The paper introduces a unified Hessian-guided framework for post-training quantization of large language models. It derives a Hessian-based approximation of output changes due to joint weight and activation quantization, enabling simultaneous optimization of quantization ranges, linear transformations, and weight rounding under a single output-sensitivity objective. The method remains practical for modern models and consistently outperforms existing transformation and rounding techniques in both channel-wise and block-wise settings.
arXiv:2606. 10890v1 Announce Type: cross Abstract: Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations.
By Juan Amboage, Pablo Monteagudo-Lago, Ian Colbert, Giuseppe Franco, Nicholas Fraser
arXiv:2607. 07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining.
By Donghyun Lee, Yuhang Li, Ruokai Yin, Priyadarshini Panda
The paper examines weight-only post‑training quantization (PTQ) and finds that minimizing reconstruction loss does not always improve model performance, sometimes even harming it on calibration data. It shows that weights with low reconstruction loss on one distribution can perform worse when activation distributions shift. To address this, the authors introduce Distributionally Robust Quantization (DRQ), a refinement step that minimizes worst‑case reconstruction loss over a constrained set of activation distributions while keeping the quantization grid unchanged. Experiments demonstrate that DRQ improves models quantized by several PTQ methods across dense and mixture‑of‑experts large language models, offering better downstream performance without extra inference cost.
By Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu, Baihua He, Xinyu Zhang, Harrison Bo Hua Zhu, Wenlong Chen, Li Zeng, Zhuo Sun
arXiv:2606. 13054v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment.
By Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Xing Hu, Zhe Jiang, Dawei Yang
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.
G$^2$PTQ is a post‑training quantization framework that improves large language models by combining first‑ and second‑order information in a globally supervised, block‑wise optimization. It refreshes gradient and Hessian estimates before each Transformer block and uses a trust‑region scaling mechanism to stabilize gradient steps, preventing exploding weight updates. The method achieves better alignment with full‑precision models and outperforms state‑of‑the‑art baselines across various model families and bit‑widths.
By Ruikang Liu, Haoli Bai, Yuxuan Sun, Qian Zhang, Wenzheng Cai, Yanqi Hao, Feiyu Wang, Weidong Zhong, Zhuang Wang, Tong Yang, Xiangsheng Zhou
The paper introduces DASH-Q, a post‑training quantization method that uses a diagonal Hessian approximation and iterative weighted least squares to reduce noise in curvature estimates. By discarding noisy cross‑channel dependencies, DASH‑Q preserves salient feature power and achieves superior performance in ultra low‑bit quantization. Across five large language models, it improves zero‑shot accuracy by an average of 7.01% and up to 14.01% over the strongest baselines, even with very small calibration datasets.
By Jaemin Kim, Sungkyun Kim, Junyeol Lee, Jiwon Seo
arXiv:2605.11222v2 Announce Type: replace
Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (...
By Ryan Lucas, Mehdi Makni, Xiang Meng, Adam Deng, Rahul Mazumder
The paper presents a comprehensive evaluation of post‑training quantization (PTQ) techniques for large language models (LLMs), addressing gaps in prior work by conducting fair, large‑scale experiments on text‑only LLMs. It decouples quantization into pre‑quantization transformation and error mitigation, systematically studies granularity, symmetry, and data formats (MXFP4, NVFP4), and finds that rotation‑based transformations and low‑rank compensation with GPTQ can improve performance, while finer granularity and asymmetric quantization benefit activations more than weights. The study highlights the importance of understanding component interactions and the impact of scaling factor formats on low‑bit quantization performance.
By Yutong Liu, Cairong Zhao, Guosheng Hu
InfoQuant is a train‑free method that reshapes activation distributions to be more compatible with low‑bit quantizers. It uses Peak Suppression Orthogonal Transformation (PSOT) and adaptive outlier‑token selection to reduce numerical range while maintaining dispersion, thereby lowering quantization error. Across several large language model families, InfoQuant outperforms existing post‑training quantization and end‑to‑end training baselines, preserving 97% of floating‑point accuracy on average and narrowing the LLaMA‑2 13B performance gap by 42%.
By Ke Li, Dong An, Xiaoling Zang, Can Ye, Liang Xie, Qibo Qiu, Chen Shen, Xiaofei He, Wenxiao Wang
arXiv:2505. 01043v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive performance across various domains.
By Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo, Han Hu, Guoxia Wang, Dianhai Yu, Yonggang Wen, Dacheng Tao