Performance per Whose Dollar?
TPUs, agentic inference, and the end of the one-chip cloud Copyright: Sanjay Basu On November 5, 2026, SemiAnalysis published benchmark results that would have sounded strange two years ago. Google’s TPUv7, called Ironwood, matched or beat NVIDIA’s best on cost for several frontier open models. The test was not a simple chatbot prompt. It was long, multi-turn, agentic work. That is the kind of serving where inference spend is heading. For a decade, the case for staying on NVIDIA was never only about the silicon. It was about the software. CUDA, the kernels, and thousands of maintainers who tuned for NVIDIA first. That moat is now being contested. And it is being contested by the same open source people who helped build it. What changed in three weeks On October 20, at PyTorch Conference 2026, Google open-sourced TorchTPU under the Apache 2.0 license. It was built with Meta’s PyTorch team. It runs PyTorch natively on TPUs. You debug in eager mode. You compile with torch.compile fo...