AutoRound

Intel's AutoRound is an advanced quantization algorithm for compressing large AI models, such as LLMs and vision language models (VLMs), with minimal loss of accuracy. It reduces model size to ultra-low bit widths (2–4 bits) using sign-gradient descent optimization and applies mixed bit widths across layers for optimal efficiency. This quantization process is also remarkably fast: You can quantize a 7-billion-parameter model in just minutes on a single GPU. Since AutoRound integrates with popular inference engines such as vLLM and Transformers, it's an attractive option for quantizing models.