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TopicDisconfirmAI EditorialUpdated 2026-11-06 06:15

TPUv7推理性能

5 steps1 pieces5 Sources

【背景】2026年11月5日,SemiAnalysis发布报告指出,谷歌第七代张量处理单元(TPUv7 Ironwood)在长上下文、多轮智能体推理基准(AgentX)上展现出显著的成本效益优势。在FP8精度下,TPUv7运行GLM-5.2模型时每美元token产出达NVIDIA B200/B300及AMD MI355X的1.2至2.7倍;在Qwen3.5上,其每美元性能为GB300 FP4模式的1.67倍。

然而,争议在于NVIDIA凭借原生FP4支持在GLM-5.2上仍保持3.5至3.6倍的领先优势,而TPUv7尚无原生FP4支持。此外,部分结果由Google、RadixArk和Inferact分别提交,未经独立复现。尽管TPUv7在特定场景下具备竞争力,但NVIDIA的软件生态壁垒(CUDA Moat)及其在高端FP4推理上的绝对性能仍是主要阻碍。随着Meta等外部客户开始在生产环境中使用TPU,市场格局正在发生微妙变化。

How it got here

  1. 2025-11-28

    SemiAnalysis首次公开报道谷歌向外部客户销售TPU,并预测Oracle、OpenAI、Anthropic和Meta将成为客户。

    newsletter.semianalysis.com ↗
  2. 2026-09-07

    SemiAnalysis发布TPUv7在InferenceX单轮工作负载上的首批第三方结果,显示在Qwen3.5 FP8模式下,TPUv7每美元性能比B200/B300高50%。

    newsletter.semianalysis.com ↗
  3. 2026-10-21

    Tom's Hardware报道Meta确认在其数据中心生产环境中运行谷歌TPU推理,通过新的PyTorch原生SGLang引擎实现,无需JAX移植。

    tomshardware.com ↗
  4. 2026-10-29

    谷歌与Meta联合发布TorchTPU论文,介绍一种混合Eager/Compiled架构,旨在实现PyTorch在TPU上的原生执行。

    arxiv.org ↗
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2026-11-05

SemiAnalysis发布TPUv7在AgentX智能体推理基准上的首批跨模型结果,涵盖GLM-5.2、Qwen3.5、Kimi K3和Muse Spark Open。

newsletter.semianalysis.com ↗

What is still unsettled

  • TPUv7缺乏原生FP4支持是否会在未来版本(如TPUv8i)中得到解决,从而缩小与NVIDIA GB300 NVL72的性能差距?
  • 未经独立复现的第三方基准测试结果(由Google、RadixArk、Inferact提交)能否被更广泛的行业接受为标准参考?
  • NVIDIA声称其内部团队正在加班以优化GB300 FP4性能,这一努力能否在短期内逆转TPUv7在每美元成本上的优势?

Sources

  • newsletter.semianalysis.comSemiAnalysis: The Iron(wood) Throne↗
  • tomshardware.comTom's Hardware: Google’s TPUs now run in Meta's data centers↗
  • newsletter.semianalysis.comSemiAnalysis: TPU Inference Externalization Full Steam Ahead↗
  • arxiv.orgArXiv: TorchTPU – A Hybrid Eager/Compiled Architecture↗
  • inferencex.semianalysis.comInferenceX: TPU v7 vs GB300 NVL72 AgentX Results↗

Coverage in this topic

  • SemiAnalysis: TPUv7 Delivers 1.2 to 2.7 Times More Tokens Per Dollar Than NVIDIA/AMD on GLM-5.2 AgentX2026-11-06News

    SemiAnalysis released the first cross-model results for TPUv7 on the AgentX agent inference benchmark on November 5, 2026. At FP8 precision, TPUv7 on GLM-5.2 delivers 1.2 to 2.7 times more tokens per dollar than the B200, B300, and MI355X. On Qwen3.5, TPUv7 FP8 performance per dollar is 1.67 times that of the GB300 FP4. However, NVIDIA FP4 still leads on GLM-5.2 by a factor of 3.5 to 3.6, and TPUv7 currently lacks native FP4 support. Results were submitted separately by Google (Qwen3.5), RadixArk (Muse Spark Open), and Inferact (Kimi K3) and have not been independently reproduced.