【如需和我们交流可扫码添加进社群】 英伟达（纳斯达克代码：NVDA）是当前 AI 基础设施革命的核心玩家，如今的它早已不是三 四年前我们印象中单纯的 "芯片公司"。 实际上， 英伟达已从一家芯片设计公司，逐步发展到有望在多个垂直领域全面主导 AI 基础设 施的程度。 这也是为什么很难看空英伟达这类公司的核心原因之一。 它的 竞争壁垒极强，几乎无法复制，但即便过去五年股价暴涨 1250%，其当前的估值倍数却 并未充分体现这一优势。 传统 CPU 本质上是按顺序执行任务，一次处理一个流程；而英伟达 的 GPU 采用并行计算，能同时处理成千上万的运算。在 AI 模型的训练和运行过程中，需要 并行处理海量数据，这种架构的重要性由此凸显。 此外，英伟达的 CUDA 软件层和开发者生态进一步放大了这一优势 ——CUDA 已成为 AI 编 程领域事实上的标准。这就意味着，开发者一旦接入这个生态，就很难再切换到其他平台，转 换成本极高，进而不断扩大英伟达的竞争壁垒。虽然这是英伟达的长远目标，目前这一趋势已 初步显现，但公司大部分收入仍来自硬件销售。 除此之外，英伟达近期还与 OpenAI 等企业达成合作，计划部署至少 10 ...

OpenAI’s push to build next-generation AI supercomputers has triggered an intense competition among chipmakers. Nvidia (NASDAQ:NVDA), the undisputed GPU leader, has pledged as much as $100 billion to fund OpenAI’s massive data center build out, with the AI company set to fill those facilities with millions of Nvidia chips. AMD, meanwhile, struck its own partnership to deploy about 6 gigawatts worth of its accelerators for OpenAI. AMD stock has surged close to 30% since it announced its OpenAI deal, while Nv ...

Key Takeaways Intel stock jumped 88.2% in six months, outpacing its industry but trailing AMD and NVIDIA.New interim Co-CEOs reaffirm Intel's core strategy while reviewing operations for growth.$14.86 billion in new funding boosts AI ambitions, yet falling earnings estimates cloud outlook.Intel Corporation (INTC) has surged 88.2% in the past six months compared with the industry’s growth of 87.6%. It has, however, lagged its peers, Advanced Micro Devices, Inc. (AMD) and NVIDIA Corporation (NVDA) . Advance M ...

Core Insights - Anthropic has secured $13 billion in funding, leading to a valuation of $183 billion, and plans to double its overseas workforce and quadruple its AI team within the year [1] - The demand for the Claude model is driving rapid growth, with the number of clients increasing from under 1,000 to 300,000 in just four years [1] Group 1: Company Background and Positioning - OpenAI, founded in 2015, initially aimed for non-profit goals but shifted focus to commercialization after the success of its GPT series, particularly after receiving significant investment from Microsoft [2][3] - OpenAI's growth is heavily supported by Microsoft, which provides not only funding but also essential computing power through Azure, making OpenAI a strategic asset for Microsoft in the cloud computing market [3][4] - Anthropic was founded by former OpenAI team members dissatisfied with the focus on AGI over safety, positioning itself as a reliable and secure alternative, particularly targeting regulated industries like finance and healthcare [6][7] Group 2: Financial Performance and Growth - Anthropic's revenue has surged from an annualized $1 billion to $5 billion in just two years, with 80% of its income derived from enterprise subscriptions and API calls [6] - Amazon has invested heavily in Anthropic, initially committing $4 billion and later increasing it to $8 billion, viewing Claude as a key model for its AWS platform [6][8] Group 3: Competitive Dynamics - The competition between OpenAI and Anthropic reflects a broader struggle between Microsoft and Amazon in the cloud computing space, with each company leveraging its respective AI partnerships to gain market share [9][20] - Microsoft Azure's market share has increased significantly, reaching 24% globally, while AWS's share has declined to 30%, indicating a tightening competitive landscape [18][21] Group 4: Strategic Partnerships and Dependencies - The relationship between AI companies and their cloud providers is critical, as access to computing power is essential for model training and development, leading to a reliance on these partnerships [10][11] - Anthropic's strategy involves maintaining flexibility in partnerships, having secured backing from both AWS and Google, while also keeping options open with Microsoft [13][22] Group 5: Market Trends and Future Outlook - The AI industry faces challenges related to the scarcity of computing resources, particularly GPUs, which are essential for training large models, creating a competitive environment for access to these resources [10][25] - Regulatory pressures and energy costs are emerging as significant factors that could impact the growth and operational strategies of AI companies, with potential implications for their partnerships and market positioning [26][28]

Group 1 - The report from Dongfang Securities highlights that process technology upgrades drive chip performance improvements, while advanced packaging serves as another key driver for enhancing chip capabilities [1] - In the context of slowing process technology upgrades, increasing the die area can enhance transistor count and computational power, with Nvidia's H100 die area nearing the reticle limit of approximately 800-900 mm² [1] - Nvidia's B200 adopts advanced packaging with dual die integration, achieving 208 billion transistors in a single package, which is more than double the 80 billion transistors in the H100 [1] - According to Nvidia's roadmap, the Rubin Ultra will integrate four dies in a single package, targeting a computational power of 100PF FP4 per card [1] Group 2 - System assembly is emerging as a new driver for AI server performance enhancement, as wafer manufacturing and advanced packaging may not keep pace with the growing demand for AI computing power [2] - The number of GPUs in AI servers is expected to increase from 8 per server to 72 per cabinet, with projections for the VR Ultra NVL576 cabinet in 2027 to support 144 GPUs, each with four die, totaling 576 die [2] - The increase in GPU count raises cooling requirements and complicates system assembly, exemplified by the production ramp-up challenges faced by GB200 NVL72 due to assembly difficulties [2] - Leading companies in the industry are likely to benefit from the rising technical barriers and improved competitive environment in system assembly [2] Group 3 - In terms of investment targets, companies related to AI server system assembly are maintained, including Industrial Fulian, which has significantly optimized GB200 series product testing and reduced cabinet debugging time [3] - Industrial Fulian has expanded capacity globally and introduced fully automated assembly lines, expecting strong growth in GB200 shipments, primarily driven by large North American cloud service providers [3] - Haiguang Information's merger with Zhongke Shuguang is anticipated to create vertical integration capabilities encompassing CPU, DCU, and system assembly [4] - Lenovo is expected to launch various servers based on Blackwell Ultra starting in the second half of 2025, as indicated by Nvidia [4] - Huaqin Technology, a core ODM supplier for domestic internet firms' AI servers, benefits from the capital expenditure expansion of downstream cloud companies [4]

Core Insights - The potential investment of up to $100 billion by Nvidia in collaboration with OpenAI to build a 10 GW AI data center highlights the financialization of computing power [1] - In 2024, global generative AI financing reached $56 billion, accounting for over half of the total AI industry financing, with major companies like Microsoft and Google significantly increasing their capital expenditures [1] - The shift from traditional GPU purchasing to a rental model is emerging as a solution to the challenges faced by AI companies, allowing for more flexible financial management [2][4] Financialization of GPUs - Traditional GPU procurement involves significant upfront costs and depreciation, which has become unsustainable due to rapid technological advancements [2] - The rental model transforms GPUs into financial products that can be leased, financed, and traded, mitigating the risks associated with ownership [4][5] - Companies like CoreWeave and Lambda Labs are leading the way in GPU rental services, with CoreWeave securing $1.7 billion in funding and Lambda Labs offering hourly rental services [5] Capital Logic of Computing Power - The financialization of computing power may disrupt the AI industry more profoundly than innovations like ChatGPT, as it introduces new investment opportunities and risks [6][8] - Future developments may include the securitization of GPU rental contracts, allowing for trading in capital markets and creating a new asset class [7] - The concentration of capital, computing power, and energy resources in the U.S. is likened to an oligopoly, where larger companies can leverage financing to maintain a competitive edge [9][11] Challenges for China - China's hardware and financial systems lag behind the U.S., with export controls limiting access to advanced GPUs and a lack of a mature financial infrastructure for computing power [12] - Chinese companies are exploring algorithm optimization and efficiency improvements, but without a robust GPU rental market and credit rating system, they risk being marginalized [12] - The need for China to develop its own GPU leasing market and financial infrastructure is critical to avoid being sidelined in the global computing power landscape [12] Conclusion - The rumored collaboration between OpenAI and Nvidia signifies a shift in industry logic, where the financialization of GPUs could accelerate AI development while potentially exacerbating inequalities in access to computing resources [13][14]

Core Viewpoint - Nvidia is transforming itself into a "central bank" for the global AI industry by proactively investing in capacity and securing long-term contracts with clients, thereby shifting its business model from selling hardware to controlling the supply of AI computing power [4][9][20]. Group 1: Nvidia's New Strategy - Nvidia's new strategy involves a $100 billion investment and a plan to build 10GW of AI computing capacity, which could generate approximately $3.5 trillion in revenue over the next few years [7][16]. - The company is moving from a traditional chip sales model to a model where it actively shapes demand and locks in future orders through long-term contracts [11][18]. - This shift allows Nvidia to reduce revenue volatility and enhance its valuation logic, positioning it as a "computing power operator" rather than just a hardware seller [18][20]. Group 2: Partnership with Intel - Nvidia has made a strategic investment of $5 billion in Intel, acquiring about 4% of its shares, which will facilitate collaboration on AI-optimized solutions [22][26]. - The partnership aims to integrate Nvidia's GPUs with Intel's custom x86 CPUs, creating a comprehensive solution for AI training and deployment [27]. - This collaboration is expected to enhance Nvidia's influence in the AI computing standard and provide Intel with much-needed capital and market confidence [27][28]. Group 3: Implications for the AI Industry - The 10GW expansion and partnership with Intel are set to drive growth across the entire AI industry chain, allowing investors to diversify their exposure beyond Nvidia [29][30]. - The current wave of AI infrastructure investment is likened to historical capital expenditure cycles, indicating a long-term growth trajectory for Nvidia [31][32]. - Nvidia's strategy effectively locks in the arms race for AI computing power within its supply chain, positioning it as a key player in the industry [33]. Group 4: Risks and Challenges - Potential risks include dependency on OpenAI's financial health and demand, as well as regulatory scrutiny due to Nvidia's dual role as an investor and supplier [28][37]. - The success of the Nvidia-OpenAI partnership hinges on OpenAI's ability to achieve explosive growth, which remains uncertain given the competitive landscape [40][42]. - Nvidia's dominance in the AI chip market could be challenged by advancements from competitors like Google and Amazon, necessitating continuous innovation [37][42].

电子行业 行业研究 | 动态跟踪 系统组装——AI 服务器升级的新驱动力 核心观点 投资建议与投资标的 风险提示 AI 落地不及预期；英伟达产品迭代进度不达预期；相关公司产能爬坡不达预期 国家/地区 中国 行业 电子行业 报告发布日期 2025 年 09 月 28 日 看好（维持） | 韩潇锐 | 执业证书编号：S0860523080004 | | --- | --- | | | hanxiaorui@orientsec.com.cn | | | 021-63326320 | | 蒯剑 | 执业证书编号：S0860514050005 | | | 香港证监会牌照：BPT856 | | | kuaijian@orientsec.com.cn | | | 021-63326320 | | 薛宏伟 | 执业证书编号：S0860524110001 | | | xuehongwei@orientsec.com.cn | | | 021-63326320 | | 朱茜 | 执业证书编号：S0860123100018 | | --- | --- | | | zhuqian@orientsec.com.cn | | | 021 ...

【文/观察者网 陈思佳】近年来，美国政府实施了一系列针对中国的芯片出口限制，企图打压中国芯片 产业发展。但美国科技企业发现，美国的围堵打压并不能阻止中国科技的进步，中国企业正在努力绕开 美国。 当地时间9月16日，路透社独家报道称，英伟达推出多款对华"减配特供版"芯片，但越来越多的中国买 家已不愿买单。比如，英伟达最新为中国市场量身定制的AI芯片RTX6000D需求低迷，其主要大客户 ——多家中国科技巨头拒绝下单。 他还补充说，他相信中国会对外部投资保持开放，"外国公司在中国投资、在中国竞争并且互动之间形 成活跃的竞争，这符合中国的利益。他们也希望能够走出中国，参与全球竞争。" 英伟达首席执行官黄仁勋 视频截图 随着AI技术高速发展，英伟达的图形处理器（GPU）业务水涨船高。但近年来，美国政府为打压中国 芯片发展，实施了严格的出口管制。今年早些时候，美国政府禁止英伟达对华出口H20芯片，直到英伟 达同意向美国政府上缴在华销售额的15%后，才恢复了该芯片的出口许可。 H20是英伟达是遵守美国的出口管制而专为中国市场设计的"减配版"AI芯片，性能仅为旗舰产品H100的 15%-30%，基于英伟达较旧的Hopper ...

9月，英伟达宣布将向OpenAI投资1000亿美元，并签署部署至少10GW算力的意向书。 大家都知道英伟达是一家卖GPU的公司，但你可能没意识到：它正在悄悄把自己变成全球AI行业的"央行"。 10GW是什么概念？它相当于全球最大规模AI集群的数倍，意味着未来三到五年英伟达将为OpenAI提供前所未有的算力和设备支持。 这不是一次普通的供货交易，而是英伟达主动出击，用自己的资金帮助客户建设产能，再通过长期合同锁定未来订单，形成一个堪称"算力央 行"的闭环。 今天我们就来深度拆解：英伟达为什么要这样做，这对它的商业模式意味着什么，潜在的机会和风险在哪里，普通投资者又能从中捕捉到哪些 机会。 英伟达"央行式"新战略 要理解英伟达的动作，首先要回顾它过去的商业模式。英伟达长期以来是一家典型的芯片公司，设计GPU、交给台积电代工，然后卖给OEM、 云厂商或者AI公司。收入以一次性确认为主，周期性波动非常明显。 2023到2024年，AI训练需求爆发，H100、H200供不应求，英伟达的营收和利润暴涨，毛利率一度超过75%，市值突破3万亿美元，成为全球最 有价值的科技公司。 | 名称 | 代码 | 毛利率 | | --- ...