In recent years, generative artificial intelligence represented by large language models (LLMs) and multimodal large language models (MLLMs) has achieved remarkable progress. These ultra-large-scale and complex models impose extremely high demand on computational resources, driving an urgent need for the development of high-density computing power. From the perspective of the technological advancement of LLMs, this study explores the development stages of LLMs, computational optimization techniques, and their characteristics in terms of computing power requirements. Focusing on the demand of LLMs for computing power, this study analyzes the implications and characteristics of high-density computing power, its current development status, and key components. It further identifies five major challenges faced by the development of high-density computing power in China, including supply chain security risks, bottlenecks at the hardware layer, incomplete software stacks with high external dependency, the power wall and energy security, as well as cybersecurity risks. In response, this study proposes several strategies for high-density computing power development in China, including improving self-supporting of the supply chain, adhering to self-dependent innovation and standardization, building an open and collaborative software-hardware ecosystem, improving green computing innovation, and optimizing an integrated research and innovation system.