Core Insights - NVIDIA announced the JUPITER supercomputer as the fastest in Europe, achieving over 2x speedup for high-performance computing and AI workloads compared to the next-fastest system [1][2] - JUPITER is expected to run at 1 quintillion FP64 operations per second, positioning it as Europe's first exascale supercomputer, facilitating advancements in various scientific fields [2][3] - The supercomputer is recognized for its energy efficiency, delivering 60 gigaflops per watt, and is built on Eviden's BullSequana XH3000 liquid-cooled architecture [3][5] Technological Advancements - JUPITER comprises nearly 24,000 NVIDIA GH200 Grace Hopper Superchips and is expected to reach over 90 exaflops of AI performance [3][4] - The system integrates NVIDIA's full software stack, enhancing performance across multiple applications, including climate modeling and quantum research [3][6] - It is designed to support hybrid quantum HPC-computation, utilizing tools like the NVIDIA CUDA-Q platform and cuQuantum SDK [5][11] Strategic Importance - The supercomputer is hosted by the Jülich Supercomputing Centre and owned by the EuroHPC Joint Undertaking, marking a significant step for European scientific and technological sovereignty [4][5] - JUPITER's capabilities are expected to catalyze foundational research in diverse fields such as climate modeling, energy systems, and biomedical innovation [5][6] - Early testing with the Linpack benchmark confirms JUPITER's performance, contributing to its ranking among the top five systems on the TOP500 list [5]

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容 编译自lightreading 。 尽管目前为止，电信行业对英伟达的兴趣有限，但该公司并未放弃向电信行业销售人工智能芯片的 努力。但这家图形处理器 (GPU) 巨头似乎已转向低端市场，在其最新的无线接入网 (RAN) 业务 推 广 中，不 再 强 调 人 工 智 能 。 如果说英伟达以著名计算机奇才 、 海 军 少 将 格 蕾 丝 · 霍 珀 (Grace Hopper) 的名字命名的芯片组是一艘豪华超级游艇，那么本周发布的 ARC-Compact 则是一艘经 济型游艇，房间更少，船帆更小。 ARC 服务器（搭载 Grace Hopper 及其继任者 Grace Blackwell）于去年发布，其目标客户是任 何构建集中式 RAN (C-RAN) 的电信运营商，该服务器可以通过附近的数据中心支持众多蜂窝基 站。ARC-Compact 的设计则针对的是安装在蜂窝基站上，即 Nvidia 和其他众多电信利益相关者 所称的分布式 RAN（或 D-RAN）。这彻底改变了经济性和可能的技术需求。 因此，ARC-Compact 的主要组件是 Grace 中央处理器 ( ...

从"能动"到"灵动"，机器人智能化步入新篇章 20250511 摘要 • 人形机器人商业化仍处于早期阶段，主要应用于工业场景中的标准化流程， 如汽车制造中的搬运工作，但实际可用场景有限。未来，流程标准化且人 力成本高的危险环境将是其率先落地的领域。 • 人形机器人商业化面临硬件和软件双重挑战。硬件方面，执行器、传感器 精度、功率密度及续航能力需提升；软件方面，人机交互效率、多模态感 知准确性、视觉图像处理及运动控制稳定性有待提高。 • 解决训练数据集匮乏问题的主要方案包括增加真实数据采集（如志远公司 搭建模拟生活空间）和采用物理仿真方法（如英伟达提供的方法），旨在 提高训练数据质量，加速商业应用拓展。 • 通过调整场景参数或对场景进行变化，可以基于少量真实世界交互数据衍 生出大量训练数据，提高数据获取效率并降低成本。未来主流方案可能是 结合真实数据采集与仿真世界数据衍生。 • 机器人基座大模型呈现多系统架构趋势，如 NVIDIA 的 Grace Hopper 等。 未来需解决多模态和泛化能力问题，即理解视觉、语言、语音及触觉信息， 并提高动作学习的举一反三能力。 Q&A 当前机器人行业的技术发展现状如何？ 过 ...

Financial Data and Key Metrics Changes - Q4 revenue reached $39.3 billion, up 12% sequentially and 78% year on year, exceeding the outlook of $37.5 billion [7][8] - Fiscal 2025 revenue totaled $130.5 billion, an increase of 114% compared to the previous year [8] - GAAP gross margins were 73%, with non-GAAP gross margins at 73.5%, down sequentially as expected due to the initial deliveries of the Blackwell architecture [37] Business Line Data and Key Metrics Changes - Data center revenue for fiscal 2025 was $115.2 billion, more than doubling from the prior year, with Q4 data center revenue at a record $35.6 billion, up 16% sequentially and 93% year on year [8][9] - Consumer Internet revenue grew 3x year on year, driven by generative AI and deep learning use cases [19] - Automotive revenue reached a record $570 million, up 27% sequentially and 103% year on year, with full-year revenue increasing by 55% [34] Market Data and Key Metrics Changes - Sequential growth in data center revenue was strongest in the US, driven by the initial ramp of Blackwell [26] - Data center sales in China remained well below previous levels due to export controls, with expectations to maintain current percentages [27] Company Strategy and Development Direction - The company is focused on expediting the manufacturing of Blackwell systems to meet high customer demand, with expectations for gross margins to improve to the mid-seventies later in the year [39][65] - Blackwell architecture is designed to support the entire AI market, from pretraining to inference, ensuring adaptability in rapidly evolving markets [16][36] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in sustained strong demand for AI computing, driven by the transition to machine learning and AI-based software [67][70] - The company anticipates significant growth in enterprise AI applications, particularly in industrial sectors, which are expected to become a larger part of the consumption mix [110][116] Other Important Information - The company returned $8.1 billion to shareholders in Q4 through share repurchases and cash dividends [39] - Upcoming events include participation in the TD Cowen Healthcare Conference and the Morgan Stanley Technology, Media, and Telecom Conference [43] Q&A Session Summary Question: Future of inference-dedicated clusters - Management discussed the increasing blurring between training and inference, highlighting the need for architectures that can handle both efficiently [46][54] Question: Status of Blackwell ramp and NVLink 72 - Management confirmed successful ramping of Blackwell systems and expressed enthusiasm for the NVLink 72 platform, noting significant demand [57][60] Question: Confidence in sustaining strong demand - Management provided insights into capital investments in data centers and the ongoing vibrancy of AI start-ups, indicating a positive outlook for demand [67][70] Question: Dynamics of Blackwell Ultra launch - Management confirmed that Blackwell Ultra is on track for a second-half launch, with a smooth transition planned from the current generation [75][78] Question: Balance between custom ASICs and merchant GPUs - Management emphasized the general-purpose nature of their architecture compared to ASICs, highlighting the advantages in performance and software ecosystem [82][84] Question: Geographic demand dynamics - Management noted that while US demand surged, China remains a significant market, albeit at reduced levels due to export controls [94][96] Question: Growth of enterprise consumption - Management indicated that enterprise consumption is expected to grow significantly, driven by the need for AI in various industrial applications [110][116]