model files, conversion tools, and inference stacks, and who need a rigorous guide to GGUF as the operational contract for local inference.
The book explains GGUF from the inside out: file structure, embedded metadata, version boundaries, architecture tags, tokenizer packaging, and compatibility diagnostics. It then follows the full production path from upstream training formats through conversion, quantization, requantization risk, and validation, using llama.cpp as the reference toolchain and extending the discussion to runtime loading, distribution, serving, and Ollama Modelfile workflows. Readers will learn how to choose quantization schemes for real hardware budgets, preserve semantic integrity during conversion, and build artifact lineages that remain reproducible and debuggable over time.
Rather than treating model files as disposable binaries, this book treats them as governed software products. Its emphasis on provenance, naming, failure analysis, and long-term compatibility makes it especially useful for engineers building repeatable local inference systems, internal model registries, or deployable model products from quantized open-weight LLMs.
