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AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models

arXiv 2311.01305 ↗First posted 2 Nov 2023cs.LG cs.AI cs.CLcited by 0

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Abstract (arXiv)

Large language models(LLMs) exhibit excellent performance across a variety of tasks, but they come with significant computational and storage costs. Quantizing these models is an effective way to alleviate this issue. However, existing methods struggle to strike a balance between model accuracy and hardware efficiency. This is where we introduce AWEQ, a post-training method that requires no additional training overhead. AWEQ excels in both ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization. There is an observation that weight quantization is less challenging than activation quantization. AWEQ transfers the difficulty of activation quantization to weights using channel equalization, achieving a balance between the quantization difficulties of both, and thereby maximizing performance. We have further refined the equalization method to mitigate quantization bias error, ensuring the robustness of the model. Extensive experiments on popular models such as LLaMA and OPT demonstrate that AWEQ outperforms all existing post-training quantization methods for large models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 2 Nov 2023.

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References · 14

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  3. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  5. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  6. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  7. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  8. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,298
  9. 2022LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scalecited by 906
  10. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 621
  11. 2022ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformerscited by 526
  12. 2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 479
2 more
  1. 2019Data-Free Quantization Through Weight Equalization and Bias Correctioncited by 336
  2. 2022Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruningcited by 322

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  2. 2026RUQuant: Towards Refining Uniform Quantization for Large Language Models
  3. 2026SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
  4. 2023ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
  5. 2024LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
  6. 2025Achieving binary weight and activation for LLMs using Post-Training Quantization
  7. 2025SiLQ: Simple Large Language Model Quantization-Aware Training
  8. 2025PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models