对于其他人工智能个股，摩根士丹利认为美光科技(MU.US)在未来几个季度将面临负面情绪。 摩尔表示："对美光而言，当前HBM 3e的高带宽内存定价明年将至少与一个客户——英伟达(NVDA.US) ——进行硬重置，英伟达已承诺2025年全年定价。但市场情绪相当负面，我们预计HBM将保持对DDR5 有意义的溢价，尽管溢价幅度在收窄。" 本月早些时候，迈威尔科技以25亿美元的价格完成了将其汽车以太网业务出售给英飞凌的交易。该业务 在2026财年预计将贡献2.25亿至2.5亿美元的营收。 以约瑟夫·摩尔为首的大摩分析师表示："预计本季度光学业务将带来上行潜力；在剥离汽车业务后，我 们略微下调了预期，但排除该影响后，我们预计业绩指引将呈积极态势。" 摩尔补充道："人工智能业务收入在7月财季预计为8.76亿美元(环比增长6.6%)，在10月财季预计为9.55 亿美元(环比增长9.0%)，其中增长较快的是专用集成电路(ASIC)。我们认为，在强劲的人工智能增长势 头推动下，光学业务的表现可能超出我们的预期，相比ASIC前景，我们更看好光学业务的前景……除 了短期供应问题外，我们认为光学业务比普遍认为的更强劲，比ASIC业务 ...

2025年08月24日 证券研究报告 | 数据中心互联技术专题四： CSP云厂AI军备竞赛加速，智算中心架构快速发展 国信通信·行业专题报告 行业研究 · 行业专题 通信 投资评级：优于大市（维持评级） 证券分析师：袁文翀 联系人：赵屿 021-60375411 021-61761068 yuanwenchong@guosen.com.cn zhaoyu6@guosen.com.cn S0980523110003 请务必阅读正文之后的免责声明及其项下所有内容 投资摘要 CSP互联网云厂AI军备竞赛进入2.0时代，智算中心互联技术发展快速迭代。自2023年，ChatGPT3.5 点燃 "大模型革命"起，AI发展万众 瞩目，各大科技公司纷纷投入大模型研发并加大智算中心建设。根据CSP厂商的Capex指引，预计2025年，海外亚马逊、谷歌 、微软 、 Meta四家厂商合计Capex增至3610亿美元，同比增幅超58%；国内字节、腾讯、阿里Capex有望超过3600亿元。本轮AI浪潮前期，英伟达作为 AI芯片领军企业，其AI芯片供不应求；随着CSP云厂持续加大智算中心投入，具备更高性价比的自研ASIC算力芯片成为AI ...

Investment Rating - The report maintains a "Buy" rating for Tianfu Communication, Xingwang Ruijie, Ruijie Network, China Mobile, China Telecom, China Unicom, Huace Navigation, and Hengtong Optoelectronics, while recommending "Hold" for Huafeng Technology [9][50]. Core Insights - North American cloud service providers (MAMG: Microsoft, Amazon, Meta, Google) reported a 69% year-on-year increase in capital expenditures (CapEx) for Q2 2025, totaling $87.4 billion, indicating strong demand for computing power [1][2][15]. - The report anticipates that the total CapEx for 2025 will reach $333.8 billion, reflecting a 49% year-on-year growth, with optimistic guidance from major players [4][15]. - The report suggests that the robust CapEx growth from overseas cloud service providers will continue to boost confidence in computing power demand, benefiting both the overseas computing supply chain and domestic internet companies [1][15]. Summary by Sections Market Performance - The communication index rose by 2.54% last week, while the Shanghai Composite Index fell by 0.94% and the Shenzhen Component Index dropped by 1.58% [1][15]. Key Companies and Dynamics - The report highlights key companies in the AI computing supply chain for 2025, recommending Tianfu Communication, Xingwang Ruijie, Ruijie Network, and Huafeng Technology, as well as core asset value reassessment for China Mobile, China Telecom, and China Unicom [5][9]. - Major cloud providers' CapEx for Q2 2025 includes Microsoft ($17.08 billion, +23%), Amazon ($31.37 billion, +91%), Meta ($16.54 billion, +102%), and Google ($22.45 billion, +70%) [16]. Capital Expenditure Guidance - Microsoft expects its Q1 FY26 CapEx to exceed $30 billion, while Amazon's Q2 CapEx rate is projected to represent the investment rate for the second half of the year [4][16]. - Meta has raised its 2025 CapEx guidance to $66-72 billion, and Google has increased its guidance to $85 billion [4][16]. Investment Recommendations - The report emphasizes the importance of focusing on the global AI computing supply chain, including components like optical modules, liquid cooling, copper connections, and switches [1][15]. - The report also notes the expected growth in domestic internet companies' investments driven by the positive outlook from overseas cloud service providers [1][15].

Investment Rating - The report maintains an "Overweight" rating for the communication industry and communication equipment manufacturing sector [8]. Core Insights - North American cloud service providers (CSPs) have shown a significant increase in capital expenditures (CapEx), with a 69% year-on-year growth in Q2 2025, totaling $87.4 billion. This trend is expected to continue, with a projected total CapEx of $333.8 billion for 2025, reflecting a 49% increase year-on-year [2][12]. - Major cloud companies such as Microsoft, Amazon, Meta, and Google have provided optimistic guidance for their 2025 CapEx, indicating strong demand for AI and cloud services. Microsoft anticipates over $30 billion in CapEx for Q1 FY26, while Amazon expects a capital expenditure rate of 18.7% for the second half of the year [11][13]. - The report suggests that the robust CapEx from overseas CSPs will boost confidence in computing power demand, benefiting both the overseas computing supply chain and domestic internet companies [1][11]. Summary by Sections Market Performance - The communication index rose by 2.54% last week, while the Shanghai Composite Index and Shenzhen Component Index fell by 0.94% and 1.58%, respectively [1][11]. Key Companies and Dynamics - The report highlights several companies as key investment opportunities in the AI computing chain for 2025, including Tianfu Communication, Xingwang Ruijie, Ruijie Network, and Huafeng Technology. It also emphasizes the core asset value reassessment of major telecom operators like China Mobile, China Telecom, and China Unicom [3][8]. Capital Expenditure Insights - The report details the Q2 2025 CapEx for the four major cloud providers: Microsoft ($17.1 billion, +23%), Amazon ($31.4 billion, +91%), Meta ($16.5 billion, +102%), and Google ($22.4 billion, +70%) [12][13]. - The optimistic outlook for 2025 CapEx includes upward revisions from Meta and Google, with Meta's guidance adjusted to $66-72 billion and Google's to $85 billion [2][12]. Recommended Stocks - The report recommends several stocks with target prices and investment ratings, including: - Tianfu Communication (Buy, target price: 119.12) - Xingwang Ruijie (Buy, target price: 35.65) - Ruijie Network (Buy, target price: 88.70) - Huafeng Technology (Hold, target price: 59.86) - China Mobile (Buy, target price: 126.40) - China Telecom (Buy, target price: 9.13) - China Unicom (Hold, target price: 7.62) [8][46].

Investment Rating - The report does not explicitly state an investment rating for the green computing industry Core Insights - Green computing is driven by the dual engines of "decarbonization" and "digitalization," making it a crucial component of new productive forces in the AI era [2][3] - The global computing power is projected to grow at a rate exceeding 50% over the next five years, with China's computing power reaching 230 EFLOPS, averaging a growth rate of nearly 30% over the past five years [2] - The energy consumption of AI data centers is expected to rise significantly, with IT energy consumption reaching 77.7 TWh in 2025 and 146.2 TWh by 2027, reflecting a compound annual growth rate of 44.8% from 2022 to 2027 [2] - Green computing encompasses three main areas: indirect carbon emissions from energy sourcing, algorithm selection and data center operations, and enabling industry transformation for carbon reduction [4][5] Summary by Sections Macro Perspective - Green computing is an inevitable choice in the AI era, serving as a key driver for the development of new productive forces [2][3] - The report highlights the importance of balancing efficient supply and sustainable development in the computing power industry [3] Mid-level Analysis - The carbon footprint of green computing includes indirect emissions from energy sourcing, lifecycle emissions from infrastructure, and direct emissions from operations [4] - The ECCI framework emphasizes efficient computing, energy conservation, clean collaboration, and inclusive usage [5][36] Micro-level Practices - Leading tech companies are implementing innovative green computing practices, such as Amazon's AWS migration reducing carbon emissions by 99%, Google's 24/7 carbon-free energy operations, and Microsoft's circular centers achieving a 90.9% server remanufacturing rate [7][8] - Tencent's deployment of renewable energy facilities and Alibaba Cloud's immersion cooling technology are notable examples of green computing initiatives in China [8]

Core Viewpoint - Advanced packaging is becoming a key differentiator in the high-end mobile market, offering higher performance, greater flexibility, and faster time-to-market compared to System on Chip (SoC) solutions [2][5]. Group 1: Market Trends - Advanced packaging technologies, such as multi-chip components, are essential for AI inference and adapting to rapid changes in AI models and communication standards [2][5]. - The high-end mobile market is increasingly adopting multi-chip assembly, moving beyond single-chip SoC solutions due to the need for enhanced performance and flexibility [5][8]. - The transition from single-chip SoCs to 2.5D systems is driven by the demand for higher computational capabilities and the limitations of traditional scaling methods [5][6]. Group 2: Technical Insights - Single-chip SoCs are efficient and cost-effective for low-end devices, integrating all necessary components on a single silicon die [3][10]. - Multi-chip components allow for greater diversity in processing units, including combinations of CPUs, GPUs, and specialized accelerators, enhancing performance for high-end applications [5][6]. - The use of advanced 3D and 2.5D packaging technologies enables vertical stacking of chips, increasing interconnect bandwidth and processing capabilities [5][6]. Group 3: AI Integration - AI capabilities are increasingly being integrated at the silicon level in high-end mobile devices, with companies like NVIDIA and Arm developing specialized hardware for AI workloads [14][15]. - The design of chips is influenced by the need to support evolving AI functionalities and communication standards, requiring flexibility in silicon design [11][18]. - Companies are exploring various configurations for AI accelerators, either integrating them into a single chip or using separate chips to optimize performance [10][14]. Group 4: Power and Efficiency - Power consumption remains a critical concern, with the need for efficient processing to extend battery life and manage heat dissipation in mobile devices [12][16]. - Innovations in chip design, such as lightweight pipelines and local data reuse, are aimed at improving power efficiency while maintaining high performance [15][16]. - The introduction of eSIM technology is an example of how companies are reducing power consumption and enhancing design flexibility in mobile devices [16].

Core Insights - OpenAI's shift to Google TPU chips marks a significant turning point in AI infrastructure, providing Google with a strong endorsement of its capabilities and potentially accelerating growth in its cloud business [1][2] - The collaboration allows OpenAI to reduce reliance on Microsoft's data centers while challenging NVIDIA's dominance in the GPU market [2][3] - Morgan Stanley projects substantial spending on NVIDIA GPUs, with estimates of $243 billion in 2027 and $258 billion in 2028, compared to approximately $21 billion and $24 billion for TPU [2] Group 1 - OpenAI's large-scale adoption of Google TPU chips represents its first significant move away from NVIDIA, indicating a strategic shift in its computing resources [2] - The partnership is expected to drive Google Cloud revenue growth, which has not yet been reflected in GOOGL's stock price [2][3] - The increasing familiarity of developers with TPU technology may lead to further adoption by companies outside of Google, providing additional growth opportunities for Google Cloud [3] Group 2 - NVIDIA is facing capacity constraints but is still projected to see revenue from Google customers grow over threefold this year, exceeding $20 billion [4] - The demand for alternative architectures is driven by a shortage in inference capabilities, highlighting Google's competitive advantage in the market [5] - Amazon AWS's absence from OpenAI's partner list raises concerns about its capacity constraints and the competitiveness of its Trainium chips [6][7]

Core Insights - Morgan Stanley indicates that OpenAI, supported by Microsoft, may utilize Google's Tensor Processing Units (TPUs) for its AI inference tasks, marking a significant endorsement of Google's hardware technology [1] - The use of Google's TPUs signifies a diversification of OpenAI's suppliers, which previously relied solely on NVIDIA's chips for training and inference calculations [1][2] - This partnership is expected to accelerate the growth of Google Cloud's business and enhance market confidence in Google's AI chip capabilities [1] Company and Industry Analysis - OpenAI is recognized as one of the most notable TPU customers, alongside Apple, Safe Superintelligence, and Cohere, highlighting Google's decade-long development of AI infrastructure [2] - Despite not being able to access Google's most advanced TPUs, OpenAI's choice to collaborate with Google underscores the latter's leading position in the broader Application-Specific Integrated Circuit (ASIC) ecosystem [2] - The decision to use Google's TPUs may be influenced by the limited supply of NVIDIA GPUs due to high demand, which could negatively impact Amazon's AWS and its custom Trainium chips [2] - OpenAI's collaboration with Google allows it to run AI workloads across major cloud service providers, including Google Cloud, Microsoft Azure, Oracle, and CoreWeave, with Amazon being a notable absence [2]

Core Insights - OpenAI's shift to Google TPU chips marks a significant turning point in AI infrastructure, providing Google with a strong endorsement of its capabilities and potentially accelerating growth in its cloud business [1][2] - The collaboration allows OpenAI to reduce its reliance on Microsoft data centers while challenging NVIDIA's dominance in the GPU market [2][3] - Morgan Stanley projects substantial spending on NVIDIA GPUs, with estimates of $243 billion in 2027 and $258 billion in 2028, while TPU spending is expected to be around $21 billion and $24 billion in the same years [2] Group 1: Google and OpenAI Collaboration - OpenAI's adoption of Google TPU chips is its first large-scale use of non-NVIDIA hardware, which could lower inference computing costs [2] - This partnership is seen as a major recognition of Google's AI infrastructure capabilities, with OpenAI being the most significant TPU customer to date [2][3] - The collaboration is expected to drive accelerated growth in Google Cloud revenue, which has not yet been reflected in GOOGL's stock price [2] Group 2: NVIDIA's Market Position - Despite facing capacity constraints, NVIDIA is projected to see its revenue from Google clients grow over threefold this year, exceeding $20 billion [4] - NVIDIA's processor market share is expected to approach 65%, indicating strong demand despite current supply issues [4] - The demand for alternative architectures is driven by a shortage in inference capabilities, highlighting Google's differentiated advantage in the market [4] Group 3: Amazon AWS Challenges - OpenAI's absence from AWS indicates potential capacity constraints at Amazon, which may not meet OpenAI's requirements [5] - The choice of OpenAI to use Google's TPU over AWS's Trainium chips suggests competitive disadvantages for Amazon in the custom silicon space [5] - This dynamic is likely to increase investor scrutiny on AWS's growth and expectations for acceleration in the latter half of the year [6]

Group 1: Broadcom's Financial Performance - Broadcom reported Q2 earnings per share of $1.58, exceeding the target of $1.56, with revenue of $15 billion, a 20% year-over-year increase, slightly above the analyst consensus of $14.99 billion [1] - The company's net profit for the quarter was $4.97 billion, more than double the $2.12 billion from the same period last year [1] - Broadcom expects Q3 revenue to be approximately $15.8 billion, higher than Wall Street's expectation of $15.7 billion [1] Group 2: AI Chip Business Growth - Broadcom's AI revenue reached $4.4 billion in the last quarter, a 46% increase year-over-year, driven by demand for new network chips like the Tomahawk 6 series [2] - The company is developing custom AI chips for three major cloud service providers, indicating strong ongoing investment in AI [2] - Broadcom anticipates AI revenue to grow to $5.1 billion in the upcoming quarter, supported by large-scale partners like AWS, Microsoft, and Google [3] Group 3: Marvell's Financial Performance - Marvell reported Q1 earnings per share of $0.62, slightly above the Wall Street estimate of $0.61, with revenue of $1.9 billion, a 63% year-over-year increase [6] - The company's net profit for the quarter was $177.9 million, a significant turnaround from a loss of $200.2 million in the same period last year [6] - Marvell's data center business saw a remarkable 76% revenue growth year-over-year, reaching $1.44 billion [6] Group 4: Marvell's Business Segments - Marvell's operator infrastructure segment experienced a 93% increase in sales, reaching $138.4 million, while the consumer segment grew by 50% to $63.1 million [7] - The only segment that saw a decline was the automotive and industrial business, which decreased by 2% to $75.7 million [6] - Marvell expects revenue for the upcoming quarter to be around $2 billion, slightly above Wall Street's forecast of $1.99 billion [6] Group 5: Market Sentiment and Analyst Opinions - Analysts from Melius Research labeled Broadcom as a "must-hold" AI stock due to its strong relationships with fabless chip suppliers and the expected growth in its network business [3] - Marvell's CEO praised the company's record revenue and anticipated continued strong growth driven by AI demand in data centers [7] - Despite strong performance, Marvell's stock has underperformed this year, with a 42% decline year-to-date [8]