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Together AI发布Mamba-3,优先考虑推理速度而非训练速度

Together AI发布了Mamba-3,这是一种新的状态空间模型(SSM),它优先考虑推理效率而非训练速度。该模型具有更具表现力的递归公式、复值状态跟踪以及增强准确性而不牺牲解码速度的多输入多输出(MIMO)变体。在1.5B参数规模下,Mamba-3 SISO在预填充和解码延迟方面表现优于之前的Mamba版本,甚至优于Llama-3.2-1B Transformer模型。该团队还开源了该模型的内核,这些内核是与卡内基梅隆大学、普林斯顿大学和Cartesia AI的研究人员合作开发的。 AI

影响 为状态空间模型的推理效率树立了新的基准,可能影响未来LLM的架构和部署策略。

排序理由 前沿AI实验室(Together AI)发布新模型,并声称其性能。 [lever_c_demoted from frontier_release: ic=1 ai=1.0]

在 Together AI blog 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发布Mamba-3,优先考虑推理速度而非训练速度

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0 / 100
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Significant
前沿AI实验室(Together AI)发布新模型,并声称其性能。 [lever_c_demoted from frontier_release: ic=1 ai=1.0]
Source corroboration
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
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Story freshness
205 days old
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完整方法见我们的编辑标准。

报道来源 [1]

  1. Together AI blog TIER_1 (SW) ·

    Mamba-3

    Meet Mamba-3: the SSM built for inference. Faster than Transformers at decode, stronger than Mamba-2, and open-source from day one.