Core Insights - The article emphasizes that NVIDIA remains the dominant player in AI computing power, providing significantly better performance per dollar compared to AMD [1][30]. - A report from Signal65 reveals that under certain conditions, NVIDIA's cost for generating the same number of tokens is only one-fifteenth of AMD's [4][30]. Performance Comparison - NVIDIA's platform offers 15 times the performance per dollar compared to AMD when generating tokens [1][30]. - The report indicates that for complex models, NVIDIA's advantages become more pronounced, especially in the context of the MoE (Mixture of Experts) architecture [16][24]. MoE Architecture - The MoE architecture allows models to split parameters into specialized "expert" sub-networks, activating only a small portion for each token, which reduces computational costs [10][11]. - However, communication delays between GPUs can lead to idle time, increasing costs for service providers [13][14]. Cost Analysis - Despite NVIDIA's higher pricing, the overall cost-effectiveness is better due to its superior performance. For instance, the GB200 NVL72 costs $16 per GPU per hour, while AMD's MI355X costs $8.60, making NVIDIA's price 1.86 times higher [27][30]. - The report concludes that at 75 tokens per second per user, the performance advantage of NVIDIA is 28 times, resulting in a cost per token that is one-fifteenth of AMD's [30][35]. Future Outlook - AMD's competitiveness is not entirely negated, as its MI325X and MI355X still have applications in dense models and capacity-driven scenarios [38]. - AMD is developing a cabinet-level solution, Helios, which may narrow the performance gap in the next 12 months [39].

华安证券主要观点如下： 总量 全球AI技术正从训练主导转向推理主导，驱动硬件产业链迎来新一轮成长机遇多模态大模型如谷歌 Gemini 3 Pro、OpenAISora 2的迭代，以及AIAgent的规模化落地，显著提升了推理算力需求。在这一趋 势推动下，云服务提供商持续上调资本开支，预计2025年全球八大CSP资本开支将达4310亿美元，同比 增长65%，2026年有望进一步增至6020亿美元。与此同时，各国主权AI计划纷纷启动，例如美国"星际 之门"计划投资约5000亿美元，欧盟拟投入215亿美元建设AI超级工厂，这些举措共同推动全球AI基础 设施步入高景气建设周期。据预测，到2030年，全球AI数据中心容量将达156GW，占数据中心总需求 的71%。 云侧 PCB：AI服务器带来明确的价值量提升，例如英伟达DGXH100单GPU对应PCB价值量达211美元，较前 代提升21%；GB200 NVL72更是将单GPU价值量推高至346美元。随着Rubin架构采用无缆化设计，以及 交换机向800G/1.6T演进，PCB正朝着高层数、使用如M9等低介电材料的更高性能方向升级。与此同 时，2026年国内高端PCB产 ...

Core Insights - The AI inference business is demonstrating remarkable profitability amid intense competition in the AI sector, with a recent Morgan Stanley report providing a comprehensive analysis of the global AI computing market's economic returns [1][3][8] Company Performance - A standard "AI inference factory" shows average profit margins exceeding 50%, with Nvidia's GB200 chip leading at nearly 78% profit margin, followed by Google's TPU v6e pod at 74.9% and Huawei's solutions also performing well [1][3][5] - AMD's AI platforms, specifically the MI300X and MI355X, are facing significant losses with profit margins of -28.2% and -64.0% respectively, attributed to high costs and low output efficiency [5][8] Market Dynamics - The report introduces a "100MW AI factory model" that evaluates total ownership costs, including infrastructure, hardware, and operational costs, using token output as a revenue measure [7] - The future AI landscape will focus on building technology ecosystems and next-generation product layouts, with Nvidia solidifying its lead through a clear roadmap for its next platform, "Rubin," expected to enter mass production in Q2 2026 [8]

Core Viewpoint - The article emphasizes the growing trend of liquid cooling technology in data centers, particularly in relation to AI applications and the performance improvements it offers over traditional cooling methods. Group 1: Liquid Cooling Technology - Liquid cooling servers outperform air-cooled versions by 25% in performance and reduce power consumption by 30% [1] - The adoption of liquid cooling is expected to rise significantly, with projections indicating that over 65% of new systems will utilize this technology by next year [1] - The cost-effectiveness of liquid cooling solutions is highlighted, as they are seen as a way to lower overall expenses while maintaining high performance [1][6] Group 2: Company Performance and Projections - NVIDIA is projected to ship approximately 30,000 units of the GB200 and 10,000 units of the GB300, with an additional 200,000 units of the B200 single card expected [5] - The GB300 is anticipated to see increased shipments, with expectations raised to 100,000 units [5] - The server assembly sector is experiencing a resurgence, indicating a positive outlook for companies involved in this space [5] Group 3: Industry Trends - All Azure regions are now equipped to support liquid cooling, enhancing the flexibility and efficiency of data center operations [3] - The industry is moving towards building gigawatt and multi-gigawatt data centers, with over 2 gigawatts of new capacity established in the past year [4] - The trend towards liquid cooling is accelerating, with domestic manufacturers beginning to capture market share [6]

Group 1 - The core viewpoint of the article highlights the introduction of the AI computing cluster solution Cloud Matrix 384 at the WAIC2025 conference, which utilizes a fully interconnected topology for efficient collaboration [1] - The Atlas 900 A3 SuperPoD features a super-node architecture with ultra-large bandwidth of 392GB/s, ultra-low latency of less than 1 microsecond, and exceptional performance of 300 PFLOPs, enhancing the training performance of models like LLaMA3 by over 2.5 times compared to traditional clusters [1] - The Ascend 910C outperforms NVIDIA's GB200 NVL72 in system-level BF16 computing power (300 PFLOPS), HBM capacity (49.2TB), and bandwidth (1229TB/s) [1] Group 2 - The semiconductor equipment ETF (159516) tracks the semiconductor materials and equipment index (931743), focusing on the materials and equipment sectors within the semiconductor industry, covering companies involved in manufacturing, processing, and testing [1] - The index constituents are primarily companies with significant technological advantages and core competitiveness in semiconductor materials and equipment, aiming to reflect the overall performance and industry development trends of listed companies in this segment [1] - Investors without stock accounts can consider the Guotai Zhongzheng Semiconductor Materials and Equipment Theme ETF Initiated Link A (019632) and Link C (019633) [1]

Group 1 - Amazon's cloud computing division has developed specialized hardware for cooling next-generation NVIDIA GPUs, which are widely used for AI-related computing tasks [1] - The high energy consumption of NVIDIA GPUs necessitates additional cooling equipment for companies utilizing these processors [1] - Amazon previously considered building data centers with liquid cooling systems but found existing solutions inadequate for their scale [1] Group 2 - The newly developed In-Row Heat Exchanger (IRHX) can be integrated into existing and new data centers to support high-power NVIDIA GPUs [1] - Customers can now access a new AWS service through the P6e compute instance, which utilizes NVIDIA's high-density computing hardware [2] - Amazon has reduced reliance on third-party suppliers by developing its own infrastructure hardware, contributing to improved profitability [2]

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 Viewpoints - The article discusses the rapid growth and potential of the computing power leasing industry, particularly through the lens of CoreWeave, a significant player in this sector [2][11]. Company Overview - CoreWeave was established in 2017, originally as a cryptocurrency mining company, and has since pivoted to focus on AI cloud and infrastructure services, operating 32 data centers by the end of 2024 [2][3]. - The company has deployed over 250,000 GPUs, primarily NVIDIA products, and is a key provider of high-performance infrastructure services [2][3]. Business Model - CoreWeave offers three main services: bare-metal GPU leasing, management software services, and application services, with a focus on GPU leasing as the core offering [3][4]. - Revenue is generated primarily through two models: commitment contracts (96% of revenue) and on-demand payment, allowing flexibility for clients [4][5]. Financial Performance - In 2024, CoreWeave's revenue reached $1.915 billion, a year-over-year increase of over seven times, with Q1 2025 revenue at $982 million, reflecting a fourfold increase [8][9]. - The company has a remaining performance obligation of $15.1 billion, indicating strong future revenue potential [8]. Competitive Advantages - CoreWeave has optimized GPU utilization rates and efficiency, achieving significant performance improvements in AI training and inference tasks [7]. - The company has established strong relationships with NVIDIA, ensuring priority access to cutting-edge chips and technology [6][7]. Market Outlook - The AI infrastructure market is projected to grow from $79 billion in 2023 to $399 billion by 2028, with a compound annual growth rate of 38%, highlighting the industry's potential [11]. - The computing power leasing sector is expected to play a crucial role in the digital economy, driven by increasing demand for AI capabilities [11][14]. Future Growth Strategies - CoreWeave plans to expand its customer base, explore new industries, and enhance vertical integration with strategic partnerships [10]. - The management aims to leverage existing contracts and maintain a low leverage asset structure to support growth [10].

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].

Group 1 - The short-term A-share market may lack strong upward catalysts, but the expectations for fundamental recovery and policy release have not been disproven, indicating limited downside risk [1][2] - The recent US-China Geneva trade talks resulted in a joint statement agreeing to significantly reduce bilateral tariff levels, boosting market confidence [2] - April's financial data showed that new social financing maintained a year-on-year increase trend, with the stock of social financing growing at a rate of 8.7%, suggesting an upward trend in fundamentals and A-share earnings [2][5] Group 2 - The recent US restrictions on high-end computing chips for China may temporarily impact Huawei's chip exports, but domestic demand for local computing chips is strengthening [26][30] - Huawei's Cloud Matrix 384 computing cluster has achieved performance metrics that surpass Nvidia's flagship product GB200 NVL72, marking a significant breakthrough in China's AI infrastructure [31][32] - The capital expenditure of major cloud service providers like Tencent and Alibaba has decreased significantly compared to the previous quarter, but remains above historical averages, indicating a potential shift in investment strategy [25][30] Group 3 - The recent US-China tariff negotiations have led to a recovery in industries closely related to exports, such as e-commerce, chemical fibers, and shipping ports [15] - The technology sector is showing signs of recovery, but the market consensus suggests a phase of consolidation and potential volatility ahead [17][21] - The overall industry scores indicate a high allocation recommendation for sectors like electronics, computers, and automation equipment, while sectors like real estate and coal are rated for lower allocation [33]