
Activation-aware Weight Quantization (AWQ) is a hardware-friendly approach for low-bit weight-only quantization of large language models (LLMs) that protects the most salient weights to significantly reduce quantization error without relying on backpropagation or reconstruction.


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Activation-aware Weight Quantization for On-Device Large Language Model Compression and Acceleration


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Quantization is a crucial technique for making large language models more efficient and deployable across diverse hardware platforms by reducing their memory footprint while maintaining similar performance levels.


5 Key Points to Unlock LLM Quantization

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Unlocking the Efficiency of Large Language Models through Quantization: A Practical Guide
