Core Insights - The article highlights a significant shift in the procurement strategy of AI computing power by top universities, moving from traditional hardware specifications to practical performance and compatibility with mainstream AI models like DeepSeek and Qwen [1][10]. Procurement Strategy - The procurement document emphasizes the importance of practical performance and compatibility over mere hardware specifications, marking a departure from the previous focus on "parameter stacking" [3][10]. - The core requirements include a focus on domestic production, ensuring real-world performance, and compatibility with existing CUDA ecosystems [3][10]. Technical Specifications - The procurement specifies the need for 13 GPU servers, with detailed requirements for CPU, memory, storage, and network capabilities, emphasizing the use of domestic components [4][6][8]. - Key performance indicators include the ability to run specific AI models under defined conditions, such as maintaining low latency and high throughput during inference tasks [9][11]. Trends in AI Computing Power Procurement - The article identifies three major trends in GPU server procurement by universities: 1. Transitioning from "indicator comparison" to "model testing" [10]. 2. Moving from a "NVIDIA-dominated ecosystem" to a "domestic compatible ecosystem" [10]. 3. Shifting focus from "hardware procurement" to "computing power system construction" [11]. Market Implications - The procurement strategy indicates a growing demand for domestic AI computing solutions that can effectively replace imported technologies, thereby fostering the development of a robust domestic AI ecosystem [12]. - Companies like Haiguang, Biran, and Muxi are highlighted as potential suppliers capable of meeting these stringent requirements, showcasing advancements in their GPU and CPU technologies [11][12].