The two best models in the world, Anthropic’s Claude 4.5 Opus and Google’s Gemini 3 have the majority of their training and inference infrastructure on Google’s TPUs and Amazon’s Trainium. Now Google is selling TPUs physically to multiple firms. Is this the end of Nvidia’s dominance? The dawn of the AI era is here, and it is crucial to understand that the cost structure of AI-driven software deviates considerably from traditional software. Chip microarchitecture and system architecture play a vital role in the development and scalability of these innovative new forms of software. The hardware infrastructure on which AI software runs has a notably larger impact on Capex and Opex, and subsequently the gross margins, in contrast to earlier generations of software, where developer costs were relatively larger. Consequently, it is even more crucial to devote considerable attention to optimizing your AI infrastructure to be able to deploy AI software. Firms that have an advantage in infrastructure will also have an advantage in the ability to deploy and scale applications with AI.

Full analysis : An in-depth look at TPUv7 Ironwood, and how the latest Google TPU generation positions Google as the most threatening challenger to Nvidia’s AI chip dominance.