Domestic AI large models have made significant strides in overcoming bottlenecks such as computational power and data security, thanks to a supportive policy environment and robust capital investment. Techniques like model compression, parallel computing, and distributed data processing have substantially improved computational efficiency. At the same time, the incorporation of advanced encryption protocols, secure data management systems, and rigorous compliance with regulatory standards ensures that data security is not compromised. These technological enhancements, underpinned by collaborative efforts among research institutions and government agencies, create a solid foundation for continuous innovation. This dual-focus strategy not only mitigates existing limitations but also positions domestic AI solutions as formidable players in both performance and security on a global scale.