MiniMax Unveils MSA: Dual-Branch Sparse Attention for MoE
Decision Brief
What changedMiniMax released MSA, a dual-branch block sparse attention mechanism that reduces compute costs.
Why it mattersMSA offers a new way to cut attention compute in large models, relevant for AI builders optimizing efficiency.
Who should careTeams building on model APIs
Affected stackNo specific stack identified
Builder actionEvaluate
Source confidenceMedium · Reliable media or first-hand reporting
MiniMax introduced MiniMax Sparse Attention (MSA), a dual-branch block sparse attention mechanism built on grouped-query attention. MSA includes a lightweight indexing branch that selects Top-k key-value blocks per query and group, while the main branch only computes attention on those blocks. It matches GQA on benchmarks but reduces attention FLOPs per 1M context by 28.4x. The design was trained on a 109B-parameter mixture-of-experts model with 3 trillion tokens.
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Sources
- MarkTechPost
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