PulseAugur
实时 19:19:34

MiniMax M3 发布,支持 1M 词元上下文和 MSA 架构

MiniMax 发布了其 M3 模型,该模型采用了新颖的稀疏注意力(MSA)架构,支持 100 万词元的上下文窗口和原生多模态。这种新架构显著降低了长上下文的计算成本,使得 M3 比前几代模型速度更快。该模型在编码和智能体任务方面也表现出色,在 SWE-Bench Pro 和 Terminal-Bench 等基准测试中超越了多个领先模型。 AI

影响 在编码基准测试中设定了新的 SOTA(State-of-the-Art),并提供了前所未有的上下文长度,可能改变模型效率和能力行业的标准。

排序理由 前沿实验室模型发布,附带系统卡和新架构。

在 Together AI blog 阅读 →

AI 生成摘要 · Google Gemini · 来自 8 个来源。 我们如何撰写摘要 →

MiniMax M3 发布,支持 1M 词元上下文和 MSA 架构

报道来源 [8]

  1. X — Fireworks (inference infra) TIER_1 English(EN) · FireworksAI_HQ ·

    MiniMax M3 搭载 MiniMax Sparse Attention (MSA) 登场,1M 令牌下解码速度提升 15.6 倍。我们正与 @MiniMax_AI 合作,为其提供推理支持

    MiniMax M3 arrives with MiniMax Sparse Attention (MSA), 15.6x faster decoding at 1M tokens. We're partnering with @MiniMax_AI to power the inference behind this week's launch. Head to https://t.co/zwLs8Pj7I6 to take it for a spin. Once the model weights are released, M3 will be …

  2. X — Together (inference / OSS) TIER_1 (AF) · togethercompute ·

    发言人:

    Speakers: Pengyu Zhao, Head of Research at MiniMax Haohai Sun, Research Scientist at MiniMax Ce Zhang, Founder/CTO at Together Yineng Zhang, Senior Director at Together Dan Fu, VP of Kernels at Together Hosted by Zain Hasan, Staff AI Engineer at Together

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

    我们将深入探讨 @MiniMax_AI M3 的模型性能、MSA 架构及其对长上下文的意义,以及 Together 如何优化推理和 KV 缓存

    We'll get into @MiniMax_AI M3's model performance, the MSA architecture and what it means for long context, and how Together is optimizing inference and KV-cache for this new architecture. Set your reminders!

  4. X — Together (inference / OSS) TIER_1 (AF) · togethercompute ·

    发言人:

    Speakers: Pengyu Zhao, Head of Research at MiniMax Haohai Sun, Research Scientist at MiniMax Ce Zhang, Founder/CTO at Together Dan Fu, VP of Kernels at Together Hosted by Zain Hasan, Staff AI Engineer at Together

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

    我们将深入探讨 @MiniMax_AI M3 的模型性能、MSA 架构及其对长上下文的意义,以及 Together 如何优化推理和 KV 缓存

    We'll get into @MiniMax_AI M3's model performance, the MSA architecture and what it means for long context, and how Together is optimizing inference and KV-cache for this new architecture. Set your reminders.

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

    MiniMax M3 上线,由 Together AI 提供推理支持 🚀

    MiniMax M3 is live and Together AI is powering its inference 🚀 Tomorrow at 6pm PT we're going live on X Spaces with the teams behind the model and the infrastructure to give you a deep dive. https://t.co/wPayfOWmNg

  7. Together AI blog TIER_1 English(EN) ·

    为高效推理提供 MiniMax-M3 服务:解锁百万级 Token 上下文与多模态能力,无后顾之忧

    How Together served MiniMax-M3 efficiently with KV-block-major sparse attention, paged MSA decode, optimized index scoring, and a Rust-based multimodal gateway.

  8. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    MiniMax发布MiniMax M3,采用MSA架构,支持100万token上下文、原生多模态和智能体编码

    <p>MiniMax M3 introduces MiniMax Sparse Attention, a 1M-token context window, and native image, video, and computer use support.</p> <p>The post <a href="https://www.marktechpost.com/2026/06/01/minimax-releases-minimax-m3-with-msa-architecture-supporting-1m-token-context-native-m…