Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality. The technique, called SINQ (Sinkhorn-Normalized Quantization), is designed to be fast, calibration-free, and easy to integrate into existing model workflows. The code for performing it has been made available by the Huawei research team on Github and Hugging Face under a permissive, enterprise-friendly ...