Google TPUv7 posts strong agentic inference results against Nvidia GB300

SemiAnalysis data shows Ironwood beating Nvidia's flagship rack on performance per dollar while external customers keep signing up Google ’s TPUv7, the chip also known as Ironwood, has delivered early benchmark results that put it ahead of Nvidia ’s GB300 NVL72 on cost efficiency for agentic AI workloads, according to SemiAnalysis. On the AgentX benchmark, TPUv7 submissions posted performance per dollar between 1.42 and 1.67 times better than Nvidia’s rack-scale system. The timing matters because the AI industry’s spending focus is shifting. Training giant models grabbed the headlines for years. Running those models for millions of users, and increasingly for autonomous agents, is where the recurring bills now pile up. AgentX tests inference for agentic workloads.

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