从预训练到推理拐点，英伟达能靠Rubin延续霸权吗？

Core Viewpoint - The article discusses NVIDIA's strategic shift towards a multi-chip architecture with the introduction of the "Rubin" platform, which aims to address the challenges in AI inference and maintain its market leadership amidst increasing competition and technological limitations [2][4][6]. Group 1: NVIDIA's Strategic Shift - NVIDIA's CEO Jensen Huang emphasized the importance of "physical AI" and positioned inference AI at the core of its future strategy, introducing the autonomous driving AI Alpamayo and the Vera Rubin computing platform [2]. - The Rubin platform integrates multiple components, including Vera CPU, Rubin GPU, and various networking technologies, to enhance computational power and address the exponential growth in model size and inference complexity [2][4]. - Industry insiders view the launch of the Rubin platform as a critical step for NVIDIA to maintain its leading position in the inference market, especially as single-chip performance gains have plateaued [4][6]. Group 2: Technical Challenges and Innovations - The Rubin platform's inference performance relies on NVFP4 adaptive precision, which may compromise higher precision calculations, potentially affecting quality in sensitive applications like video generation [5][19]. - Huang claimed that the Rubin platform could reduce global data center power consumption by approximately 6% through its innovative cooling design, although experts raised concerns about the actual effectiveness of this approach [5][24]. - The platform's power consumption is reportedly double that of its predecessor, raising questions about its scalability and the need for enhanced cooling solutions to manage heat effectively [21][23]. Group 3: Market Implications and Competitive Landscape - The introduction of the Rubin platform may initially negatively impact domestic chip manufacturers, but it could ultimately benefit them as the industry shifts towards multi-chip systems [6][12]. - The article highlights a growing consensus that the core value in training is efficiency, while in inference, it is cost, indicating a shift in market dynamics that NVIDIA must navigate [7]. - The competition in the inference space is intensifying, with domestic firms also pursuing similar technological advancements, suggesting that NVIDIA's current strategies may face significant challenges [18][19].