The article provides a detailed analysis of the computational power requirements for large-scale models with billions of parameters. Taking the Chinese large model "Yuan 1.0" developed by Inspur Information as an example, it utilizes 266 servers each equipped with 8 A100 GPUs, achieving a single-card computational efficiency of 44%. The model employs a three-dimensional parallel strategy combining tensor parallelism, pipeline parallelism, and data parallelism. The article suggests that to enhance the performance of large models, optimization is needed in multiple aspects including the framework, I/O, and communication. Compared to GPT-4, domestic large models in China still have significant gaps in terms of computational power, algorithms, and data. It is necessary to continue increasing efforts in technological research and development to improve the performance of large models.