Title: Optimizing Mixture of Block Attention
Primary area: foundation or frontier models, including LLMs
Keywords: LLM, Efficiency, Attention

Abstract: Mixture of Block Attention (MoBA) is a promising building block for efficiently processing long contexts in LLMs by enabling queries to sparsely attend to a small subset of key-value blocks, drastically reducing computational cost.
However, the design principles governing MoBA's performance are poorly understood, and it lacks an efficient GPU implementation, hindering its practical adoption.
In this paper, we first develop a statistical model to analyze MoBA's underlying mechanics. Our model reveals that performance critically depends on the router's ability to accurately distinguish relevant from irrelevant blocks based on query-key affinities. We derive a signal-to-noise ratio that formally connects architectural parameters to this retrieval accuracy. Guided by our analysis, we identify three key pathways for improvement: using smaller block sizes, increasing head dimensions, and applying a short convolution on keys to cluster relevant signals, which enhances routing accuracy.
While theoretically better, small block sizes are inefficient on GPUs. To bridge this gap, we introduce FlashMoBA, a hardware-aware CUDA kernel that enables efficient MoBA execution even with the small block sizes our theory recommends. We validate our insights by training LLMs from scratch, showing that our improved MoBA models match the performance of dense attention baselines. FlashMoBA achieves up to 9× speedup over FlashAttention-2 for small blocks, making our theoretically-grounded improvements practical. Code will be released upon publication.