Core Viewpoint - The article discusses the evolution of financing leasing from "financing goods to new" to "intelligent financing empowering production," emphasizing the integration of computing power, energy, services, and ecosystems as the new industry direction [1]. Group 1: Industry Trends - The demand for computing power is shifting from centralized training to distributed inference, making it more accessible for small and medium enterprises [1]. - The combination of "computing power + energy + services + ecosystem" is identified as the future direction of the industry [1]. Group 2: Event Overview - The "Financing Leasing Industry Forum: Synergy of Computing Power and Electricity" is organized by the Shanghai Financing Leasing Industry Association, Yicai Media, and Shanghai Trading Group, aiming to connect industry companies with leasing institutions [1]. - Key industry leaders and financial infrastructure heads are invited to discuss the collaboration between AI and computing power within the industry chain [1]. Group 3: Agenda Highlights - The event features several thematic presentations, including: - "New Stage: Prospects for Intelligent Computing Business Development" by Han Xuebin from Sugon [7]. - "Development and Financial Planning of Computing Power Business in Listed Companies" by Lin Ming, CFO of Yike [7]. - "Practice and Industrial Application of 'Computing Power and Electricity Synergy'" by Du Jie from Longxin Technology [7].

| I | | --- | | 2026 年第一次临时股东会会议议程 | | 2026 年第一次临时股东会会议须知 | | 议案一 关于公司符合向不特定对象发行可转换公司债券条件的议案 4 | | 议案二 关于公司向不特定对象发行可转换公司债券方案的议案 5 | | (一)发行证券的种类 . | | (二) 发行规模 | | (三) 票面金额和发行价格 . | | (四) 债券期限 | | (五) 债券利率 | | (六) 付息的期限和方式 | | (七) 转股期限 . | | (八) 转股价格的确定及其调整 | | (九)转股价格向下修正条款 | | (十)转股股数确定方式 | | (十一) 赎回条款 . | | (十二) 回售条款 . | | (十三) 转股年度有关股利的归属 | | (十四) 发行方式及发行对象 | | (十五)向原股东配售的安排 | | (十六) 债券持有人会议相关事项 . | | (十七) 本次募集资金用途 | | (十八) 担保事项 | | (十九) 评级事项 | | (二十) 募集资金存管 | | (二十一) 本次发行方案的有效期 | | 议案三 关于公司向不特定对象发行可转换公 ...

Investment Rating - The report does not explicitly state an investment rating for the cloud computing industry or the specific ETFs mentioned. Core Insights - The AI technology innovation cycle is divided into two halves: the first half focuses on model and method innovation, while the second half emphasizes problem definition and the integration of AI into real-world applications, with a focus on application value [4][7]. - The CSI Cloud Computing 50 Index comprehensively covers the entire cloud computing industry chain, balancing hardware and software, and aims to capture both AI computing infrastructure benefits and software growth opportunities [4][9]. - The report highlights that the cloud is a core component in the second half of the AI technology cycle, where the focus shifts from training to inference, making cloud computing essential for AI applications [7][79]. Summary by Sections Cloud Computing Overview - Cloud computing is defined as the centralized management and dynamic allocation of resources via the internet, likened to utilities like water and electricity [18]. - The global cloud computing market reached a size of 586.4 billion yuan in 2023, with a growth rate of 19.4%, and is expected to exceed one trillion dollars by 2027 [20]. AI's Impact on Cloud Computing - AI is creating new demands in the IaaS and MaaS layers, particularly for large model calls and custom model needs, leading to a shift in cloud service architectures [8][82]. - The business model for cloud computing is anticipated to transition from resource pricing to value pricing, which could enhance gross margins for cloud resources in the long term [8][85]. CSI Cloud Computing 50 Index - The index includes companies providing IaaS, PaaS, and SaaS, selected based on liquidity, growth potential, and market capitalization, ensuring a balanced representation of the cloud computing sector [9][10]. - The index has shown strong performance compared to mainstream indices, indicating its long-term investment value [9]. New Investment Opportunities - The report emphasizes the growth potential in the AI large model solutions market and the MaaS market, both expected to experience rapid growth in the coming years [87][88]. - The integration of GPU, cloud, and AI is seen as a significant growth driver, allowing cloud providers to differentiate their services and enhance their competitive edge [8][94].

Core Insights - The AI era is facing a critical challenge with the shortage of high-speed interconnect networks, which is essential for the efficient operation of large-scale computing clusters [1][10] - Domestic companies are making strides in developing their own computing chips, but the core technology for high-speed interconnects remains dominated by Nvidia, posing a significant risk to the industry [1][10] Group 1: Industry Challenges - The transition from GPU to high-speed interconnects is becoming a new bottleneck as the scale of computing clusters increases from thousands to tens of thousands of nodes [1][4] - The communication time in distributed training can account for 30-50% of the total time, indicating that a significant portion of the investment in computing power is wasted on data transfer rather than computation [4][5] - The demand for high-speed networks has increased by 10 to 20 times as servers now require multiple network cards to support GPU-centric architectures [6] Group 2: Technological Landscape - There are two main technological routes in the high-speed network domain: RoCE and InfiniBand, with the latter being the preferred choice for high-performance computing due to its superior performance metrics [7][10] - InfiniBand networks are used in approximately 60% of the world's high-performance computing systems, and they are almost standard in the largest AI training clusters [10] Group 3: Domestic Developments - In response to the challenges posed by the dominance of foreign technology, companies like Zhongke Shuguang have developed their own high-speed network solutions, such as the scaleFabric, which is fully self-developed [2][11] - The decision to pursue a fully self-developed InfiniBand system was driven by the inadequacy of available commercial IPs and open-source solutions to meet the performance and reliability requirements for large-scale clusters [12]

Investment Rating - The report maintains a "Buy" rating for the computer industry [4] Core Insights - The "14th Five-Year Plan" emphasizes the core strategic position of artificial intelligence (AI) in national development, with the term "artificial intelligence" appearing 30 times, compared to only 6 times in the previous plan [6][8] - The next five years will see AI Agents as the driving force for economic transformation, with a focus on high-value AI Agent growth leading to significant value creation [6][10] - The demand for intelligent computing power is expected to rise significantly, with projections indicating that by 2028, intelligent computing power will account for over 95% of total computing power in China [6][12] - The report highlights the emergence of "Token inflation" due to the rapid growth in AI model usage, with a projected annual Token consumption increase from 0.0005 PetaTokens in 2025 to 152,667 PetaTokens by 2030, reflecting a CAGR of 3418% [6][24] - Investment opportunities are identified in AI-native application companies, edge AI technologies, domestic computing power chain replacements, and collaborative infrastructure for computing and electricity [6][38] Summary by Sections Section 1: The "14th Five-Year Plan" as a Key Period for Intelligent Economy - The plan introduces the concept of "intelligent native," suggesting AI may become a new production factor [11] - The intelligent economy will drive the reconstruction of AI factor value [13] Section 2: Outlook for the "14th Five-Year Plan" - The intelligent economy is set to initiate a rapid explosion in Token usage, with AI Agents transitioning from cost centers to profit centers [17][38] - The report anticipates a significant increase in the number of active AI Agents, from approximately 28.6 million in 2025 to 2.216 billion by 2030, with a compound annual growth rate (CAGR) of 139% [24] Section 3: Comprehensive Upgrade of AI Factors During the "14th Five-Year Plan" - The report emphasizes the importance of high-quality data sets as a core barrier for building irreplaceable AI Agents [16] - The demand for high-quality, proprietary data sets is expected to surge, with a focus on transforming data resources into valuable assets [16] Section 4: Investment Recommendations - The report suggests focusing on AI-native application companies capable of generating scalable revenue, as well as companies that integrate AI Agents with vertical industry know-how [6][38] - Specific companies to watch include Horizon Robotics, JingTai Holdings, Meitu, and others [6]

Investment Rating - The industry investment rating is "Overweight" and has been maintained from the previous rating [5]. Core Insights - The breakthrough in domestic RDMA technology is expected to enhance the certainty of the deployment of domestic supernodes in 2026, which is a critical year for this development [3]. - The scaleFabric 400 network card and switch meet the performance requirements for high bandwidth and low latency networks needed for large-scale AI training clusters [2]. - The integration of RDMA technology with high-performance domestic network cards and adaptive congestion control algorithms is anticipated to improve the collaborative efficiency of domestic AI computing chips [3]. Summary by Sections Industry Overview - RDMA technology addresses data transmission delays and CPU consumption issues in large-scale parallel computing, becoming a fundamental technology for AI computing infrastructure [1]. - The scaleFabric network, launched by Zhongke Shuguang, represents a significant advancement in domestic RDMA technology, designed for ultra-large-scale intelligent computing clusters [1]. Technical Specifications - The scaleFabric 400 network card features a PCIe 5.0 interface with a port bandwidth of 400 Gbps and an end-to-end communication latency as low as 0.9 microseconds [2]. - The scaleFabric 400 switch has a single-port bandwidth of 800 Gbps and a total switching capacity of 64 Tbps, with a switching latency of approximately 260 nanoseconds [2]. Market Implications - The report suggests that companies with strong technological foundations in the industry are likely to experience significant and flexible growth as the demand for AI computing infrastructure increases [3]. - Recommended companies to watch include Zhongke Shuguang, Cambricon, Haiguang Information, and others involved in AI chips and interconnection technology [3].

Core Viewpoint - The efficiency bottleneck in large model training is shifting from chip computing power to network interconnectivity, with the launch of the scaleFabric 400G lossless high-speed network product by Zhongke Shuguang marking a significant technological breakthrough in the high-end RDMA field in China [2][3]. Group 1: Technological Advancements - Zhongke Shuguang's scaleFabric system features an end-to-end latency as low as 0.9 microseconds and can support a theoretical maximum of 114,000 card clusters, which is 2.33 times the scale of traditional InfiniBand [2][3][4]. - The product has been operational for over 10 months at the National Supercomputing Internet core node in Zhengzhou, supporting a 30,000 card intelligent computing cluster for real large model training tasks [2][7]. - The system's performance is comparable to NVIDIA's CX7, with a single port bandwidth of 800 Gbps and a total switching capacity of 64 Tbps [4][5]. Group 2: Market Dynamics - The demand for high-speed networks is expected to grow significantly, with port usage increasing by 10 to 20 times as the industry transitions from CPU to GPU architectures [3][13]. - Zhongke Shuguang aims to achieve domestic substitution for InfiniBand technology, targeting a significant market share in the high-speed network sector [13][14]. - The company emphasizes the importance of an open ecosystem to break NVIDIA's dominance, focusing on compatibility with various computing chips and fostering collaboration with domestic partners [10][11]. Group 3: Future Outlook - The market for high-speed networks is projected to be vast, with significant interest from sectors such as scientific engineering and artificial intelligence training [14]. - Zhongke Shuguang is preparing for large-scale deployment while ensuring product stability and performance through extensive real-world testing [14][15]. - The integration of AI and intelligent agents into supercomputing systems is expected to enhance user experience, making computing resources more accessible and efficient for solving real-world problems [15].

Core Viewpoint - The article highlights the significant technological advancement made by Zhongke Shuguang in the high-end RDMA network sector, marking a shift from "catching up" to "running alongside" in China's intelligent computing infrastructure, particularly in high-speed networking [1][7]. Group 1: Technological Breakthroughs - Zhongke Shuguang has launched the scaleFabric, a fully self-developed 400G lossless high-speed network product, achieving a low end-to-end latency of 0.9 microseconds and supporting a theoretical maximum of 114,000 card cluster deployments [1][3]. - The scaleFabric system has been operational for over 10 months at the National Supercomputing Internet core node in Zhengzhou, successfully supporting a 30,000 card intelligent computing cluster [6][10]. - The product's core technology indicators include a link fault recovery time of less than 1 millisecond and a single subnet interconnection scale 2.33 times that of traditional InfiniBand [3][4]. Group 2: Market Dynamics - The demand for high-speed networks is driven by the increasing number of network ports required as cluster sizes grow from thousands to tens of thousands of cards, leading to a 10 to 20 times increase in network usage [3][10]. - The current market is dominated by NVIDIA, particularly through its InfiniBand technology, which has established a closed-loop ecosystem that poses a challenge for competitors [7][10]. - Zhongke Shuguang aims to provide a more open and adaptable network solution that can integrate with various domestic computing chips, thereby reducing dependency on a single vendor [10][12]. Group 3: Strategic Positioning - Zhongke Shuguang's strategy involves inheriting the open aspects of InfiniBand while attempting to break the commercial binding within NVIDIA's ecosystem [9][12]. - The company plans to establish a working group under the Guanghe organization to create relevant standards and enhance ecosystem adaptability through user feedback [9][12]. - The anticipated market for high-speed networks is substantial, with a focus on scientific engineering computing and AI training, indicating numerous potential deployment opportunities [12][10].

Core Viewpoint - The article emphasizes the critical role of computing power in the AI era, highlighting the rapid deployment of large-scale intelligent computing centers as a reflection of the expanding AI applications across various industries [1][5][15]. Group 1: Computing Power Infrastructure - The deployment of the scaleFabric high-speed network at the national supercomputing internet core node has attracted hundreds of AI model vendors and numerous universities and research institutions [1]. - The scaleFabric network, developed by Zhongke Shuguang, represents a significant leap from merely increasing the number of computing cards to enhancing network efficiency, which is crucial for maximizing the value of computing clusters [3][7]. - The demand for computing power for large model training is doubling approximately every 3.5 months, with AI application performance also improving significantly every 9 months [5][6]. Group 2: Network Efficiency and Challenges - The current intelligent computing networks face challenges such as high latency, difficulty in coordination, and operational issues, necessitating a shift towards integrated and self-researched solutions [5][6]. - Research indicates that communication time in dense models accounts for 10% to 20%, while in Mixture of Experts (MoE) models, it can reach 40% to 60%, highlighting the importance of network efficiency in determining cluster value [6][7]. Group 3: Technological Advancements - The scaleFabric network features an ultra-high bandwidth of 800Gb/s per port and end-to-end transmission latency of less than 1 microsecond, significantly enhancing scalability and reducing overall network costs by 30% compared to existing solutions [8][15]. - The choice of InfiniBand (IB) technology over RDMA over Converged Ethernet (RoCE) allows for better performance and lower latency, making it suitable for large-scale networks [10][11]. Group 4: Market Impact and Future Outlook - The introduction of the scaleFabric network is expected to reshape the domestic computing power landscape, providing a cost-effective alternative to imported IB solutions and enabling easier maintenance and operation of large clusters [15][16]. - The demand for high-bandwidth products is anticipated to grow, with the IBTA organization projecting a need for 1.6Tb/s IB products by 2028, indicating a competitive landscape for next-generation AI model training [15][16].

Group 1 - The core viewpoint of the article highlights the breakthrough in domestic RDMA technology with the launch of the scaleFabric, a native lossless RDMA high-speed network by Zhongke Shuguang, which competes with Nvidia's NDR technology [2][4] - The scaleFabric400 series network products have been validated in a nearly 10,000-card scale environment and have been running stably for over 10 months, filling a technological gap in domestic high-speed interconnects for clusters [2][8] - The performance specifications of the scaleFabric400 include a port bandwidth of 400Gbps, end-to-end communication latency as low as 0.9 microseconds, and a switch with a single port bandwidth of 800Gbps, supporting a total switching capacity of 64Tbps [6][8] Group 2 - The product features a credit-based lossless flow control mechanism that mitigates congestion and packet loss risks, with a link failure recovery time of less than 1 millisecond, supporting nearly 10,000-card clusters [8][11] - Compared to Nvidia's NDR, the scaleFabric400 offers a 25% increase in switch port density, a 100% increase in maximum QP number supported by network cards, and a maximum interconnect scale that is 2.33 times that of traditional IB [8][10] - The deployment of the scaleFabric network is already operational in Zhengzhou, supporting a national-level AI computing network base with a total scale of 30,000 cards [9][10] Group 3 - Zhongke Shuguang has developed a complete computing power foundation through long-term technological accumulation in high-performance computing, storage, and networking, enabling a collaborative development of "computing-storage-network" [13] - The successful implementation of the native RDMA network signifies the formation of an independent technological path in intelligent computing interconnects in China, addressing a critical component of the country's computing infrastructure [13] - The high-performance network industry ecosystem surrounding the native RDMA technology is accelerating its formation as the product is applied in ultra-large-scale intelligent computing clusters [13]