MiniMax Sparse Attention (MSA) cuts per-token compute to 1/20th via block-major KV gathering, enabling 1M-token context with 9.7× prefill and 15.6× decode speedups on compatible hardware.
June 15, 2026
Summary
Long-context agent tasks and video understanding become feasible on standard infrastructure; sparse attention kernel design trades indexing overhead for massive throughput gains, directly reducing inference latency on coding and agentic workloads.
Why it matters
Long-context agent tasks and video understanding become feasible on standard infrastructure; sparse attention kernel design trades indexing overhead for massive throughput gains, directly reducing inference latency on coding and agentic workloads.
Implementation verdict
Replaces full-attention models for 512K+ context use cases. Requires whole-stack optimization (Modular Cloud deployment or custom kernel tuning); sparse selection overhead adds complexity. Enterprise access available today; self-hosted adoption blocked until kernel libraries mature. Worth trying now if you're latency-bound on long documents.
Sources
Dev Signal
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