Model Optimization Generative AI
LLM Quantization
Cookbook
A hands-on practical guide to quantizing Large Language Models — GPTQ, AWQ, bitsandbytes, and GGUF covered with theory, code, and benchmarks. Run 70B models on consumer hardware.
4×
Memory Reduction
4+
Quant Methods
8
Practical Chapters
Quantization Methods Covered
Practical comparison and benchmarking of industry-leading methods
GPTQ
4-bit2–3×
Post-training quantization using second-order Hessian information
AWQ
4-bit3–4×
Activation-aware Weight Quantization — protects salient weights
bitsandbytes
8-bit / 4-bit2×
Drop-in 8-bit & 4-bit quantization via Hugging Face integration
GGUF
2–8 bit4×+
Unified file format for distributing quantized models on edge devices
Tools Used
Python
Hugging Face
GPTQ
AWQ
bitsandbytes
llama.cpp / GGUF
Cookbook Contents
Hands-on notebooks from fundamentals to production
01Introduction to Quantization & Why It Matters
02Post-Training Quantization (PTQ) vs Quantization-Aware Training (QAT)
03GPTQ: Precise Quantization with Second-Order Information
04AWQ: Protecting the Most Important Weights
05bitsandbytes: Zero-Effort Quantization with Hugging Face
06GGUF & llama.cpp: Edge and Local Deployment
07Quality Metrics: Perplexity, MMLU, and Holistic Evaluation
08Choosing the Right Quantization Method for Production
Run 70B Models on Consumer Hardware
Fully open-source with Jupyter notebooks and production-ready code examples.
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