Google TPU vs NVIDIA GPU: Analysis of AI Chip Competition Landscape and Investment Opportunities

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According to Ginlix Analytical Database: NVIDIA holds about 80% share in the AI accelerator market; Google TPU v7 Ironwood has a peak performance of 4614 TFLOP/s, and its speed in neural network tasks is 2.5-4 times that of GPUs, with per-dollar performance 1.2-1.7 times better than GPUs.
According to Sina Finance: The AI accelerator market size is expected to reach $140.55 billion in 2025 and exceed $440.3 billion in 2030, with a CAGR of 25%; it is estimated that the total ASIC shipments of overseas AI giants may surpass NVIDIA GPUs in 2026.

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Reddit user: Google TPU+OCS architecture has advantages in computing power infrastructure, but still relies on NVIDIA GPUs (due to flexibility needs); Anthropic’s 1 million TPU agreement validates demand, and is optimistic about the computing power supply chain (optical module LITE, domestic Xuchuang/Shenghong) and NAND flash memory.
Xueqiu user: Gemini3.0’s text-to-image function refutes the AI bubble theory, but TPU is only applicable to Google’s closed technology stack and does not support CUDA; NVIDIA builds computing backbone networks for other companies; recommends US stocks LITE (optical chip) and NAND flash memory (SanDisk).

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Both research and social media recognize Google TPU’s technical and cost advantages, but consistently point out NVIDIA’s CUDA ecosystem’s moat effect; both believe that under the explosion of computing power demand, the supply chain (optical modules, NAND) and energy infrastructure are core investment opportunities, while low-bargaining-power software companies face negative impacts. Google’s closed technology stack limits the popularization of TPU, and NVIDIA still dominates the third-party market, forming a differentiated competition pattern.