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
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:2610.08164v1 Announce Type: new
Abstract: Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-$r$ ada...
By Seobin Song, Geonho Lee, Janghwan Lee, Jungwook Choi
arXiv:2608. 14149v1 Announce Type: new Abstract: Recent training-free post-training quantization methods restore model accuracy through closed-form residual compensation.
By Lin-Fa Lee, Yi-Yu Chang, Kuo-Hei Yeh
DualQuant introduces a dual‑scale, calibration‑free post‑training quantization framework that jointly optimizes weight‑reconstruction MSE and derives per‑channel activation rescalings, enabling effective 4‑bit weight and activation quantization without calibration data. The method employs a non‑differentiable optimization solved via variable splitting and block coordinate descent, supporting a wide range of formats including MX and NVFP4. Experiments on Llama and Qwen models show DualQuant matches or surpasses calibration‑based SmoothQuant and significantly outperforms other calibration‑free baselines, achieving up to 2.61× memory reduction and 2.46× throughput speedup on an RTX 4090.
By Mahsa Salmani, Sayeh Sharify, Hesham Mostafa
Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-$r$ adapter beside each frozen quantized weight, without...