TPUv7 Ironwood公布首批AgentX长上下文成绩
SemiAnalysis公布了Google TPUv7 Ironwood在Muse Spark Open、GLM-5.2、Qwen3.5和Kimi K3上的首批长上下文、多轮AgentX正式成绩。报道称，Ironwood在这些提交中具备竞争力或取得最佳每美元性能，其中GLM-5.2的FP8测试达到NVIDIA B200、B300和AMD MI355X每美元token数的1.2至2.7倍。不同精度和任务的结果不一，NVIDIA的FP4方案在部分比较中领先，而Rubin数据仍属预览提交，SemiAnalysis尚未独立复现。
这批结果为采购方评估TPUv7在真实智能体推理中的成本效率提供了新依据，并显示Google的TPU业务正同时扩展硬件外部客户和开源服务生态。
In November 2025, we published a newsletter saying that Google is selling TPUs to external customers. Since then, we have been proven right over and over again with Oracle, OpenAI, Anthropic, and Meta all becoming customers of TPUs just as our Accelerator Model reported.
Hardware demand is only half the story, as NVIDIA’s moat has historically been its software and large developer ecosystem. This is where TPU is also making headway, partnering with upstream maintainers across the entire ecosystem, including the Meta PyTorch team, vLLM (Inferact, RedHat), SGLang (RadixArk), llm-d, Ray, etc. This is not a surprise to folks that have read our September 2026 article laying out Google TPU’s externalization strategy.
Back in our September article, we published the first third-party TPUv7 results on InferenceX on single-turn workloads, showing that in an apples-to-apples FP8 comparison on Qwen 3.5 397B, TPUv7 Ironwood) delivered up to 50% better performance per dollar than B200 and B300.
We also reported that many Google TPU customers were asking for AgentX results, since long-context, multi-turn agentic serving is where inference spend is heading.
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