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The definitive engineering reference for 4-bit quantization of large language models — from 70B all the way to 400B parameters. Written for the people who actually have to fold these models onto real hardware and keep them fast, accurate, and deployable.

This is not an overview. It is a deep technical field manual covering the full quantization stack: the math, the algorithms, the hardware setup, the calibration process, the runtime kernels, and the memory tricks that make the difference between a model that runs and a model that doesn't.

What's inside

Technical scope and core engineering objectives of quantization for 70B-400B class models
Mathematical foundation of Floating Point (FP32, FP16, BF16) vs Integer (INT8, INT4) representations
Quantization-aware outlier analysis for 70B+ parameter models and why they break naive schemes
High-performance computing environment setup, including multi-node H100 and A100 configurations
Linux kernel parameter tuning and NVMe scratch space for massive weight-sharding during quantization
Round-to-Nearest (RTN) quantization deep dive and its limitations at scale
GPTQ (Optimal Brain Quantization) theory and Hessian-based error compensation, implemented
Activation-aware Weight Quantization (AWQ) and preserving salient weights
GGUF and K-Quants mechanics for cross-platform deployment
EXL2 format deconstructed, with variable bitrate strategies for Llama-3 class models
QuIP# and the application of randomized Hadamard transforms to minimize quantization error
HQQ (Half-Quadratic Quantization) for fast, calibration-free 4-bit compression
Calibration dataset selection (The Pile, C4) for accurate weight distribution recovery
Layer-wise error tracking pipelines using KL-Divergence and Mean Squared Error
Offloading strategies for quantizing 400B models on limited VRAM via CPU-RAM swapping
Breaking the Memory Wall: optimizing CUDA kernels for 4-bit dequantization at runtime
QLoRA (Quantized Low-Rank Adaptation) for fine-tuning 4-bit artifacts
Double Quantization techniques to shrink the footprint of quantization constants themselves
KV-Cache quantization to 4-bit and 8-bit for long-context inference
And more — densely technical, cover to cover

Who this is for

ML engineers deploying local LLMs at scale, research engineers at AI startups, contractors compressing models for clients, and serious practitioners who are tired of scattered arXiv papers and want the full quantization stack in one place, with the math and the code.

Format

Delivered as PDF and TXT. Read the PDF cover to cover, or feed the TXT straight into your own local RAG, grep it, index it, pipe it into your local LLM. The book about quantization, ready to be consumed by a quantized model. + EXTRAS
PAGES 112

A note on responsibility

This material is provided for educational and research purposes. You are responsible for complying with the licenses of the models and tools you deploy, and with the laws of your jurisdiction. Build carefully and build for the right reasons.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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