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English(EN) Up to 2.7× more throughput at the same speed per user.

Together AI编译器在NVIDIA硬件上将推理吞吐量提高高达2.7倍

Together AI发布了基准测试,展示了其编译器在性能上的显著提升。与在NVIDIA B200和B300硬件上优化的InferenceX配置相比,Together Compiler在多种模型上实现了高达2.7倍的吞吐量提升。具体而言,Kimi K3的提升率为2.7倍,GLM-5.3和DeepSeek V4.1 Flash均实现了1.8倍的提升,而MiniMax M3.1 Flash的吞吐量提升了1.5倍。 AI

影响 展示了AI推理效率的显著提高,可能降低运营成本并加快部署速度。

排序理由 该条目详细介绍了在特定硬件上比较编译器性能的基准测试结果,这属于研究里程碑。 [lever_c_demoted from research: ic=1 ai=0.7]

在 X — Together (inference / OSS) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Together AI编译器在NVIDIA硬件上将推理吞吐量提高高达2.7倍

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该条目详细介绍了在特定硬件上比较编译器性能的基准测试结果,这属于研究里程碑。 [lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. X — Together (inference / OSS) TIER_1 English(EN) · togethercompute ·

    每位用户吞吐量提升高达2.7倍,速度不变。

    Up to 2.7× more throughput at the same speed per user. In our benchmarks comparing Together Compiler vs. the best InferenceX configs on NVIDIA B200 and B300: → Kimi K3: 2.7× → GLM-5.3: 1.8× → DeepSeek V4.1 Flash: 1.8× → MiniMax M3.1 Flash: 1.5× Read more: https://t.co/5QUdF70n…