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
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
arXiv:2608. 13966v1 Announce Type: new Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality.
By Vincent Counathe, Ben Athiwaratkun, Christopher De Sa, Tianyi Zhang
As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model...
arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.
By Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen, Xin Qiu
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