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

arXiv:2609.36120v1 Announce Type: cross Abstract: Learned rotations play an important role in enabling low-bit weight and activation quantization of large language models by smoothing outliers in the activation distribution. State-of-the-art approaches include