Optimizing MiniMax M3 Sparse Attention on NVIDIA Blackwell

In the context of long-context inference, attention mechanisms are identified as the primary drivers of computational and memory costs, with sparse attention techniques like those used in MiniMax M3 being particularly effective in reducing these costs. However, implementing sparse attention efficiently is complex due to data-dependent selection and irregular memory access patterns. The Fireworks AI Performance team developed a Blackwell (SM100) kernel for M3 sparse attention, leveraging a KV-stationary execution path to mitigate these challenges. This approach involves loading each selected KV block once and attending to every query that selects it, focusing optimization on minimizing memory traffic and improving load balancing. As a result, their implementation achieves significant performance improvements, including a throughput of approximately 980 TFLOP/s at 4.1 TB/s HBM bandwidth, which represents a 1.9–2.4× speedup over a query-stationary baseline and a 1.6× improvement over MiniMax’s open-source MSA kernel. Additionally, the full module performance gains range from 1.18–1.43× over the baseline and 1.32–1.41× over open-source MSA, attributed to optimizations in memory traffic and execution scheduling.

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