丨证券研究报告丨 行业研究丨点评报告丨软件与服务 [Table_Title] 算力产业跟踪：超节点引领新一代算力基础设施 报告要点 [Table_Summary] 超节点是新一代算力基础设施，当前算力龙头企业纷纷推出超节点产品。超节点能解决大模型 训练推理中以通信速度为核心的性能瓶颈，技术升级和复杂度提升，超节点带来多个算力环节 价值量提升，建议关注产业链相关环节投资机会。 分析师及联系人 [Table_Author] 宗建树 余庚宗 SAC：S0490520030004 SAC：S0490516030002 SFC：BUX668 软件与服务 cjzqdt11111 [Table_Title 算力产业跟踪：超节点引领新一代算力基础设 2] 施 [Table_Summary2] 事件描述 全球算力产业龙头英伟达 2025 年逐渐开始主要以超节点 GB200 面向市场出货，而华为也在 2025 年推出昇腾 384 超节点，浪潮信息在 2025 开放计算技术大会发布超节点 AI 服务器"元 脑 SD200"。 事件评论 请阅读最后评级说明和重要声明 %% %% %% %% research.95579.com 1 ...

Investment Rating - The investment rating for the company is "Buy" (maintained) [6] Core Viewpoints - The company is a communication equipment giant, with significant underestimation of its AI computing and network business progress, which is expected to recreate ZTE in the AI era [1] - The company has a complete AI product ecosystem and has successfully expanded its internet customer base, with a projected revenue of 18.6 billion yuan for its government and enterprise business in 2024, representing a year-on-year growth of 37% [3] - The domestic supernode progress is exceeding expectations, with a core increment in the switching network, and the domestic supernode penetration rate is expected to accelerate [4][5] Summary by Sections Company Expectations - The company is expected to occupy a core strategic position in the supernode system, leveraging its core capabilities [2][17] AI Product Ecosystem - The company has made significant breakthroughs in servers, switches, switching chips, and self-developed chips, with products entering the internet customer ecosystem and related business segments experiencing rapid revenue growth [3] Domestic Supernode Progress - The domestic supernode market is expected to see accelerated penetration, with projections indicating a penetration rate of 5% in 2025, 19% in 2026, and 45% in 2027 [4] Scale Up Switching Network - The Scale Up switching network is identified as a core incremental segment of the supernode solution, with a projected market size of 14 billion USD by 2027 [5] Market Position - The company occupies a core position in the domestic supernode solution, with a significant gap in market recognition and expectations [10] Demand for Scale Up Switching Chips - The demand for Scale Up switching is accelerating the localization of switching chips, with domestic manufacturers gradually catching up in technology [11] Profit Forecast and Valuation - The company's net profit is projected to be 8.7 billion yuan in 2025, 9.3 billion yuan in 2026, and 10.1 billion yuan in 2027, with corresponding PE ratios of 22, 21, and 19 times [13][16]

Core Viewpoint - Inspur Information has launched the "Yuan Nao SD200," a super-node AI server designed for trillion-parameter large models, addressing the growing computational demands of AI systems [2][3]. Group 1: Product Features - The "Yuan Nao SD200" utilizes a multi-host low-latency memory semantic communication architecture, supporting 64 local GPU chips and enabling the operation of trillion-parameter models on a single machine [2]. - The super-node integrates multiple servers and computing chips into a larger computational unit, enhancing overall efficiency, communication bandwidth, and space utilization through optimized interconnect technology and liquid cooling [2][3]. Group 2: Industry Challenges - The rapid increase in model parameters and sequence lengths necessitates intelligent computing systems with vast memory capacity, as traditional architectures struggle to meet the demands of efficient, low-power, and large-scale AI computations [3]. - The shift towards multi-model collaboration in AI requires systems capable of handling significantly increased data token generation, leading to a surge in computational requirements [3]. Group 3: Technological Innovation - The "Yuan Nao SD200" addresses the core needs for large memory space and low communication latency for trillion-parameter models through an open bus switching technology [3][4]. - The server's performance is enhanced through a software-hardware collaborative system, achieving super-linear performance improvements of 3.7 times for the DeepSeek R1 model and 1.7 times for the Kimi K2 model [4]. Group 4: Ecosystem Development - The advancement of open-source models is accelerating the transition to an intelligent era, necessitating higher demands on computational infrastructure [4]. - Inspur Information aims to foster innovation across the supply chain by utilizing high-speed connectors and cables, thereby enhancing the overall industry ecosystem and competitiveness [4].

Group 1: DeepSeek and ACL Conference - DeepSeek, in collaboration with Peking University, won the Best Paper Award at the 63rd ACL conference, highlighting a significant achievement in natural language processing with the introduction of the Native Sparse Attention (NSA) mechanism [1][2] - The ACL conference saw a record submission of over 8000 papers, with a main conference acceptance rate of 20.3% and a Findings acceptance rate of 16.7% [1] Group 2: Anthropic's Market Position - Anthropic has surpassed OpenAI in popularity among enterprises, capturing 32% of the large language model market, while OpenAI's share has decreased to 25% [3][4] - Two years ago, OpenAI held a dominant 50% market share, with Anthropic at only 12%, indicating a significant shift in the competitive landscape [3] Group 3: AI Model Developments - The AI startup Step 3 has released an open-source foundational model with 321 billion parameters, showcasing advanced capabilities in visual perception and complex reasoning [5] - Multiple companies, including Tencent and Moonlight, have also released open-source models, indicating a trend towards open-source solutions in the AI industry [5] Group 4: Baidu's AI Integration - Baidu is testing an AI application entry point on its search homepage, allowing users to access various AI applications directly [6][7] - This move follows a major redesign of Baidu's search platform and reflects the company's commitment to integrating AI into its services [6] Group 5: Robotics Industry Insights - Tencent's chief scientist, Zhang Zhengyou, stated that the embodied intelligence industry is still in its early stages, comparing it to the mobile phone industry's evolution [8] - He emphasized that current humanoid robots are primarily used for data collection and research, and a significant breakthrough is needed for widespread adoption [8] Group 6: Supernode Solutions - Several companies showcased supernode solutions at the WAIC, addressing the challenges of large-scale computing clusters [9] - Supernodes aim to enhance performance by integrating computing chip resources, which is increasingly necessary as model parameters grow larger [9] Group 7: Financial Performance of Major Tech Companies - Meta reported a 22% year-over-year revenue increase in Q2, reaching $47.5 billion, with a net profit of $18.3 billion, up 36% [10] - Microsoft achieved a revenue of $76.4 billion in Q4, an 18% increase, with its market capitalization reaching $4 trillion, driven by demand for AI services [11]

Core Viewpoint - The article discusses the evolving landscape of the semiconductor industry in China, highlighting the significant role of investment firms like Wehao Chuangxin and the impact of recent market trends and IPO activities on the sector [1][2][3]. Group 1: Industry Trends - The semiconductor industry is undergoing a transformation, with a notable increase in IPO activities in Hong Kong, where 43 companies successfully listed in the first half of 2025, a 43% increase from the previous year [2]. - The market for semiconductor investments is shifting, with a focus on high-tech barriers and specialized projects as the industry matures and faces new challenges [8][17]. - The rise of AI and the need for integrated hardware solutions are creating new opportunities for companies that can deliver complete systems rather than just individual chips [46][50]. Group 2: Investment Insights - Investment logic in the semiconductor sector is centered around two key questions: the market potential of a product and the feasibility of reducing costs to a profitable level [9][66]. - The current investment environment is characterized by a cautious approach, as many market funds are retreating from semiconductor projects due to long lead times and uncertain returns [21][22]. - Despite challenges, there are still opportunities in areas such as AI-related sensors, smart terminals, and critical components that impact manufacturing processes [8][60]. Group 3: Company Case Studies - Wehao Chuangxin, backed by Weir Shares, has played a crucial role in the semiconductor ecosystem, facilitating significant mergers and acquisitions, including the notable acquisition of OmniVision by Weir Shares [2][39]. - The article highlights the importance of understanding the internal logic of emerging technologies, such as the trend towards "super nodes" in AI, which require a comprehensive approach to hardware and software integration [7][46]. - The shift in focus from general-purpose GPUs to specialized applications reflects the competitive landscape where companies must adapt to survive [40][55].

Group 1: Market Trends and Opportunities - The A-share market is experiencing increased opportunities, with the Shanghai Composite Index rising and reaching above 3600 points, driven by investor enthusiasm [1][7] - The recent performance of the semiconductor chip sector has been strong, indicating a positive trend in technology-related investments [1] - The "Digging Gold Competition" is ongoing, providing a platform for participants to engage in simulated trading and share insights on market trends and investment strategies [1][2] Group 2: Huawei's Ascend 384 Super Node - Huawei showcased its Ascend 384 super node at the 2025 World Artificial Intelligence Conference, attracting significant attention alongside other Chinese companies' super node solutions [2] - The Ascend 384 super node claims to achieve 67% higher total computing power, 107% higher network interconnect bandwidth, and 113% higher memory bandwidth compared to NVIDIA's NVL72 super node [3] - Analysts suggest that super nodes represent an efficient, scalable, and standardized computing cluster architecture necessary for the era of large models, influenced by chip performance and geopolitical factors [3] Group 3: Chikungunya Fever and Market Reactions - Chikungunya fever, caused by the chikungunya virus and transmitted by mosquitoes, has garnered market attention due to its symptoms, which include high fever and joint pain [5][6] - Companies such as Rainbow Group, Runben Co., and Renhe Pharmaceutical have indicated they possess products related to mosquito repellent and pain relief, responding to investor inquiries about chikungunya-related products [6] - Some participants in the "Digging Gold Competition" view the chikungunya fever topic as a speculative investment, suggesting that ordinary investors should consider smaller positions while focusing on stocks with growth potential [6] Group 4: Fund Predictions and Market Sentiment - A public fund's internal prediction of the Shanghai Composite Index reaching 10,000 points has sparked discussions, although some experts express skepticism about the accuracy of such forecasts [7] - Market analysts emphasize the importance of following trends and maintaining positions above the 5-day moving average, with a critical resistance level at 3700 points that could attract more buying interest if surpassed [7]

Core Insights - The report by Shenwan Hongyuan highlights a significant shift in computing power demand from single-point solutions to system-level integration, driven by the explosive growth of model parameters [1] - Two core dimensions for expanding computing power are identified as Scale-up and Scale-out, which will reshape the computing power industry chain and create investment opportunities [1] Group 1: Scale-up and Scale-out - Scale-up refers to increasing the number of GPUs within a single node, moving beyond traditional single-server limitations to a "super node" era, enabling full interconnectivity of GPUs [1][2] - Scale-out focuses on increasing the number of nodes, allowing for elastic expansion to support loosely coupled tasks like data parallelism, with essential differences in protocol stacks, hardware, and fault tolerance mechanisms [1][2] Group 2: Industry Trends and Mergers - Major chip manufacturers like NVIDIA, Broadcom, Huawei, and Haiguang are expected to deepen their focus on the Scale-up domain, while Ethernet technologies will concentrate on Scale-out [2] - Haiguang Information's planned merger with Zhongke Shuguang reflects the trend of vertical integration in the AI chip sector, aiming to enhance capabilities across communication, storage, and software [3] Group 3: Market Dynamics and Opportunities - AI chip manufacturers are not expected to enter the foundry business, as seen with AMD's divestment of its foundry operations post-acquisition of ZT System [4] - The industry chain may further differentiate into card design foundry suppliers and cabinet foundry suppliers, with card design capabilities becoming a key differentiator for value capture [4] - Companies to watch in this evolving landscape include Haiguang Information, Zhongke Shuguang, Inspur Information, Unisplendour, Digital China, Lenovo Group, and Huaqin Technology [4]

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].

Core Viewpoint - Huawei's CloudMatrix 384 super node claims to rival Nvidia's NVL72, but there are discrepancies in the hardware descriptions and capabilities between CloudMatrix and the UB-Mesh paper, suggesting they may represent different hardware forms [1][2][8]. Group 1: CloudMatrix vs. UB-Mesh - CloudMatrix is described as a commercial 384 NPU scale-up super node, while UB-Mesh outlines a plan for an 8000 NPU scale-up super node [8]. - The UB-Mesh paper indicates a different architecture for the next generation of NPUs, potentially enhancing capabilities beyond the current 910C model [10][11]. - There are significant differences in the number of NPUs per rack, with CloudMatrix having 32 NPUs per rack compared to UB-Mesh's 64 NPUs per rack [1]. Group 2: Technical Analysis - CloudMatrix's total power consumption is estimated at 500KW, significantly higher than NVL72's 145KW, raising questions about its energy efficiency [2]. - The analysis of optical fiber requirements for CloudMatrix suggests that Huawei's vertical integration may mitigate costs and power consumption concerns associated with fiber optics [3][4]. - The UB-Mesh paper proposes a multi-rack structure using electrical connections within racks and optical connections between racks, which could optimize deployment and reduce complexity [9]. Group 3: Market Implications - The competitive landscape may shift if Huawei successfully develops a robust AI hardware ecosystem, potentially challenging Nvidia's dominance in the market [11]. - The ongoing development of AI infrastructure in China could lead to a new competitive environment, especially with the emergence of products like DeepSeek [11][12]. - The perception of optical modules and their cost-effectiveness may evolve, similar to the trajectory of laser radar technology in the automotive industry [6].