Google has re-emerged as a major force in the AI hardware race, driven not by its consumer services but by rapid advances in its Tensor Processing Units (TPUs). With the seventh-generation Ironwood (TPUv7) architecture gaining industry attention, major players like Meta and Anthropic are now exploring large-scale TPU adoption—signalling a potential shift in global AI infrastructure.
Originally developed in 2013 to support Google’s large-scale machine learning workloads, TPUs evolved as specialised chips optimised for deep learning and tensor computations. While they were initially restricted to Google’s internal systems, the rise of generative AI has pushed Google to reposition TPUs as high-performance, cost-efficient alternatives to GPUs, particularly for inference at massive scale.
This strategic shift is reshaping market dynamics. Anthropic has already begun integrating TPUs into its multichip ecosystem, and Meta is reportedly negotiating a multi-billion-dollar deal to begin large-scale TPU deployment from 2026, marking its first major move away from GPU-exclusive infrastructure. News of this potential shift contributed to a temporary drop in Nvidia’s stock, reflecting concerns over its dependence on hyperscalers for data centre revenue.
Despite Google’s momentum, Nvidia continues to lead with CUDA—its widely adopted software ecosystem—and highly integrated GPU systems that support diverse AI workloads. Still, Google’s Ironwood TPUs now rival Nvidia’s Blackwell GPUs in raw compute and memory performance, signalling intensifying competition between general-purpose GPUs and specialised AI accelerators.
As global demand for AI continues to surge, analysts expect the coming years to feature a closely contested race between Nvidia’s powerful GPU platforms and Google’s rapidly advancing TPU silicon.
