arXiv AI
Oct 31

A Unified Hessian-Guided Framework for Post-Training Quantization

The paper introduces a unified Hessian-guided framework for post‑training quantization of large language models. By deriving a Hessian‑based approximation of output changes, it jointly optimizes 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.

By Mads Toftrup, Marco Federici, Boris van Breugel, Paul Whatmough, Markus Nagel
arXiv Machine Learning
Jul 24

KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers

arXiv:2607. 21446v1 Announce Type: new Abstract: Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-bit formats cannot represent.

By Yann Bouquet, Alireza Khodamoradi, Kristof Denolf, Mathieu Salzmann