Google+Meta join forces to break through! TPU compatible with PyTorch, Nvidia CUDA moat urgently needed!

The monopoly pattern of the global AI computing power market is facing disruptive challenges. Recently, Google launched a campaign codenamed "TorchTPU" with the core goal of enabling its self-developed TPU chips to achieve smooth support for the global mainstream AI framework PyTorch, with Meta, the controller of PyTorch, also deeply involved. The collaboration between two tech giants directly targets Nvidia's strongest barrier - the CUDA ecosystem, marking the beginning of a commercial siege aimed at restructuring the AI computing market landscape.

For a long time, NVIDIA has built an unshakable industry hegemony through the CUDA ecosystem. As a parallel computing platform, CUDA has been deeply involved in the industry for over 20 years, forming a complete closed loop of "chip+toolchain+application". Over 4 million developers worldwide rely on this platform for research and development, and 15000 optimized applications cover the entire field. More importantly, CUDA has formed a deep binding with PyTorch, becoming the default choice for AI model training and inference. The cost of enterprise migration can reach millions of dollars, which also allows Nvidia to occupy over 80% of the global data center GPU market. In contrast, although Google TPU has advantages in AI specific computing performance and energy efficiency, it has fallen into the dilemma of "excellent hardware but difficult to popularize" due to long-term dependence on its own Jax framework and disconnection from the usage habits of mainstream developers.

This collaboration between Google and Meta accurately addresses the pain points. The "TorchTPU" project is not a sporadic technological adaptation, but a major layout of Google's investment in war resources. The core is to eliminate the software threshold for developers to migrate to TPU, allowing users who are accustomed to PyTorch to achieve "painless migration", and even open sourcing some software to accelerate the journey. For Meta, promoting software adaptation to TPU has significant value. It can not only reduce the inference cost of its own AI model, but also reduce its dependence on Nvidia through hardware diversification, and gain more initiative in supply chain negotiations. The alignment of interests between the two sides has given this alliance strong impetus to advance.

The impact of this collaboration on Nvidia is self-evident. The industry generally believes that the CUDA ecosystem is Nvidia's true moat, rather than just a hardware performance advantage. Previously, although AMD and other manufacturers have been constantly catching up in hardware parameters, they have always been unable to break through the barriers of the software ecosystem. The collaboration between Google and Meta is equivalent to directly dismantling Nvidia's advantages from the ecological foundation - once TPU can smoothly run PyTorch, the conversion cost for enterprises to choose alternative solutions will be significantly reduced, and Nvidia's market share is likely to be diverted.

At present, Google has begun to adjust its strategic layout, not only selling TPU directly to customer data centers, but also adjusting its organizational structure, with veterans reporting directly to the CEO to be responsible for AI infrastructure, demonstrating its determination to seize the computing power market. Morgan Stanley estimates that if Google sells 1 million TPU tablets to the public in 2027, it can increase cloud revenue by $26 billion. Although it cannot shake Nvidia in the short term, it is enough to drive the market from monopoly to multipolarity. For the global AI industry, this ecological competition may break the monopoly of one dominant company, bringing more comprehensive competition and more cost-effective computing options, but for Nvidia, unprecedented challenges have arrived.