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.

The article mainly discusses the computational and storage challenges in the field of artificial intelligence, particularly regarding large language models (LLMs). With the enhancement of AI capabilities, such as the emergence of the Bloom model, the required computational resources and storage space have significantly increased, leading to high costs and accessibility issues. To tackle this problem, researchers have introduced 'quantization' techniques to reduce the model size and accelerate the running speed by lowering the precision of model weights and activations, while controlling potential accuracy loss. The article highlights the collaboration between Beihang University and SenseTime in developing this tool.