SINQ is a fast and model-agnostic quantization technique that enables the deployment of large language models on GPUs with limited memory while maintaining accuracy. It significantly reduces memory requirements and quantization time, offering improved model quality compared to existing methods. The technique introduces dual scaling to enhance quantization stability, allowing users to quantize models quickly and efficiently.