When the company was founded 32 years ago, Nvidia was primarily known among computer gamers. For decades, it was the leading manufacturer of powerful graphics processing units (GPUs) that brought immersive 3D worlds to life in games and visual applications. Over time, it became clear that these same GPUs were not only useful for graphics, but also highly efficient at performing parallel mathematical calculations, making them ideal for training artificial intelligence models.
This dual role — graphics and high-performance computing — propelled Nvidia to the center of the AI revolution. The surging global demand for chips used to train and run large-scale machine learning models has driven Nvidia’s market value to extraordinary levels, placing the company among the most valuable in the world. At the same time, its technology has become a strategic asset in current geopolitical rivalries, particularly between the United States and China in the race for AI dominance.
In recent years, the U.S. government introduced strict export controls on advanced AI chips, limiting the sale of Nvidia’s most powerful GPUs to the Chinese market. These measures were designed to restrict access to technologies that could be used for military or strategic purposes. However, in 2025, the U.S. administration partially relaxed these restrictions, allowing the export of certain high-performance chips to China under specific conditions, signaling a shift from the previous hard-line stance. In response, China has accelerated efforts to build a more self-sufficient
AI semiconductor industry, supporting a growing number of domestic companies aiming to compete in the AI chip market. Among the most notable are: Cambricon Technologies, focused on designing AI accelerators and chips for both training and inference, with the goal of reducing reliance on foreign technology. MetaX Integrated Circuits, an emerging competitor developing GPUs and AI accelerators optimized for deep learning workloads. Moore Threads, founded by industry veterans, working on high-performance GPUs tailored for AI and intensive computing tasks.
Biren Technology, another designer of GPU-like AI accelerators with growing market ambitions. Kunlunxin, the AI chip division of a major Chinese technology company, focused on hardware for servers and AI infrastructure. Enflame Technology, developing AI accelerators and data-center solutions. Iluvatar CoreX, producing parallel-architecture chips designed for specialized AI workloads.
These Chinese companies are steadily gaining ground in specific segments of the artificial intelligence market, particularly within China, where they benefit from strong institutional and state support. While many still face challenges in fully matching Nvidia’s performance and global ecosystem, their rapid growth represents a meaningful competitive threat that could reshape the global AI chip landscape in the years ahead.
