Core Insights - The recent WAIC highlighted the growing interest in supernodes, with companies like Huawei, ZTE, and H3C showcasing their advancements in this technology [3][4][5] - Supernodes are essential for managing large-scale AI models, enabling efficient resource utilization and high-performance computing [3][4][5] - The shift from traditional AI servers to supernode architectures is driven by the increasing complexity and size of AI models, which now reach trillions of parameters [4][5][9] Group 1: Supernode Technology - Supernodes integrate computing resources to create low-latency, high-bandwidth computing entities, enhancing the efficiency of AI model training and inference [3][4] - The technology allows for performance improvements even when individual chip manufacturing processes are limited, making it a crucial development in the industry [4][9] - Companies are exploring both horizontal (scale out) and vertical (scale up) expansion strategies to optimize supernode performance [5][9] Group 2: Market Dynamics - Domestic AI chip manufacturers are increasing their market share in AI servers, with the proportion of externally sourced chips expected to drop from 63% to 49% this year [10] - Companies like墨芯人工智能 are adopting strategies that focus on specific AI applications, such as inference optimization, to compete with established players like NVIDIA [10][11] - The competitive landscape is shifting, with firms like云天励飞 and后摩智能 targeting niche markets in edge computing and AI inference, avoiding direct competition with larger chip manufacturers [11][12][13] Group 3: Technological Innovations - The introduction of optical interconnects in supernode technology is a significant advancement, providing high bandwidth and low latency for AI workloads [6][9] - Companies are developing solutions that leverage optical communication to enhance the performance of AI chip clusters, addressing the limitations of traditional electrical interconnects [6][9] - The focus on sparse computing techniques allows for lower manufacturing process requirements, enabling more efficient AI model computations [11][12]