Star Attention: Efficient LLM Inference over Long Sequences

Shantanu Acharya; Fei Jia; Boris Ginsburg

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arXiv · 2411.17116

Star Attention: Efficient LLM Inference over Long Sequences

Shantanu Acharya·Fei Jia·Boris Ginsburg

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Abstract

Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead. In the first phase, the context is processed using blockwise-local attention across hosts, in parallel. In the second phase, query and response tokens attend to all prior cached tokens through sequence-global attention. Star Attention integrates seamlessly with most Transformer-based LLMs trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 97-100% of accuracy.

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Shantanu Acharya, Fei Jia, Boris Ginsburg. 2024-11-26. Star Attention: Efficient LLM Inference over Long Sequences. https://arxiv.org/abs/2411.17116

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