Core Insights - The 2025 National College Student Computer System Capability Competition - Intelligent Computing Innovation Design Competition (Pilot Cup) successfully concluded in Hefei, emphasizing the importance of AI computing open architecture in the integration of industry, academia, and research [1][3] Group 1: Competition Overview - The Pilot Cup is the only intelligent computing track in the national competition, featuring a high prize pool and employment referral opportunities for winners [3] - This year, the competition introduced a "teaching and training competition" model, enhancing the relevance of competition topics, coverage of scenarios, and integration of education and industry [3] - The competition attracted nearly 10,000 students from over 1,200 universities, with 58 teams awarded [3] Group 2: Industry Impact - The competition addressed engineering challenges in AI implementation with three key topics: "MoE language model end-to-end efficiency optimization," "ONNX Runtime operator performance optimization," and "GMRES algorithm optimization" [3] - The Senior Vice President of Sugon, Li Bin, highlighted the launch of China's first AI computing open architecture, indicating a new phase for the Chinese AI industry and a growing demand for interdisciplinary AI talent [3] Group 3: Educational Initiatives - Sugon's computing platform is user-friendly for students, comparable to CUDA, and provides extensive free learning resources [4] - The Chief Scientist of Intelligent Computing at Sugon noted an explosive growth in AI talent demand, particularly in the era of large models, and emphasized the competition's role in expanding the audience for AI talent [4] - Sugon has consistently focused on talent education, launching various initiatives such as the Pilot Cup, developer communities, and joint laboratories to explore new models for AI talent cultivation [4]

Investment Rating - The report maintains a "Recommended" rating for the computer industry [1] Core Insights - The report highlights the increasing consensus on supernodes as a foundational infrastructure for AI, driven by significant capital expenditures from major cloud service providers (CSPs) and advancements in GPU/ASIC technologies [5][8] - OpenAI has secured substantial power agreements totaling over $1 trillion, indicating a robust demand for AI computing power [5][14] - The report emphasizes the growth potential in various segments including servers, liquid cooling, power supply, and storage, with clear growth momentum identified [7][8] Summary by Sections Demand Side - CSPs are raising their capital expenditure forecasts, with Google increasing its 2025 capex to $91-93 billion, Meta to $70-72 billion, and Amazon to $125 billion, primarily for AI infrastructure [5][29] - OpenAI has signed significant power agreements with NVIDIA, AMD, and Broadcom, totaling 26GW and over $1.1 trillion in value [5][15] - Sovereign AI investments are projected to reach $1 trillion over the next five years, with major projects in the US, EU, and Saudi Arabia [5][25] Supply Side - The report notes the continuous iteration of chips, with supernodes becoming a consensus in AI infrastructure, as evidenced by new product launches from various manufacturers [34][5] - NVIDIA's upcoming GPU architectures are expected to enhance computational capabilities significantly, with the Blackwell Ultra and Rubin architectures set to launch in the coming years [36][39] - Major companies like AMD and Huawei are also advancing their AI chip offerings, with AMD's Helios solution and Huawei's Atlas series expected to drive further innovation [41][44] Growth Segments - The server market is experiencing substantial growth, with companies like Hon Hai and Wistron achieving significant increases in AI server shipments [7] - Liquid cooling technology is becoming essential in AI data centers, with companies reporting high double-digit revenue growth [7] - The storage chip industry is entering a period of severe shortage, driving up DRAM prices and increasing demand from CSPs [7] Multi-modal and Application Ecosystem - The report outlines the rapid evolution of AI models, with major updates from OpenAI and Alibaba, and a significant increase in token usage across platforms [7][8] - OpenAI's new applications and models are enhancing its ecosystem, with a notable increase in daily token usage [7][8]

Core Insights - A total of 677 stocks in the Shanghai and Shenzhen markets have experienced net outflows of main funds for five consecutive days or more as of November 17 [1] - The stock with the longest continuous net outflow is Jianyan Institute, with 20 days of outflows, followed by Jindun Co., which has seen 19 days of outflows [1] - The largest total net outflow amount is from Zhinan Zhen, with a cumulative outflow of 5.922 billion yuan over 13 days [1] Summary by Category Stocks with Longest Net Outflows - Jianyan Institute: 20 days of net outflows [1] - Jindun Co.: 19 days of net outflows [1] Stocks with Largest Net Outflow Amounts - Zhinan Zhen: 5.922 billion yuan over 13 days [1] - Sanhua Intelligent Control: 5.208 billion yuan over 7 days [1] - Shenghong Technology: 3.657 billion yuan over 7 days [1] Stocks with Highest Net Outflow Ratios - Daon Co.: 13.36% net outflow ratio over 5 days [1] - Sanhua Intelligent Control: 9.04% net outflow ratio over 7 days [1] - Zhongke Shuguang: 9.30% net outflow ratio over 7 days [1] Stocks with Notable Price Changes - Zhinan Zhen: -21.74% cumulative price change [1] - Sanhua Intelligent Control: -16.90% cumulative price change [1] - Tianfu Communication: -16.17% cumulative price change [1]

Core Insights - The "Xian Dao Cup" competition has become a significant platform for observing the development of China's AI computing ecosystem, attracting nearly 10,000 students from 1,200 universities this year [1][9] - The event aims to address the fragmentation and bottleneck issues in the domestic computing ecosystem by fostering early talent development and launching the first "AI Computing Open Architecture" in China [1][5] Group 1: Competition Overview - The competition featured cutting-edge topics such as "MoE model efficiency optimization" and "ONNX Runtime operator performance," focusing on real industry challenges [2][4] - Participants were required to complete a full process from theoretical analysis to performance tuning on domestic computing platforms, enhancing their understanding of algorithms and system-level engineering capabilities [4][6] Group 2: Industry Context - The Chinese AI computing market has been dominated by NVIDIA's CUDA ecosystem, which presents a significant barrier for domestic chip manufacturers [5][7] - The lack of a competitive equivalent to NVIDIA's offerings is highlighted by the projection that by 2025, the number of equivalent H100 chips in China will be less than 500,000, only one-twentieth of that in the U.S. [5] Group 3: Open Architecture Initiative - The "AI Computing Open Architecture" aims to create a collaborative ecosystem that integrates various industry players, moving away from a fragmented approach [7][8] - This initiative includes the establishment of the "AI Computing Open Architecture Joint Laboratory," which seeks to reduce barriers for small and medium enterprises and promote collective innovation [7][8] Group 4: Future Implications - The shift from a closed to an open system is seen as essential for enhancing the vitality of the domestic AI ecosystem, allowing for greater participation and collaboration among enterprises [8][9] - The integration of talent cultivation through competitions and the development of an open architecture is viewed as a pathway to achieving sustainable and inclusive computing infrastructure in China [9]

Group 1 - AIGC, AI computing power, and ChatGPT sectors experienced a strong rebound, with stocks like Dongfang Guoxin and BlueFocus rising over 10% [1] - The AI sector is seeing increased capital inflow, with the Huaxia AI ETF (159381) rising 1.82% and the 5G Communication ETF (515050) up 0.61% [1] - Huawei is set to release breakthrough technology in the AI field, potentially increasing the utilization rate of GPU and NPU resources from the industry average of 30%-40% to 70% [1] Group 2 - Bohai Securities noted that the demand side for AI computing power is driven by continued high capital expenditure from cloud vendors and a growing consensus on domestic computing power [1] - Dongguan Securities highlighted that tech giants like Inspur and Huawei are actively developing supernode technology, aiming to create a self-controlled and open domestic computing foundation [2] Group 3 - The Huaxia AI ETF (159381) tracks the ChiNext AI Index and has a significant allocation to optical modules, with the top three holdings being Zhongji Xuchuang, Xinyi Sheng, and Tianfu Communication [3] - The 5G Communication ETF (515050) focuses on the 5G communication theme index and has a scale exceeding 9 billion, with major holdings including Zhongji Xuchuang, Xinyi Sheng, and Luxshare Precision [3]

Core Insights - The article discusses the rapid growth and development of distributed all-flash storage in China, highlighting its performance advantages and increasing market share compared to traditional storage solutions [1][7]. Distributed All-Flash Storage Overview - Distributed storage can be categorized into distributed all-flash and mixed-flash storage, with all-flash storage consisting entirely of SSDs, achieving IOPS in the millions, which is nearly a thousand times faster than traditional HDDs [1][3]. - The demand for high-performance and reliable data storage solutions is increasing due to the explosive growth of data in the digital age [1][7]. Market Size and Growth - The market size for distributed all-flash storage in China is projected to reach 4.78 billion yuan in 2024, representing a year-on-year growth of 71.5%, significantly outpacing the overall growth of the distributed storage industry [1][7]. - The overall distributed storage market in China is expected to exceed 19.82 billion yuan in 2024, with a year-on-year growth rate of 43.7%, accounting for 60.2% of the total storage market [4][6]. Market Share Dynamics - The share of distributed all-flash storage is increasing, rising from 13.4% in 2021 to 24.1% in 2024, indicating a growing adoption among users [6][7]. - In 2024, mixed-flash storage will still dominate the market with a 75.9% share, primarily due to the cost advantages of HDDs in high-capacity applications [6][7]. Competitive Landscape - Major players in the distributed all-flash storage market include Huawei, Zhongke Shuguang, and Inspur Information, alongside emerging companies like Fanlian Information, which show strong competitive potential [2][10]. - The competitive landscape is evolving, with several companies launching distributed all-flash array products that achieve throughput exceeding 100GB/s, positioning China among the global leaders in distributed storage technology [10]. Industry Trends - The industry is expected to see increased penetration, reduced costs, and enhanced security features in distributed all-flash storage solutions [1][10]. - The maturation of the domestic SSD supply chain is providing a solid foundation for the growth of distributed all-flash storage in China, with the enterprise SSD market projected to grow from 21.16 billion yuan in 2021 to 44.51 billion yuan in 2024, reflecting a compound annual growth rate of 28.13% [8][9].

Core Viewpoint - The implementation of the "Guidelines for the Supervision of Listed Companies No. 10 - Market Value Management" has led to a significant increase in the use of various market value management tools by listed companies, including cash dividends, share buybacks, mergers and acquisitions, and equity incentives, to enhance investment value and return to investors [1][2]. Group 1: Market Value Management Tools - Cash dividends and share buybacks have become frequently used tools in the market value management toolbox, with companies encouraged to establish clear mechanisms for share repurchase and to develop and disclose medium- to long-term dividend plans [1][2]. - As of October 31, 2023, 1,195 companies in China's stock market have announced 1,525 share buyback plans for 2025, with a total buyback amount of 92.3 billion yuan, of which 36% was funded by self-owned capital and 26% was for cancellation [2]. - The total cash dividend amount across the market reached 734.9 billion yuan, with 89 companies distributing over 1 billion yuan in dividends within the year [2]. Group 2: Mergers and Acquisitions - The past year has seen a vibrant M&A market, particularly in the "hard technology" sector, with notable cases such as the acquisition of 72.33% of Chip Alliance's shares and the merger of Haiguang Information with Zhongke Shuguang [3]. - State-owned enterprises are also actively engaging in professional integration, exemplified by China Shenhua's plan to consolidate 13 energy companies and Guotai Junan's merger with Haitong Securities [3]. - Policy support has been a key driver for the active M&A market, with various reforms and guidelines aimed at enhancing the efficiency and vitality of mergers and acquisitions [3]. Group 3: Equity Incentives - Equity incentives have been highlighted as a significant market value management tool, with companies encouraged to establish long-term incentive mechanisms [4][5]. - By mid-2023, nearly 3,500 listed companies had implemented equity incentive or employee stock ownership plans, representing 64% of all A-share listed companies [5]. - The recognition of equity incentives as a market value management strategy has deepened, with more companies expected to adopt these tools to enhance long-term value [5].

Core Viewpoint - The article discusses the emergence and significance of the "SuperNode" concept in the AI computing market, highlighting the competitive landscape among domestic manufacturers aiming to match or surpass Nvidia's offerings [1][11]. Group 1: SuperNode Concept - The term "SuperNode" refers to high-performance computing systems that integrate multiple AI training chips within a single cabinet, enabling efficient parallel computing [5][7]. - Domestic manufacturers have rapidly adopted the SuperNode concept, with various companies showcasing their solutions at industry events, indicating a collective push towards advanced AI computing capabilities [2][4]. Group 2: Performance Metrics - Companies are emphasizing the performance metrics of their SuperNode products, with Huawei's 384 SuperNode reportedly offering 1.67 times the computing power of similar Nvidia devices [3][12]. - The scale of integration, indicated by numbers like "384" or "640," reflects the number of AI training chips within a single system, serving as a key performance indicator for manufacturers [7][8]. Group 3: Challenges and Solutions - The industry faces a "communication wall" where a significant portion of computing time is spent waiting for data transfer, necessitating the development of SuperNodes to enhance communication efficiency [6][9]. - The transition from traditional computing methods to SuperNode architectures is driven by the need for higher performance in training large AI models, with manufacturers exploring both Scale-Up and Scale-Out strategies [7][8]. Group 4: Competitive Landscape - Domestic firms are positioning their SuperNode products against Nvidia's offerings, with Huawei's Atlas950 expected to outperform Nvidia's NVL144 in several key metrics [11][12]. - The competition is not only about performance but also about innovative engineering solutions to manage power consumption and heat dissipation in densely packed systems [13][15]. Group 5: Market Demand - The primary demand for AI computing resources is expected to come from large internet companies and state-led cloud services, which are likely to drive the market in the next few years [20][21]. - There are concerns about the sustainability of this demand, as companies may face challenges in justifying high capital expenditures for advanced computing resources [21][22]. Group 6: Future Outlook - The article suggests that while hardware challenges exist, the real test for domestic manufacturers will be in developing robust software ecosystems to support their SuperNode offerings [19][22]. - There is optimism about the potential for AI applications in sectors like robotics and advanced manufacturing, which could drive sustained demand for high-performance computing solutions [22].

Core Insights - The AI computing power market is increasingly focused on "SuperNode" technology, with multiple companies showcasing their solutions at various conferences throughout 2023 [2][3] - The emergence of SuperNodes is driven by the need to overcome bottlenecks in training large AI models, particularly the "communication wall" that arises during parallel computing [4][9] - Domestic companies are adopting SuperNode technology as a practical solution to enhance overall computing power, compensating for limitations in single-chip performance [10][12] Group 1: SuperNode Technology - SuperNode refers to a high-density computing solution that integrates multiple AI chips within a single cabinet, allowing them to function as a unified system [6][7] - The design of SuperNodes involves two main approaches: Scale-Up, which increases resources within a single cabinet, and Scale-Out, which connects multiple cabinets [5][8] - The numbers associated with SuperNodes (e.g., "384", "640") indicate the number of AI training chips integrated within a single system, serving as a key metric for performance and density [7][8] Group 2: Industry Competition - Companies like Huawei and Inspur are positioning their SuperNode products as superior to NVIDIA's offerings, with Huawei claiming its Atlas 950 will outperform NVIDIA's NVL144 in multiple performance metrics [10][11] - The competitive landscape is marked by aggressive parameter comparisons, with domestic firms striving to achieve higher integration density within their SuperNode solutions [12][14] - The engineering challenges of integrating numerous high-power chips into a single cabinet necessitate advanced cooling and power supply technologies [12][14] Group 3: Market Demand and Challenges - The primary demand for AI computing power is expected to come from large internet companies and state-led cloud services, which have the infrastructure to support high-end computing needs [19][20] - Despite the strong demand, there are concerns about the sustainability of investments in AI computing infrastructure, particularly regarding the potential for overbuilding [20][22] - The software ecosystem remains a significant challenge for domestic manufacturers, as effective software solutions are crucial for the successful deployment of high-density computing systems [18][22]

Investment Rating - The report maintains an "Overweight" rating for the computer industry, indicating an expectation that the industry index will outperform the market index by more than 10% over the next six months [1][32]. Core Insights - Domestic technology giants are actively investing in supernode technology, presenting investment opportunities in domestic AI computing power [1][28]. - The SW computer sector has seen a cumulative decline of 2.64% over the past two weeks, underperforming the CSI 300 index by 2.48 percentage points, ranking 28th among 31 first-level industries [10][13]. - The sector's price-to-earnings (PE) ratio is currently at 55.36 times, placing it in the 88.40th percentile for the past five years and the 77.89th percentile for the past ten years [20][22]. Summary by Sections 1. Market Review - The SW computer sector has experienced a cumulative decline of 2.64% from October 31 to November 13, 2025, and a decline of 3.73% in November, while it has increased by 20.45% year-to-date [10][13][28]. 2. Valuation Situation - As of November 13, 2025, the SW computer sector's PE TTM (excluding negative values) stands at 55.36 times, indicating a high valuation relative to historical performance [20][22]. 3. Industry News - Key developments include the launch of the Kimi K2 Thinking model by Moonlight, the introduction of the world's first single-cabinet 640-card supernode by Inspur, and significant investments in AI infrastructure by companies like Tencent and Anthropic [21][23][28]. 4. Company Announcements - Recent announcements include equity transfer agreements and the establishment of joint ventures aimed at enhancing business strategies and market positions [24][25][26][27]. 5. Weekly Perspective - The report highlights the launch of the scaleX640 supernode by Inspur, which significantly enhances computing power and efficiency, and emphasizes the importance of collaborative efforts in the AI computing ecosystem [28][29].