Why it matters

AWQ is often the top choice for INT4 quantization now. Understanding it complements GPTQ knowledge.

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The architecture

Analyze activations: find channels with large activation magnitudes.

Protect corresponding weights: scale them up before quantization so quantization error is smaller relative to their value.

AWQ approachAnalyze activationsfind importantScale important weightsprotect precisionQuantize normallyINT4Scaling before quantization concentrates precision on impactful weights
AWQ steps.
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How it works end to end

Salient weights: those in channels with large activations dominate output. Preserving them preserves quality.

Grouped scales: still uses per-group scales for storage efficiency.

No optimization: unlike GPTQ, doesn't solve per-layer optimization. Simpler.

Quality: often matches or beats GPTQ for LLMs.