arXiv Machine Learning October 3, 2026 By Chayne Thrash, Ali Abbasi, Soheil Kolouri

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

Read the original on arXiv Machine Learning →

ConQuR introduces a lightweight post‑training rotation calibration for large language model activation quantization. By learning orthogonal rotations that align normalized activations with the corners of an inscribed hypercube, the method distributes activation energy evenly and can be updated online without storing activations. Experiments on Llama‑2 and Llama‑3 models (3B–70B) show competitive or improved perplexity and reasoning performance while avoiding costly training or large offline storage.

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arXiv AI

4d ago

ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs

ThinQuant introduces efficient rotation learning for low‑bit weight and activation quantization of large language models by reducing calibration data through a geometric selection of activations and solving a lower‑dimensional optimization problem via an ADMM algorithm. The method achieves comparable or better quantization performance with dramatically fewer calibration points, completing rotation calibration for Llama‑3‑70B in under 12 minutes and for Llama‑3.1‑405B in just over 2 hours on a single GPU. ThinQuant outperforms existing gradient‑free approaches such as DartQuant and gradient‑based SpinQuant in both speed and perplexity metrics on WikiText‑2.

By Mehdi Makni, Ryan Lucas, Rahul Mazumder

llms efficiency benchmarks

Hugging Face Trending Papers

Jun 11

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

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.

llms efficiency

arXiv AI

Jun 12

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

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

llms efficiency

arXiv Machine Learning

Jun 8

AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

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

arXiv Machine Learning

Jun 3

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration

arXiv:2507. 23035v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and compute resources during inference.

By Xueying Wu, Baijun Zhou, Zhihui Gao, Yuzhe Fu, Qilin Zheng, Yintao He, Hai Li

llms efficiency

arXiv AI

Jun 8

OffQ: Taming Structured Outliers in LLM Quantization by Offsetting

arXiv:2606. 07116v1 Announce Type: cross Abstract: Low-bit quantization has been widely adopted to accelerate the inference of large language models (LLMs) by significantly reducing computational cost and memory usage.

By Haoqi Wang, Lorenz K. Mueller, Jiawei Zhuang, Mathieu Salzmann, Lukas Cavigelli

llms efficiency benchmarks

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ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs

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

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

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