In today's rapid development of Large Language Models (LLM), the training and inference costs of models have increasingly become a focus of research and application. Recently, the Tencent Hunyuan team released an important study that delves into the 'Scaling Laws' of low-bit floating point quantization training, which refers to the principles governing the scale of floating point quantization training. The core of this research lies in exploring how to significantly reduce computational and storage costs without sacrificing performance by lowering the precision of the model.

According to the CEO of Anthropic, the current cost of training AI models is as high as $10 billion, and it could rise to $100 billion or even $1 trillion in the next three years. This forecast has sparked concerns about whether the AI bubble is about to burst.Image Source Note: The image was generated by AI, provided by the image authorization service provider Midjourney Dario Amodei points out that as AI models continue to evolve, hardware demand will also grow exponentially, becoming a major