AI竞赛转向推理，英伟达宣布Rubin芯片平台全面投产

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]