Nvidia(US:NVDA) 第一财经·2026-01-06 03:17Core Viewpoint - The article highlights NVIDIA's advancements in AI and computing architecture, emphasizing the dual transformation occurring in AI and computing, which is reshaping the entire technology stack and creating new applications and ecosystems [6][7]. Group 1: AI and Computing Transformation - Huang emphasized that the computing industry undergoes a platform change every 10 to 15 years, with the current shift driven by AI and computing architecture simultaneously evolving [6]. - AI is both an application and a new platform, leading to a paradigm shift in software development from coding to model training [6][7]. - The modernization of a $10 trillion computing infrastructure is underway, with billions in venture capital flowing into AI, as industries shift R&D budgets towards AI [7]. Group 2: Open Source Models - Huang noted that one of the significant changes in the industry last year was the rise of open-source models, specifically mentioning China's DeepSeek R1 as a remarkable contributor to this global movement [7][8]. - Multiple open-source models were showcased, including three from China: Kimi K2, Qwen, and DeepseekV3.2 [8]. Group 3: Physical AI and Autonomous Driving - Huang stated that the next phase of AI development involves entering the physical world, requiring AI to learn common sense about physical properties [10]. - NVIDIA is working on a system that allows AI to learn about the physical world, which is crucial for applications like autonomous driving [10][12]. - Huang believes that the transition from non-autonomous to autonomous vehicles is imminent, with a significant portion of cars expected to be autonomous in the next decade [14]. Group 4: New Chip Platform - Rubin - The Rubin platform includes six new chips, with the Rubin GPU achieving a reasoning power of 50 PFLOPS, five times that of the previous Blackwell platform [21]. - The Rubin platform's design allows for a tenfold reduction in reasoning token costs and a fourfold decrease in the number of GPUs needed for training [21][22]. - The new Vera Rubin NVL72 chip is expected to significantly enhance performance, with reasoning and training capabilities reaching 3.6 EFLOPS and 2.5 EFLOPS, respectively [24]. Group 5: Collaborations and Future Developments - NVIDIA announced a deepened collaboration with Siemens to integrate its physical AI models into Siemens' industrial software, covering the entire lifecycle from chip design to production [16]. - The first autonomous vehicles using NVIDIA's DRIVE AV software are set to hit the roads in the U.S. in the first quarter of this year, with further expansions planned for Europe and Asia [16].