证券研究报告 2025 年 12 月 20 日 湘财证券研究所 行业研究 电子行业 AI 算力需求推动 PCB 量价齐升 相关研究： | 1.《H200解禁，看好算力需求》 | | --- | | 2025.12.14 | | 2.《豆包发布手机助手预览版， | | 看好端侧AI投资机会》 | | 2025.12.08 | | 3.《北京推出"太空算力"规 | | 划，看好算力需求》2025.11.30 | ❑ 交换机升级推动 PCB 单机价值量提升 数据中心交换机是专为大规模数据中心环境设计的高性能网络设备，用 于服务器、存储设备和其他网络节点之间的高速数据交换。目前 400G 交 换机是主流，2024 年出货量占比 38%，800G 交换机 2025 年放量，预计 1.6T 交换机 2026 年开始量产。随着交换机端口速率的迭代，交换机使用 的 PCB 层数不断增加，使用的材料等级从 M6 升级到 M9，推动其单机 PCB 价值量也持续大幅提升。 行业评级：增持 近十二个月行业表现 % 1 个月 3 个月 12 个月 相对收益 1.2 -3.7 21.9 绝对收益 0.8 -2.2 37.7 -20% - ...

Core Insights - The report emphasizes the synergy between AI innovation and capital expenditure (CAPEX), highlighting that model innovation is the core driver of AI development, with CAPEX serving as the foundation for the AI cycle [12][14] - The AI industry chain includes AI hardware, CAPEX, and AI models and applications, which collectively support the computational needs for large model training and inference [12][14] - The report suggests that the AI storage cycle is driven by rising prices and simultaneous expansion and upgrades in production capacity, particularly in cloud and edge storage [4][34] Group 1: AI Innovation and CAPEX - Model innovation is identified as the key driver of AI development, with significant capital expenditures from cloud service providers and leading enterprises providing a stable cash flow to support upstream hardware sectors [14][24] - The report notes that major companies like Google and OpenAI are making substantial advancements in multi-modal models, which are expected to enhance user engagement and monetization opportunities [19][25] - The integration of AI capabilities into various applications is projected to create a closed loop of high computational demand leading to high-value content and increased user willingness to pay [24][25] Group 2: Storage Cycle - The report indicates that storage prices are on the rise, significantly boosting the gross margins of original manufacturers, with capital expenditures in the storage sector entering an upward phase [4][34] - It highlights that traditional DRAM and NAND production is being approached cautiously, while HBM production is prioritized, indicating a shift in focus within the storage industry [4][34] - The report discusses the emergence of new opportunities in the storage foundry model, driven by the evolving demands of AI applications [4][34] Group 3: Investment Recommendations - The report recommends focusing on companies within the AI ecosystem, particularly those involved in AI storage, PCB, and power supply sectors, as they are expected to experience sustained growth [4][34] - It suggests that the ongoing upgrades in DRAM and NAND architectures will create new equipment demand, presenting investment opportunities in related companies [4][34] - The report encourages attention to the storage industry chain, particularly in light of the anticipated price increases and margin improvements for original manufacturers [4][34]

Investment Rating - The report maintains a "Buy" rating for the computer industry [1] Core Insights - The demand for AI computing power is expected to grow significantly, driven by advancements in large model training and the introduction of new architectures like GB300 and Vera Rubin [11] - NVIDIA's revenue for FY2026 Q2 reached $46.7 billion, a year-on-year increase of 56%, indicating strong market demand for AI computing solutions [5][59] - The report highlights the performance improvements of NVIDIA's new GPU architectures, with the GB300 achieving a 1.5x increase in FP4 computing power compared to its predecessor [30] Summary by Sections Section 1: GPU Core - The GB300 GPU, based on the Blackwell Ultra architecture, utilizes TSMC's 4NP process and features a floating-point performance of 15 PFLOPS, which is 1.5 times that of the B200 [5][26] - The Rubin Ultra NVL576 is expected to launch in 2027, offering significant performance enhancements over the GB300 NVL72 [11][31] Section 2: Server Details - The GB300 NVL72 system consists of 18 compute trays and 9 switch trays, integrating 72 Blackwell Ultra GPUs and 36 Grace CPUs, with potential performance improvements of up to 50 times compared to previous architectures [6][80] - The report discusses the transition from HGX to MGX server designs, allowing for more efficient AI and HPC applications [67] Section 3: Networking - The introduction of CPO technology is set to replace traditional pluggable optical modules, enhancing energy efficiency by 3.5 times and deployment speed by 1.3 times [7] - The Rubin architecture will utilize NVLink 6.0 technology, doubling the speed to 3.6 TB/s, facilitating high-speed interconnects for AI applications [7] Section 4: HBM - HBM4 is expected to achieve mass production in 2026, with SK Hynix leading the market, and collaborations with major clients like NVIDIA and Microsoft [8] Section 5: Liquid Cooling - The GB300 NVL72 employs a full liquid cooling solution, enhancing thermal efficiency and operational cost-effectiveness [9] Section 6: Investment Recommendations and Related Companies - The report identifies potential beneficiaries in the AI computing supply chain, including companies involved in AI chips, server systems, HBM, and cooling technologies [12]

Core Insights - The event highlighted the transformative shift in data centers towards AI-driven computing, with projected capital expenditures exceeding $1 trillion by 2028 for data center construction, primarily focused on accelerated computing chips [2][12][13] - NVIDIA's Blackwell architecture is fully operational, showcasing significant performance improvements and a roadmap for future products like Rubin and Feynman, which promise substantial enhancements in computational power and efficiency [3][42][45] - The introduction of the Quantum-X CPO switch and Spectrum-X technology aims to revolutionize networking capabilities, reducing energy consumption and increasing deployment efficiency [5][46] - The advancements in AI applications, particularly in autonomous driving and robotics, are supported by NVIDIA's new systems and frameworks, enhancing the development and training processes [6][26][24] Capital Expenditure and AI Infrastructure - Data center capital expenditures are expected to reach $1 trillion by 2028, with a significant portion allocated to accelerated computing chips [2][12] - NVIDIA plans to deliver 1.3 million Hopper GPUs to major cloud service providers in 2024, with an increase to 3.6 million Blackwell GPUs in 2025 [2][3] AI Model Training and Inference - The demand for computational power for AI training and inference has surged, with estimates suggesting a 100-fold increase in required computing resources compared to the previous year [10][11] - NVIDIA outlines three levels of AI: Generative AI, Agentic AI, and Physical AI, each representing a different stage of AI development and application [8][10] Product Development and Future Roadmap - Blackwell has been fully launched, with significant customer demand and performance improvements, including a 40-fold increase in inference performance compared to previous models [3][42] - Future products like Vera Rubin and Rubin Ultra are set to enhance computational capabilities further, with expected performance increases of up to 15 times [45][42] Networking Innovations - The Quantum-X CPO switch is anticipated to launch in late 2025, offering substantial energy savings and improved network efficiency [5][46] - Spectrum-X technology will provide high bandwidth and low latency, integrating seamlessly into NVIDIA's computing architecture [5][46] AI Applications in Autonomous Driving and Robotics - NVIDIA's Halos system aims to enhance safety in autonomous vehicles, while the open-source Isaac Groot N1 model supports robotics development [6][24] - The integration of Omniverse and Cosmos platforms accelerates the development of AI for autonomous driving, enabling end-to-end training capabilities [26][24] Data Center Evolution - The transition of data centers into AI factories is underway, focusing on processing, analyzing, and generating AI-driven applications [12][13] - NVIDIA's Dynamo operating system is designed to optimize AI factory operations, enhancing efficiency and performance [35][36]

Investment Rating - The report maintains a "Buy" rating for the company, indicating an expected price increase of over 15% in the next 6-12 months [4]. Core Insights - The company is expected to benefit as a leading AI chip manufacturer due to rapid hardware iteration and a rich software ecosystem, which enhances its competitive edge against rivals [4]. - The demand for AI computing power is anticipated to remain strong, driven by the complexity of models in the inference stage, which require significantly more computing resources compared to earlier generative AI models [2][4]. Summary by Sections Performance Review - The company held the GTC 2025 event on March 18, 2025, showcasing future product launches including GB300 (GB Ultra), Vera Rubin, Rubin Ultra GPUs, and CPO switches for Infiniband and Ethernet [1]. Operational Analysis - The transition from simple generative AI to assistant AI is expected to sustain demand for computing power, with inference requiring 100 times more tokens than before. The company anticipates strong customer demand, with major cloud providers expected to purchase 3.6 million Blackwell GPU dies in 2024 [2]. - Upcoming product releases include GB300 NVL72 in H2 2025, which will feature 288GB HBM3e memory and 1.5 times the computing power of GB200 NVL72. Vera Rubin NVL144 is expected in H2 2026, offering 3.3 times the computing power of GB300 NVL72, and Rubin Ultra NVL576 is projected for H2 2027, with 14 times the computing power of GB300 NVL72 [2]. Software Ecosystem - The company continues to enhance its software ecosystem, launching libraries tailored for various industries, such as cuLitho for lithography and CUDA-Q for quantum computing. Additionally, the introduction of the Dynamo system aims to improve GPU efficiency by assisting with prefill and decode tasks [3]. Profit Forecast and Valuation - The company forecasts net profits of $122.2 billion, $156.9 billion, and $177.9 billion for FY26, FY27, and FY28, respectively, with corresponding P/E ratios of 23, 18, and 16 [4][6].

Core Insights - Nvidia announced the upcoming release of its next-generation AI chip, Rubin, expected to ship in the second half of 2026 [3] - The Rubin platform will utilize NVLink 144 technology, promising a performance increase of 100% compared to its predecessor [3] - Rubin will achieve a processing speed of 50 petaflops during inference, significantly surpassing the current Blackwell chip's 20 petaflops [3] - The chip will support up to 288 GB of fast memory [3] - Following Rubin, Nvidia plans to release Rubin Ultra NVL576 in the second half of 2027, which is projected to be 14 times more powerful than the GB 300 NVL72 [3]