炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 早在2025年3月的GTC大会上，黄仁勋就已预告了代号"Vera Rubin"的超级芯片，并明确其将于2026年量 产。 当地时间1月5日，在美国CES上，黄仁勋出乎意料地提前发布了下一代AI芯片平台"Rubin"，打破了英 伟达通常在每年3月GTC大会上集中公布新一代架构的传统。 AI竞赛进入推理时代，英伟达决定加速出击。 Vera Rubin已投产 此次在CES上，黄仁勋对Rubin平台进行了系统性发布，Rubin成为英伟达最新GPU的代号。 "Rubin的到来正逢其时。无论是训练还是推理，AI对计算的需求都在急剧攀升。"黄仁勋表示，"我们坚 持每年推出新一代AI超级计算机，通过六颗全新芯片的极致协同设计，Rubin正在向AI的下一个前沿迈 出巨大一步。" Rubin平台采用极端协同设计理念，整合了6颗芯片，包括NVIDIA Vera CPU、Rubin GPU、NVLink 6交 换芯片、ConnectX-9 SuperNIC、BlueField-4 DPU以及Spectrum-6以太网交换芯片，覆盖了从计算、网络 到存储与安全的 ...

Core Viewpoint - NVIDIA has accelerated its AI chip platform release with the introduction of the "Rubin" platform at CES 2026, marking a shift in its product announcement strategy and emphasizing the growing demand for AI computing in both training and inference [2][4]. Group 1: Rubin Platform Overview - The Rubin platform, which integrates six chips including the NVIDIA Vera CPU and Rubin GPU, is designed for extreme collaborative performance, enhancing AI training performance by 3.5 times and operational performance by 5 times compared to the previous Blackwell architecture [4]. - The Rubin platform's inference token cost can be reduced by up to 10 times, and the number of GPUs required for training MoE models is decreased by four times [5]. - The NVL72 system, which includes 72 GPU packaging units, was also announced, with each unit containing two Rubin dies, totaling 144 Rubin dies in the system [5]. Group 2: Ecosystem and Market Response - Major cloud providers and model companies such as AWS, Microsoft, Google, OpenAI, and Meta have shown strong interest in the Rubin platform, indicating a positive market response [6]. - NVIDIA's early announcement of Rubin aims to provide engineering samples to ecosystem partners for preparation of subsequent deployment and scaling applications [6]. Group 3: Full-Stack AI Strategy - NVIDIA's presentation at CES included a range of AI products, signaling a shift from training scale to inference systems, with the introduction of the Inference Context Memory Storage Platform designed for efficient management of KV Cache [8]. - The company is expanding its focus on physical AI, releasing open-source models and frameworks that extend AI capabilities into robotics, autonomous driving, and industrial edge scenarios [8][9]. - The Cosmos and GR00T series models were introduced for robotics, enabling machines to understand and act in the physical world, while the Alpamayo model family was launched for autonomous driving, supported by a high-fidelity simulation framework [11].

Core Insights - NVIDIA has accelerated its AI chip platform release schedule by unveiling the next-generation AI chip platform "Rubin" earlier than usual at CES on January 5, 2026, breaking its traditional March GTC announcement pattern [1][2] Group 1: Rubin Platform Overview - The Rubin platform, which includes six new chips, is designed for extreme collaboration and aims to meet the increasing computational demands of AI for both training and inference [4] - Compared to the previous Blackwell architecture, Rubin accelerators improve AI training performance by 3.5 times and operational performance by 5 times, featuring a new CPU with 88 cores [4] - Rubin can reduce inference token costs by up to 90% and decrease the number of GPUs required for training mixture of experts (MoE) models by 75% compared to the Blackwell platform [4] Group 2: Ecosystem and Market Response - The NVL72 system, which includes 72 GPU packaging units, was also announced, with each unit containing two Rubin dies, totaling 144 Rubin dies in the system [5] - Major cloud providers and model companies, including AWS, Microsoft, Google, OpenAI, and Meta, have responded positively to Rubin, indicating strong market interest [5] - NVIDIA aims to provide engineering samples to ecosystem partners early to prepare for subsequent deployment and scaling applications [5] Group 3: AI Strategy and Product Launches - NVIDIA's focus is shifting from "training scale" to "inference systems," as demonstrated by the introduction of the Inference Context Memory Storage Platform, designed specifically for inference scenarios [6] - The company is also advancing its long-term strategy in physical AI, releasing open-source models and frameworks that extend AI capabilities to robotics, autonomous driving, and industrial edge scenarios [6] - The launch of the Cosmos and GR00T series models aims to enhance robotic learning, reasoning, and action planning, marking a significant step in the evolution of physical AI [7] Group 4: Autonomous Driving Developments - NVIDIA introduced the Alpamayo open-source model family for autonomous driving, targeting "long-tail scenarios," along with a high-fidelity simulation framework and an open dataset for training [9] - The first autonomous vehicle from NVIDIA is set to launch in the U.S. in the first quarter, with plans for expansion to other regions [9] - The overall strategy emphasizes that the competition in AI infrastructure is moving towards "system engineering capabilities," where the complete delivery from architecture to ecosystem is crucial [9]