万卡级智算集群网络建设运维及演进

Investment Rating - The report does not explicitly state an investment rating for the industry. Core Insights - The development of cognitive intelligent large models is expected to enable machines to truly understand and utilize human language and knowledge, marking a significant step towards general artificial intelligence [4][10]. - The report highlights the rapid advancements in large models, with significant improvements in capabilities such as multi-modal interaction and reasoning abilities, particularly in comparison to previous models like GPT-3.5 and GPT-4 [9][13]. - The infrastructure for intelligent computing clusters is becoming increasingly complex, with a focus on optimizing load balancing, fault localization, and ensuring the stability of optical connections [17][30]. Summary by Sections Large Model Fundamentals - The report discusses the evolution of large models, including the introduction of the iFlytek Spark large model and its parallel training methods, emphasizing the emergence of cognitive models that can facilitate human-like learning [4][10]. - The report notes that the release of ChatGPT led to over 100 million monthly active users within two months, showcasing the rapid adoption of advanced AI technologies [4]. Intelligent Computing Cluster Operations - The report details the operational challenges faced by large computing clusters, including the need for stable optical connections, load balancing optimization, and efficient fault localization [17][36]. - It mentions the deployment of over 10,000 computing acceleration cards and 30,000 optical fibers in the iFlytek cluster, indicating the scale of infrastructure required [17]. Network Architecture and Optimization - The report outlines the evolution of network architecture for large-scale clusters, comparing different topologies such as Fat Tree and Dragonfly, and their implications for scalability and performance [55][56]. - It emphasizes the importance of adaptive routing and enhanced hashing algorithms to address load balancing issues in intelligent computing networks [30][31]. Fault Analysis and Maintenance - The report provides insights into the root causes of faults in large models, highlighting issues related to hardware components and the complexity of managing extensive optical networks [27][36]. - It discusses the establishment of a unified maintenance platform that enables rapid fault diagnosis and recovery, crucial for maintaining the performance of AI training tasks [38][39].