Hua Er Jie Jian Wen·2025-12-03 07:21Core Insights - Google is attempting to sell its self-developed AI chip, TPU (Tensor Processing Unit), to a broader market, posing a significant challenge to Nvidia, the current leader in AI chips [1] - The advanced AI models from Google and Anthropic utilize Google's TPU chips, which has prompted major clients like Meta to consider using TPUs for new model development [1] - Morgan Stanley predicts that Google plans to produce over 3 million TPUs by 2026 and around 5 million by 2027, while Nvidia's current GPU production is approximately three times that of Google's TPUs [1][7] Performance Comparison - Although a single TPU chip is less powerful than Nvidia's strongest GPU, Google's strategy leverages large-scale clusters to enhance performance and cost-effectiveness [2][3] - Thousands of TPUs can be connected to form a "super pod," providing superior performance in training large models compared to Nvidia's GPU systems, which can connect a maximum of about 256 GPUs directly [3] Software Ecosystem - Nvidia's competitive advantage lies in its deeply integrated CUDA software ecosystem, making it more cost-effective for existing users to rent Nvidia chips [4] - TPU's compatibility challenges arise as it primarily works with specific AI software tools like TensorFlow, while most AI researchers prefer PyTorch, which performs better on GPUs [4] Cost Dynamics - The manufacturing costs of TPU and GPU are comparable, with TPU using advanced but more expensive manufacturing technology [5] - Nvidia's hardware business maintains a gross margin of 63%, while Google's cloud services have a margin of only 24%, explaining Nvidia's strong profitability in price competition [6] Capacity Competition - TSMC does not allocate all its production capacity to a single client, allowing space for alternatives like TPU in the market [7] - As Google ramps up TPU production, the gap between TPU and Nvidia's GPU production is narrowing, encouraging clients to explore multiple options [7] Commercialization Challenges - Google faces significant challenges in building a complete supply chain for TPU sales, including partnerships with server manufacturers and distribution networks [8] - Deploying TPUs in client data centers could lead to a loss of cloud service revenue for Google, indicating that TPUs may not follow a low-cost strategy but rather a complex strategic approach [8] - The broader significance of TPU for Google lies in its potential to negotiate with Nvidia and promote its Gemini AI ecosystem, enhancing Google's autonomy in AI infrastructure [8]