毛利率方面，第三季度GAAP和非GAAP毛利率同比下降，因为业务模式从提供Hopper HGX™系统转向 Blackwell全规模数据中心解决方案。 11月19日盘后，英伟达公布2026财年第三财季财报，营收570.06亿美元，同比增长62%，连续九个季度 营收持续增长，超过市场预期552.12亿美元；净利润为319.1亿美元，同比增长65%。 分业务板块来看，最为核心的数据中心业务收入达到创纪录的512亿美元，环比增长25%，同比增长 66%。 这一成绩拉动英伟达盘后股价一度涨超4%。 财报披露，英伟达数据中心业务增长主要受加速计算、强大的AI模型和智能代理应用三大平台转变推 动。Blackwell Ultra现已成为英伟达所有客户类别的领先架构，而之前的Blackwell架构需求依然强劲。 H20在第三季度的销售额微不足道。 第三季度数据中心计算收入达到创纪录的430亿美元，同比增长56%，环比增长27%。网络收入达到创 纪录的82亿美元，得益于GB200和GB300系统的NVLink计算结构推出及持续增长，同比大增162%。 网络收入环比增长13%，主要由XDR InfiniBand产品、NVLink™ ...

Core Viewpoint - The company anticipates that AI-related revenue will surpass the total revenue from software and non-AI businesses within two years, with a long-term goal of reaching $120 billion in AI revenue by fiscal year 2030, directly linking this target to the CEO's compensation [1][3][5]. AI Revenue Growth - The CEO emphasized that meeting the AI computing needs of specific clients is the company's top priority, predicting that AI revenue will become the absolute core pillar of the business [2][3]. - The "Tan PSU Award" executive incentive plan ties the CEO's compensation to achieving specific AI revenue milestones, highlighting management's confidence in the AI business [3][5]. Market Segmentation - The AI accelerator market is expected to see a division, with large cloud service providers favoring customized ASIC chips for their specific workloads, while enterprise clients will continue to rely on commercial GPUs [6]. - The company identifies its XPU (customized processor) business opportunities primarily from existing and potential clients among the seven major cloud service providers [6]. Networking in AI - The CEO highlighted the growth potential of AI networking, asserting that Ethernet will play an increasingly important role in AI networks due to its proven technology and the rising demand for scalable networks [7]. - The company expects large-scale deployment of Ethernet in these expanded networks within the next 18-24 months [7].

Group 1 - The core sentiment in the market is driven by the dovish stance of the Federal Reserve, leading to significant gains in various stock indices, including a 2% rise in the Hang Seng Index, reaching a four-year high [1] - The technology sector, particularly Chinese concept stocks, has shown strong performance, with a 3.8% increase last Friday, also marking a four-year high [1] - The real estate sector has seen a rebound, with Vanke's stock hitting the limit up, indicating a potential recovery in the housing market, supported by debt management strategies [4] Group 2 - The rare earth sector is experiencing a surge due to news of import controls on rare earth minerals, highlighting China's dominance not only in mining but also in refining capabilities [3] - The chip industry, particularly stocks related to Nvidia, continues to perform well, driven by new product launches and positive market sentiment [3] - The white wine sector is showing strength, with several stocks hitting limit up, indicating a recovery in this previously underperforming segment [10] Group 3 - The overall market sentiment is mixed, with a significant increase in trading volume but a relatively low number of stocks hitting limit up, suggesting caution among investors [7] - The current market rally is characterized by sector rotation rather than broad-based gains, with technology stocks leading the charge while other sectors lag behind [8] - The automotive sector is facing challenges, with competitive pressures leading to underperformance, particularly in the complete vehicle segment [5]

Core Viewpoint - The article discusses the competition between InfiniBand and Ethernet in AI networking, highlighting the advantages of Ethernet in terms of cost, scalability, and compatibility with existing infrastructure [6][8][22]. Group 1: AI Networking Overview - AI networks are primarily based on InfiniBand due to NVIDIA's dominance in the AI server market, but Ethernet is gaining traction due to its cost-effectiveness and established deployment in large-scale data centers [8][20]. - The establishment of the "Ultra Ethernet Consortium" (UEC) aims to enhance Ethernet's capabilities for high-performance computing and AI, directly competing with InfiniBand [8][9]. Group 2: Deployment Considerations - Teams face four key questions when deploying AI networks: whether to use existing TCP/IP networks or build dedicated high-performance networks, whether to choose InfiniBand or Ethernet-based RoCE, how to manage and maintain the network, and whether it can support multi-tenant isolation [9][10]. - The increasing size of AI models, often reaching hundreds of billions of parameters, necessitates distributed training, which relies heavily on network performance for communication efficiency [10][20]. Group 3: Technical Comparison - InfiniBand offers advantages in bandwidth and latency, with capabilities such as high-speed data transfer and low end-to-end communication delays, making it suitable for high-performance computing [20][21]. - Ethernet, particularly RoCE v2, provides flexibility and cost advantages, allowing for the integration of traditional Ethernet services while supporting high-performance RDMA [18][22]. Group 4: Future Trends - In AI inference scenarios, Ethernet is expected to demonstrate greater applicability and advantages due to its compatibility with existing infrastructure and cost-effectiveness, leading to more high-performance clusters being deployed on Ethernet [22][23].

Core Insights - The article discusses the rise of AI Networking, particularly focusing on the "Scale Up" segment, highlighting its technological trends, vendor dynamics, and future outlook [1] Group 1: Market Dynamics - The accelerator market is divided into "commercial market" led by NVIDIA and "custom market" represented by Google TPU and Amazon Tranium, with the custom accelerator market expected to gradually match the GPU market in size [3] - Scale Up networking is transitioning from a niche market to mainstream, with revenue projected to exceed $1 billion by Q2 2025 [3] - The total addressable market (TAM) for AI Network Scale Up is estimated at $60-70 billion, with potential upward revisions to $100 billion [12] Group 2: Technological Evolution - AI networking has evolved from "single network" to "dual network," currently existing in a phase of "multiple network topologies," with Ethernet expected to dominate in the long term [4] - The competition between Ethernet and NVLink is intensifying, with NVLink currently leading due to its maturity, but Ethernet is expected to gain market share over the decade [5] - Scale Up is defined as a "cache coherent GPU to GPU network," providing significantly higher bandwidth compared to Scale Out, with expectations of market size surpassing Scale Out by 2035 [8] Group 3: Performance and Cost Analysis - Scale Up technology shows a significant performance advantage, with latency for Scale Up products like Broadcom's Tomahawk Ultra at approximately 250ns, compared to 600-700ns for Scale Out [9] - Cost-wise, Scale Up Ethernet products are projected to be 2-2.5 times more expensive than Scale Out products, indicating a higher investment requirement for Scale Up solutions [9] Group 4: Vendor Strategies - Different vendors are adopting varied strategies in the Scale Up domain, with NVIDIA focusing on NVLink, AMD betting on UA Link, and major cloud providers like Google and Amazon transitioning towards Ethernet solutions [13] - The hardware landscape is shifting towards embedded designs in racks, with a potential increase in the importance of software for network management and congestion control as Scale Up matures [13]

Core Viewpoint - The rise of humanoid robots has gained significant attention following a performance at a Spring Festival gala, highlighting advancements in embodied intelligence and the need for improved hardware, particularly chips, to overcome existing challenges [1] Group 1: Humanoid Robot Development - Humanoid robots are increasingly compared to upright vehicles, requiring perception systems, high-performance chips, and effective power management for extended operation [2] - The execution capabilities of humanoid robots differ from cars, as they must also manipulate objects with dexterity, particularly through their hands [2] Group 2: ADI's Role in Humanoid Robotics - ADI has been involved in the robotics market for years and is now accelerating its offerings, including traditional chips and subsystems to facilitate product design and implementation [4] - ADI provides a range of products for humanoid robots, including sensors, internal connection systems, motor control modules, and power management solutions [5] Group 3: Connection Technologies - GMSL (Gigabit Multimedia Serial Link) is highlighted as a key technology for internal connections in humanoid robots, offering efficient data transmission and improved performance [9] - ADI's GMSL solution supports real-time transmission of video, sensor data, and power, making it suitable for the complex requirements of humanoid robots [10] Group 4: Isolation and Control Solutions - ADI offers isolation devices to protect sensitive electronics in humanoid robots from electrical interference, ensuring reliable operation in challenging environments [10] - The ADMT4000 solution provides precise joint control for robotic arms, enabling memory of positions even after power loss, thus enhancing operational reliability [12][14] Group 5: Challenges in Dexterous Manipulation - The development of dexterous hands, referred to as "smart hands," is a critical challenge in the humanoid robotics industry, requiring advanced sensors and AI algorithms [15] - Simplifying internal connections within these dexterous hands is also a significant focus for developers [15]