Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

This article introduces Intel's AutoRound, a new quantization technique designed to improve the efficiency of Large Language Models (LLMs) and Vision-Language Models (VLMs). The focus is on optimizing these models, likely to reduce computational costs and improve inference speed. The article probably highlights the benefits of AutoRound, such as improved performance or reduced memory footprint compared to existing quantization methods. The source, Hugging Face, suggests the article is likely a technical deep dive or announcement related to model optimization and hardware acceleration.

• •AutoRound is a new quantization technique from Intel.
• •It is designed for LLMs and VLMs.
• •The goal is likely to improve efficiency and performance.