Investment Rating - The report maintains a "Positive" outlook for the computer industry, expecting the industry index to outperform the market index by over 5% in the next six months [42]. Core Insights - The computer sector index rose by 2.12% from July 14 to July 18, outperforming the CSI 300 index by 1.03 percentage points, ranking 8th among all sectors [13][16]. - The introduction of NVIDIA's GB300 server, based on the Blackwell Ultra architecture, marks a significant advancement in AI computing capabilities, with a peak performance of 1.1 ExaFLOPS, representing an 11-fold increase in inference speed compared to the previous Hopper architecture [2][19]. - The GB300's production capacity is projected to reach 2,000-2,500 units per month in Q2 2025, with total production expected to be between 5,000-6,000 units, driven by demand from major cloud service providers [2][21]. Summary by Sections Market Performance - The computer sector index increased by 2.12% during the week, while the CSI 300 index rose by 1.09%, indicating strong sector performance [13][16]. - Top gainers included Xiling Information (+27.08%), ST Cube (+26.86%), and Yanhua Intelligent (+24.04%), while major losers were Dazhihui (-17.51%), Jinzhen Shares (-10.85%), and Jingbeifang (-10.61%) [16][17]. Technology Progress and Industry Impact - The GB300 server integrates advanced technologies, including a modular liquid cooling system and NVLink 5.0, which allows for high-density GPU deployment [2][19]. - The GB300's liquid cooling technology is expected to increase its penetration from approximately 10% in 2024 to over 30% in 2025, particularly in high-end AI training servers [7][28]. Supply Chain Dynamics - The production of GB200 and GB300 servers is driving significant adjustments in the global supply chain, with major manufacturers like Foxconn and Quanta ramping up production [21][26]. - North America's major cloud providers, including Microsoft, Google, AWS, and Meta, are the primary purchasers of these servers, with significant orders also coming from Middle Eastern sovereign AI projects [21][26]. Investment Opportunities - The demand for high-end optical modules is expected to surge due to the GB300's introduction, with the 1.6T optical module's transmission speed doubling that of the 800G version and power consumption decreasing by 30%-35% [4][26]. - The PCB industry is also set to benefit from the GB300's rollout, with a shift towards higher-layer boards to support increased computing density [4][28].

Investment Rating - The report maintains a "Positive" investment rating for the supernode industry, driven by the explosive growth of model parameters and the shift in computing power demand from single points to system-level integration [3]. Core Insights - The supernode trend is characterized by a dual expansion of high-density single-cabinet and multi-cabinet interconnection, balancing communication protocols and engineering costs [4][5]. - Domestic supernode solutions, represented by Huawei's CloudMatrix 384, achieve a breakthrough in computing power scale, surpassing single-card performance limitations [4][5]. - The industrialization of supernodes will reshape the computing power industry chain, creating investment opportunities in server integration, optical communication, and liquid cooling penetration [4][5][6]. - Current market perceptions underestimate the cost-performance advantages of domestic solutions in inference scenarios and overlook the transformative impact of computing network architecture on the industry chain [4][7]. Summary by Sections 1. Supernode: New Trends in AI Computing Networks - The growth of large model parameters and architectural changes necessitate understanding the two dimensions of computing power expansion: Scale-up and Scale-out [15]. - Scale-up focuses on tightly coupled hardware, while Scale-out emphasizes elastic expansion to support loosely coupled tasks [15][18]. 2. Huawei's Response to Supernode Challenges - Huawei's CloudMatrix 384 represents a domestic paradigm for cross-cabinet supernodes, achieving a computing power scale 1.7 times that of NVIDIA's NVL72 [4][5][6]. - The design of supernodes must balance model training and inference performance with engineering costs, particularly in multi-GPU inference scenarios [69][77]. 3. Impact on the Industry Chain - The industrialization of supernodes will lead to a more refined division of labor across the computing power industry chain, with significant implications for server integration and optical communication [6][4]. - The demand for optical modules driven by Huawei's CloudMatrix is expected to reach a ratio of 1:18 compared to GPU demand [6]. 4. Key Company Valuations - The report suggests focusing on companies involved in optical communication, network devices, data center supply chains, copper connections, and AI chip and server suppliers [5][6].

Core Viewpoint - The article discusses the emergence and significance of Super Nodes in addressing the increasing computational demands of AI, highlighting their advantages over traditional server architectures in terms of efficiency and performance [4][10][46]. Group 1: Definition and Characteristics of Super Nodes - Super Nodes are defined as highly efficient structures that integrate numerous high-speed computing chips to meet the growing computational needs of AI tasks [6][10]. - Key features of Super Nodes include extreme computing density, powerful internal interconnects using technologies like NVLink, and deep optimization for AI workloads [10][16]. Group 2: Evolution and Historical Context - The concept of Super Nodes evolved from earlier data center designs focused on resource pooling and space efficiency, with significant advancements driven by the rise of GPUs and their parallel computing capabilities [12][13]. - The transition to Super Nodes is marked by the need for high-speed interconnects to facilitate massive data exchanges between GPUs during model parallelism [14][21]. Group 3: Advantages of Super Nodes - Super Nodes offer superior deployment and operational efficiency, leading to cost savings [23]. - They also provide lower energy consumption and higher energy efficiency, with potential for reduced operational costs through advanced cooling technologies [24][30]. Group 4: Technical Challenges - Super Nodes face several technical challenges, including power supply systems capable of handling high wattage demands, advanced cooling solutions to manage heat dissipation, and efficient network systems to ensure high-speed data transfer [31][32][30]. Group 5: Current Trends and Future Directions - The industry is moving towards centralized power supply systems and higher voltage direct current (DC) solutions to improve efficiency [33][40]. - Next-generation cooling solutions, such as liquid cooling and innovative thermal management techniques, are being developed to support the increasing power density of Super Nodes [41][45]. Group 6: Market Leaders and Innovations - NVIDIA's GB200 NVL72 is highlighted as a leading example of Super Node technology, showcasing high integration and efficiency [37][38]. - Huawei's CloudMatrix 384 represents a strategic approach to achieving competitive performance through large-scale chip deployment and advanced interconnect systems [40].