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Understanding Quantization: GGUF, AWQ, and GPTQ Explained

Demystifying LLM quantization formats. Learn how GGUF, AWQ, and GPTQ shrink giant AI models so they can run on consumer hardware.

July 28, 20266 min read1,063 views
Understanding Quantization: GGUF, AWQ, and GPTQ Explained
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What is Quantization?

Quantization reduces the precision of a model's weights (e.g., from 16-bit to 4-bit). This slashes memory requirements by 75% while retaining 95% of the model's intelligence.

GGUF: The CPU King

Designed for Llama.cpp, GGUF is perfect for running models on CPUs and MacBooks. It supports mixed quantization and is highly portable.

AWQ and GPTQ: GPU Warriors

If you are deploying on Nvidia GPUs, AWQ (Activation-aware Weight Quantization) and GPTQ offer incredibly fast generation speeds and are supported natively by vLLM.

Frequently Asked Questions

Does quantization make models dumber?+
At 4-bit or 5-bit, the degradation is often imperceptible for general tasks. Going down to 2-bit or 3-bit can cause significant quality loss.

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