Tutorials on Activation Aware Weight Quantization

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AWQ in Practice: Quantize LLMs for Faster AI Inference Without Killing Output Quality

AWQ cuts 70B model serving costs from $0.757 to $0.253 per million tokens by moving from two GPUs to one. Llama 3.3 70B quantized to INT4 with AWQ drops 2 MMLU points and 3 HumanEval points versus full precision. AWQ INT4 brings a 70B model's VRAM down from 140GB to roughly 40GB, fitting it on a…
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What Is AWQ in LLM Quantization and How It Works

AWQ stands for activation-aware weight quantization. It scales the most influential weight channels based on offline activation statistics, then quantizes everything else to ultra-low bit widths. By protecting a small fraction of salient weights, it keeps quantization error down without…
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