RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models
A novel sparse attention technique that accelerates long-context prefill by prioritizing relevant token clusters.
Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation begins. Sparse block selection can reduce this cost, but a block centroid may hide a highly relevant token among many irrelevan...
ArXiv CS.AI
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RBS-Attention addresses the efficiency bottleneck in processing long prompts by intelligently focusing on localized token clusters rather than computing dense global attention. This optimization significantly lowers latency and memory overhead for large-context tasks, enabling faster deployment of LLMs in production environments.
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