A Comprehensive Study on Post-Training Quantization for Large Language Models. - Zhewei Yao; Cheng Li 001; Xiaoxia Wu; Stephen Youn; Yuxiong He (CoRR, 2023)

ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers. - Zhewei Yao; Reza Yazdani Aminabadi; Stephen Youn; Xiaoxia Wu; Elton Zheng; Yuxiong He (CoRR, 2023)

ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks. - Xiaoxia Wu; Haojun Xia; Stephen Youn; Zhen Zheng; Shiyang Chen 004; Arash Bakhtiari; Michael Wyatt; Reza Yazdani Aminabadi; Yuxiong He; Olatunji Ruwase; Leon Song; Zhewei Yao (CoRR, 2023)

Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation. - Zhewei Yao; Xiaoxia Wu; Cheng Li 001; Stephen Youn; Yuxiong He (AAAI, 2024)

Quant-LLM: Accelerating the Serving of Large Language Models via FP6-Centric Algorithm-System Co-Design on Modern GPUs. - Haojun Xia; Zhen Zheng; Xiaoxia Wu; Shiyang Chen 004; Zhewei Yao; Stephen Youn; Arash Bakhtiari; Michael Wyatt; Donglin Zhuang; Zhongzhu Zhou; Olatunji Ruwase; Yuxiong He; et al. (USENIX ATC, 2024)

FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design. - Haojun Xia; Zhen Zheng; Xiaoxia Wu; Shiyang Chen 004; Zhewei Yao; Stephen Youn; Arash Bakhtiari; Michael Wyatt; Donglin Zhuang; Zhongzhu Zhou; Olatunji Ruwase; Yuxiong He; et al. (CoRR, 2024)