Huawei's new open source technique shrinks LLMs to make them run on less powerful, less expensive hardware
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 Apache 2.0 license, allowing organizations to take and use it, modify it, and deploy it commercially — all for free.Across models of different sizes, SINQ cuts memory usage by 60–70%, depending on architecture and bit-width. This enables models that would previously require >60 GB of memory [...]
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