From-Scratch Quantization Lesson Teaches FP8, GPTQ, AWQ, GGUF

This is the right kind of quantization tutorial: practical, implementation-heavy, and tied to real deployment tradeoffs instead of abstract compression theory.

• –Strong coverage from bit-level formats to modern LLM deployment formats, which makes it useful for builders who want both intuition and code.
• –The runnable NumPy approach is the main value-add: readers can inspect the math, tweak parameters, and see quality loss directly.
• –GPTQ, AWQ, and GGUF are the right techniques to compare because they represent the main post-training paths people actually use.
• –The lesson is especially good as a bridge between “I know what quantization is” and “I can decide which scheme fits my model and hardware.”
• –Some of the performance claims are hardware- and model-dependent, so the lesson works best when readers treat the numbers as illustrative rather than universal.