#Google TPU

SemiAnalysis published what it describes as the first long-context, multi-turn agentic inference results for Google&\x27;s TPUv7 \(Ironwood\) on the InferenceX/AgentX benchmark, covering four models served through vLLM and SGLang by ecosystem contributors including Inferact and RadixArk. On GLM-5.2 \(FP8\), the authors report TPUv7 as the cheapest FP8 option at 1.2x to 2.7x the tokens per dollar of B200, B300 and MI355X between 60 and 300 tok/s/user, while noting NVIDIA NVFP4 still wins with an acknowledged accuracy/quality loss; Kimi K3 \(MXFP4\) is reported at 1.15x to 2.5x the tokens per dollar of GB200, B200, B300 and MI355X. Qwen3.5 \(FP8\) with llm-d disaggregated serving is said to beat GB300 FP8 at every interactivity level, whereas Muse Spark Open \(NVFP4 weights\) is only described as competitive. These are SemiAnalysis-authored, partly community-submitted measurements rather than independent audits, and the piece cites Google&\x27;s partnerships with Meta&\x27;s PyTorch team, vLLM, SGLang, llm-d and Ray as evidence it is contesting NVIDIA&\x27;s software moat.