arXiv Machine Learning September 22, 2026 By Peiran Wang, Anqi Wang, Jiaying Zhao, Huiwen Yang, Zhenyu Ming, Rongqian Wang, Yiwu Yao, Kun Tian, Xin Yao, Gong Zhang, Fan Yang, Zhongyi Huang

PRQuant: Permutation Residual Quantization for Low-Overhead Inference

Read the original on arXiv Machine Learning →

PRQuant introduces a training‑free, low‑overhead method for low‑bit quantization of linear layers by permuting input channels that cause the largest quantization error into contiguous tail blocks and precomputing residual weight sub‑tensors. The approach eliminates the need for online gathering during inference, converting scattered residual compensation into a regular tail‑augmented GEMM and thereby reducing latency. Experiments show that PRQuant lowers down‑projection reconstruction error and outperforms standard MXFP4 and other post‑training quantization baselines on five downstream benchmarks, improving accuracy by up to 1.24 points on Qwen3‑4B‑Instruct‑2507.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

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arXiv Machine Learning

Jun 8

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arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.

By Beshr IslamBouli, David Jin

llms efficiency

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