Google TPU's Rise vs Nvidia's Dominance: Can Google Disrupt NVDA's AI Chip Leadership?

According to research, Google’s 7th-gen TPU Ironwood (3nm process) delivers 4614 TFLOPS of performance, with training costs only 1/5 of Nvidia’s solutions. However, Nvidia holds over 80% of the AI server market share and 90% of the AI chip market share, primarily due to its CUDA ecosystem (including cuDNN and TensorRT) that creates high switching costs for customers. Nvidia’s GB300 GPU offers 15P FLOPS FP4算力 but has higher power consumption compared to TPU. Google plans to achieve a 1000x performance boost in 4-5 years, with its next-gen TPU using a 2nm process and MediaTek as a partner, plus OCS optical switching technology to improve network efficiency by 30% and reduce power consumption by 40% [1][2][3].

Reddit users discuss Google’s TPU+OCS architecture advantages in specific workloads but note its reliance on Nvidia GPUs for flexibility. They highlight Anthropic’s 1 million TPU deal as validation of demand and recommend investments in supply chain players like Lumentum (LITE), Xuchuang (300308.SZ), and Shenghong (300476.SZ), as well as energy infrastructure [6]. Xueqiu users argue that while Google’s Gemini3 is strong, it does not颠覆性 Nvidia’s position due to TPU’s closed technical stack and lack of CUDA compatibility. They emphasize Nvidia’s unassailable moat from the CUDA ecosystem and suggest opportunities in NAND flash and energy supply solutions [7].

Google’s TPU is a formidable niche competitor, excelling in cost efficiency and specific AI workloads, but it is unlikely to displace Nvidia in the short term due to Nvidia’s deep CUDA ecosystem lock-in and high customer switching costs. Both companies’ investments in AI infrastructure will drive demand for supply chain components (e.g., optical modules, PCBs) and energy solutions (e.g., power supply, storage). Investors should consider balancing exposure to both Google’s supply chain and Nvidia’s ecosystem, while also looking at emerging opportunities in energy infrastructure to address power constraints in data centers.

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