AI Quantization Techniques 2026

Quantization allows large models to run on consumer-grade GPUs. Among the three mainstream approaches—GGUF, GPTQ, and AWQ—the choice depends on your use case.

💡 What You Will Learn

Quantization allows large models to run on consumer-grade GPUs. Among the three mainstream approaches—GGUF, GPTQ, and AWQ—the choice depends on your use case.

📜 Table of Contents

• What is Quantization?
• Comparison of Three Quantization Methods
• GGUF — The Go-To for CPU Inference
• GPTQ — The Standard for GPU Inference
• AWQ — The Best Precision Quantization
• Selection Guide
• Summary

What is Quantization?

Quantization is the process of compressing model parameters from high precision (e.g., float16) to low precision (e.g., int4). The principle is simple: not every parameter needs 32-bit precision to be stored. After compression, the model is half the size, runs twice as fast, and typically loses less than 5% accuracy.

Comparison of Three Quantization Methods

GGUF — The Go-To for CPU Inference

GGUF is the format used by llama.cpp and the only quantization method that can efficiently run large models on CPU.

Pros: No GPU required, runs on regular computers Cons: Slower than GPU, large models (>13B) struggle on CPU

GPTQ — The Standard for GPU Inference

GPTQ is currently the most widely used GPU quantization method. Most quantized models on HuggingFace are in GPTQ format.

Pros: Fast, supports batch inference Cons: Requires CUDA environment

AWQ — The Best Precision Quantization

AWQ currently offers the least accuracy loss among quantization methods. It dynamically adjusts quantization granularity based on the importance of each parameter.

Pros: Minimal accuracy loss (<1%) Cons: Toolchain less mature than GGUF/GPTQ

Selection Guide

Summary

Use GGUF for CPU quantization, GPTQ for fast GPU deployment, and AWQ when quality matters most. For most scenarios, GPTQ 4bit offers the best value — fast enough, accurate enough, and with the most mature toolchain.