高质量大模型基础设施研究报告（2024年）

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The rapid development of large model technology is driving the intelligent transformation across various industries, necessitating high-quality infrastructure to support model training, deployment, and application [6][12] - Current large model infrastructure faces challenges such as low availability and poor stability, which need to be addressed through multi-layer optimization across computing, networking, storage, software, and operations [6][27] - The report identifies five core capability areas for large model infrastructure: computing, storage, networking, development toolchain, and operations management [6][12] Summary by Sections Overview of Large Model Infrastructure - Large model infrastructure refers to the hardware and software resources that support the training, deployment, and application of large-scale AI models [13] - The infrastructure must possess high availability, high performance, scalability, and evaluability to meet the demands of large model applications [15][22] Current Status of Large Model Infrastructure - Technological advancements in AI storage and networking are improving infrastructure availability and communication efficiency [23][24] - Major tech companies like Amazon, Microsoft, and Google dominate the large model infrastructure ecosystem, integrating computing, platforms, models, and software [24] - Governments are increasing funding to promote the development of AI data center infrastructure [25][26] Challenges in Large Model Infrastructure - Low availability of large model infrastructure clusters and inefficient resource allocation are significant challenges [27][31] - Data processing inefficiencies and storage bottlenecks hinder the performance of large models [33][34] - Network communication issues arise as the scale of parallel computing increases, impacting training efficiency [37][39] Key Technologies for Large Model Infrastructure - Efficient computing resource management and scheduling technologies are essential for optimizing resource utilization [49][50] - High-performance storage technologies, such as KV-cache, enhance the efficiency of model inference [51][53] - Advanced networking technologies improve service stability and address communication bottlenecks in large model training [56][58]