以下文章来源于经济观察报观察家 ，作者陈永伟 经济观察报观察家 . 理性，建设性 本文来自微信公众号： 经济观察报观察家 （ID：eeoobserver） ，作者：陈永伟，题图来自：视觉 中国 北京时间9月19日凌晨，英伟达创始人黄仁勋与英特尔首席执行官陈立武共同召开了一场线上发布 会。会上，黄仁勋宣布了一个爆炸性的消息： 英伟达将向老对手英特尔注资50亿美元，并与其携手 开发革命性的"Intel x86 with RTX"芯片。 虽然黄仁勋本人只是轻描淡写地用一句"英伟达很高兴成为英特尔的股东"来概括这一事件，但只要是 对芯片行业有所关注的人都知道，这次芯片界两大巨擘在历经三十年恩怨情仇后的惊人握手，绝不会 只是一场简简单单的交易。它不仅会震动华尔街的股市曲线，更会在全球科技圈掀起巨大的波澜。 英特尔和英伟达，一个在PC时代铸就了庞大的帝国，将"Intel Inside"的印记深深植入亿万用户的内 心；另一个则在像素与帧数的洪流中崛起，用精湛的图形处理技术点亮了虚拟世界。 为了争夺芯片 市场的主导权，它们曾互为敌手二十余年。如今它们放下恩怨，很可能会带来整个芯片行业的一场重 大洗牌。 在这个节点上，重新回望 ...

国芯网[原:中国半导体论坛] 振兴国产半导体产业！ 不拘中国、 放眼世界 ！ 关注 世界半导体论坛 ↓ ↓ ↓ 9月23日消息，OpenAI与英伟达宣布达成具有里程碑意义的战略合作意向书，英伟达将向投资最多1000 亿美元！ 根据双方的合作协议，OpenAI将部署10GW容量的AI数据中心，用于训练下一代模型，配备400万至500 万块英伟达GPU，相当于英伟达2025年全年出货量。同时，英伟达采用分阶段投资模式：首个1GW数 据中心预计2026年下半年上线(基于Vera Rubin平台)，后续按进度追加投资，总额或达千亿美元。 黄仁勋表示："从首台 DGX 超级计算机到 ChatGPT 的突破性成就，英伟达与 OpenAI 已相互成就十 年。此次投资与基础设施合作标志着新的跨越 —— 以 10GW算力为下一代智能时代赋能。" OpenAI CEO Sam Altman表示，合作将"赋能个人和企业创造新AI突破"，并暗示未来几周将发布计算密 集型新产品。 外媒分析称，对OpenAI而言，该协议解决了其从非营利组织转型为盈利性企业后的最大瓶颈——算力 成本。当前，训练GPT-5级模型的电费支出已占其总成本的35 ...

Core Viewpoint - OpenAI has announced a strategic partnership with NVIDIA to deploy a total of at least 10GW of AI computing systems, with NVIDIA providing up to $100 billion in funding support, indicating a significant investment in AI infrastructure and a potential shift in the industry towards a "compute as capital" model [3][5][6]. Group 1: Partnership Details - OpenAI plans to phase in the deployment of NVIDIA's AI computing systems over several years, involving millions of GPUs [3]. - The partnership is still in the intention stage, with discussions ongoing regarding investment forms, equity structures, and delivery timelines [3][5]. - The collaboration is expected to create a closed-loop ecosystem where capital, computing power, and models drive each other, marking a new phase in AI development [5][6]. Group 2: Market Context and Trends - The AI industry is experiencing rapid growth, with a projected annual growth rate of over 40% for AI data center construction in China over the next two to three years, eventually stabilizing around 10% by 2030 [6]. - The complexity of AI training and inference tasks is increasing, leading to higher power requirements for data centers, with single cabinet power moving from 50kW to 300kW or more [8]. - International projects are achieving higher power densities, with leading projects reaching 120-150kW per cabinet, while domestic projects are currently at 40-60kW per cabinet [8]. Group 3: Challenges and Solutions - The industry faces challenges related to energy consumption approvals and supply uncertainties due to chip export controls [8]. - There is a growing need for green energy solutions in data center construction, as the demand for computing power increases, leading to potential conflicts with energy supply stability [9]. - A proposed solution involves adding a layer of energy storage within computing centers to ensure stable output despite variable energy input, although this model has yet to be implemented in the domestic market [9].

Core Viewpoint - Nvidia and OpenAI have announced a strategic partnership to deploy up to 10 gigawatts of Nvidia systems, significantly enhancing AI infrastructure and capabilities [1][4]. Group 1: Partnership Details - OpenAI will utilize Nvidia's systems to build and deploy at least 10 gigawatts of AI data centers, which will consist of millions of GPUs for training and running advanced AI models [5][6]. - Nvidia's CEO Jensen Huang stated that the 10 gigawatts of power equates to approximately 4 to 5 million GPUs, doubling the number Nvidia is expected to ship this year [6]. - Nvidia plans to invest up to $100 billion in total to support the deployment, with investments made in phases based on the gigawatt deployment progress [6]. Group 2: Technological Advancements - The first phase of the system is expected to be operational by the second half of 2026, based on Nvidia's Vera Rubin platform, which integrates CPUs, GPUs, and dedicated accelerators for complex AI tasks [6]. - The collaboration aims to push the boundaries of AI technology, with both companies emphasizing the importance of computational infrastructure for future economic growth [7][8]. Group 3: Market Impact - Following the announcement, Nvidia's stock price rose nearly 4%, adding approximately $170 billion to its market capitalization, which is now close to $4.5 trillion [9].

英伟达创始人兼首席执行官黄仁勋（Jensen Huang）表示："十年来，从首款DGX超级计算机到ChatGPT的突破性进展，英伟达与OpenAI一直相互推动、 共同发展。此次投资与基础设施合作标志着双方迈入新的飞跃阶段——我们将部署10吉瓦的算力，为智能新时代提供动力。" OpenAI联合创始人兼首席执行官山姆·奥特曼（Sam Altman）称："一切都始于算力。算力基础设施将成为未来经济的基础，我们将借助与英伟达共同搭建 的技术体系，一方面实现人工智能领域的新突破，另一方面大规模地将这些技术成果赋能个人与企业。" OpenAI联合创始人兼总裁格雷格·布罗克曼（Greg Brockman）表示："自OpenAI创立初期，我们就与英伟达保持着密切合作。我们借助他们的平台打造出 了亿万人日常使用的人工智能系统。如今，我们很高兴能与英伟达携手部署10吉瓦算力，以此拓展智能的边界，并让这项技术的红利惠及每一个人。" OpenAI将与英伟达合作，作为其AI工厂增长计划的首选战略计算和网络合作伙伴，同时，双方还将共同优化OpenAI模型和基础架构软件以及英伟达硬件 和软件的路线图。 （文章来源：上海证券报） 当地时间9 ...

Core Insights - NVIDIA is extending its dominance in data centers to the physical world with the launch of the Jetson Thor robot computing platform, which features a significant performance leap of 7.5 times compared to its predecessor [2][3] - The release of Jetson Thor is seen as a critical move for NVIDIA to initiate its "second growth curve," with expectations of substantial growth in its automotive and robotics segments, projected to increase by 80% year-over-year [2][9] Product Launch - Jetson Thor, priced at $3,499, boasts an AI computing power of 2070 TFLOPS and is designed to run complex generative AI models locally on robots, enhancing their real-time perception and decision-making capabilities [2][3] - The platform introduces multi-instance GPU (MIG) technology, allowing for the safe operation of high-safety motion control tasks alongside non-critical AI applications, thereby improving reliability [3] Ecosystem Development - NVIDIA is building a comprehensive ecosystem around Jetson Thor, which includes a triad architecture: AI factories for training, digital twins for simulation, and the Jetson Thor for deployment in physical environments [4][5] - Major companies, including Boston Dynamics, Amazon Robotics, and OpenAI, are early adopters of Jetson Thor, signaling its importance in developing advanced robotics [5][6] Market Expectations - The upcoming Q2 earnings report is highly anticipated, with analysts predicting total revenue of approximately $45.8 billion, a year-over-year increase of about 52% [8][9] - The automotive and robotics sectors are expected to be the main highlights of the earnings report, with a projected acceleration in growth from 72% to 80% [9][10] Challenges Ahead - Despite the powerful capabilities of Jetson Thor, the broader robotics industry faces challenges in achieving human-like flexibility and functionality, which may delay widespread adoption [8] - The success of Jetson Thor as a growth engine depends on the scalability of robotics applications and the maturity of the physical environment [8][10]

Group 1 - Nvidia's CEO Jensen Huang emphasized the importance of the Chinese market, stating it has cultivated 50% of the world's AI talent [3][9][10] - The company has made significant advancements in AI technology, with the introduction of the DeepSeek open-source inference model, which is considered revolutionary [3][4][10] - Nvidia's stock reached an all-time high, with a market capitalization exceeding $4.1 trillion, reflecting strong investor confidence [2][5] Group 2 - Nvidia has a long history in the AI infrastructure sector, starting from gaming chips in 1993 and evolving into a leader in AI computing [4][5] - The company has developed technologies that have increased AI computing power by 100 times, significantly outpacing Moore's Law [5][10] - Nvidia's partnerships with major Chinese tech companies like Tencent, Alibaba, and Xiaomi have been crucial for its growth and innovation in the region [4][6] Group 3 - Huang highlighted the rapid development of AI applications in China, driven by intense market competition and a strong educational system [3][6][10] - The company is focused on creating a global AI ecosystem, with applications ranging from healthcare to autonomous vehicles [6][8] - Nvidia's H20 chip is particularly suited for training large models, while the RTX Pro is designed for digital twins and robotic simulations [7][10] Group 4 - Huang praised Huawei for its strong technological foundation in chips, systems, and networks, asserting that underestimating Chinese manufacturing capabilities is naive [9][10] - The future of AI is expected to be driven by robotics, with a focus on automation to address global labor shortages [11][12] - Huang expressed optimism about the unique advantages China possesses in AI technology, mechanical engineering, and manufacturing, which will foster the growth of the robotics industry [11][12]

Core Viewpoint - Nvidia's CEO Jensen Huang emphasized the company's pivotal role in AI infrastructure and its long-term vision for the industry during his keynote at COMPUTEX 2025, highlighting the evolution of AI technology and Nvidia's strategic initiatives in Taiwan and globally [3][4][6]. Group 1: Nvidia's AI Infrastructure and Business Developments - Nvidia has been in Taiwan for 30 years and is now focusing on building AI infrastructure, similar to how Siemens built electrical infrastructure during the industrial revolution [4][6]. - The company is fully producing the Grace Blackwell machine, with the GB300 chip set to enhance inference performance by 1.5 times and increase HBM memory capacity by 1.5 times [5]. - Nvidia is developing a quantum computing platform, predicting that future supercomputers will integrate GPU, QPU (quantum processing unit), and CPU technologies [5][6]. Group 2: New AI Products and Innovations - The DGX Spark, a personal AI computer, has entered full production and is expected to launch soon, while the DGX Station can operate on standard wall power and run AI models with 1 trillion parameters [6][7]. - Huang discussed the concept of agent-based AI, which possesses reasoning capabilities and can solve previously unseen problems, marking a significant advancement in AI technology [6]. Group 3: Strategic Partnerships and Global Expansion - Nvidia is collaborating with major Taiwanese companies like TSMC and Foxconn to build a giant AI supercomputer in Taiwan, enhancing the local AI ecosystem [5][6]. - The company has also established a partnership with Saudi Arabia's Public Investment Fund (PIF) to supply 18,000 AI chips for a newly formed AI company, Humain [7].