概念
别名：MSA、MiniMax 稀疏注意力
MiniMax M3的核心架构创新，通过块稀疏注意力机制降低长上下文处理成本。
已跟踪 2 条高相关材料
最近变化
2026-06-02 · MiniMax M3 supports 1M-token context and native multimodality, making it suitable for complex real-world tasks.
为什么值得关注
MiniMax Sparse Attention (MSA) 被反复提及时，通常意味着它正在影响产品路线、开发者工作流或 AI 产业判断。这个页面把分散材料合并成一个可持续更新的观察入口。
Serving MiniMax-M3 for efficient inference: Unlocking 1M-Token Context and Multimodality Without Regrets
Together AI Blog · 8.7 分
Together AI has optimized the deployment of MiniMax M3, a model with 1M-token context and multimodal support, achieving 81–125% throughput...
MiniMax M3 on AI Gateway
Vercel News · 6.5 分
MiniMax M3 模型现已接入 Vercel AI Gateway，支持100万令牌上下文窗口与原生多模态能力，基于MSA稀疏注意力架构，提升工程效率与工具调用能力。
已收录 2 条与 MiniMax Sparse Attention (MSA) 相关的内容，按评分排序。
Together AI optimized the deployment of MiniMax M3, achieving 81–125% throughput improvements through architectural and engineering innovations.
入选理由：MiniMax M3 supports 1M-token context and native multimodality, making it suitable for complex real-world tasks.
MiniMax M3 is now available on Vercel AI Gateway, offering a 1M-token context window and native multimodal support via MSA architecture, enhancing engineering efficiency and tool integration.
入选理由：M3模型支持1M令牌上下文窗口，显著提升长文本处理能力。
