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. What the benchmark actually measured AgentX tests inference for agentic workloads. Inference is the stage where a trained model answers questions or carries out tasks, and agentic workloads involve AI systems working through multi-step jobs instead of single replies. Those jobs tend to involve long context and multi-turn exchanges, meaning the model has to remember a lot and talk back and forth repeatedly. SemiAnalysis’s research notes that TPU architecture now support...