2 1 Shi Ji Jing Ji Bao Dao·2025-11-26 13:44Core Viewpoint - The AI chip market is experiencing significant shifts, with Google accelerating the commercialization of its self-developed AI chip, TPU, which may disrupt the dominance of NVIDIA's GPUs in the computing power market [2][4]. Group 1: Google's TPU Development - Google has been developing TPU since 2013, primarily for internal AI workloads and Google Cloud services, but is now pushing for external commercialization, with potential contracts worth billions [6]. - Meta is considering deploying Google's TPU in its data centers starting in 2027, with the possibility of renting TPU capacity through Google Cloud as early as next year [6]. - Google's strategy aligns with its long-term goal of integrating hardware and software, aiming to reduce energy consumption and control costs amid rising training costs for large models [6]. Group 2: NVIDIA's Market Position - NVIDIA, holding over 90% of the AI chip market, responded to Google's competition by emphasizing its industry leadership and the unique capabilities of its GPUs [4][7]. - Despite the potential entry of TPU into major data centers, NVIDIA maintains that GPUs will not be replaced in the short term, as both TPU and NVIDIA GPUs are experiencing growing demand [4][7]. - NVIDIA's CEO highlighted the complexity of accelerated computing, suggesting that while many companies are developing AI ASICs, few have successfully brought products to market [10]. Group 3: Industry Trends - The trend of major tech companies developing their own AI chips is growing, with AWS and Microsoft also iterating on their self-developed chips, indicating a shift towards a heterogeneous architecture in the industry [9]. - Companies are increasingly adopting a multi-vendor strategy for AI training and inference, as seen in Anthropic's partnerships with both NVIDIA and Google [9]. - The AI infrastructure industry is evolving from a single hardware competition to a system-level competition, influenced by changes in software frameworks, model systems, and energy efficiency [10].