Star Attention: Efficient LLM Inference over Long Sequences
Keywords: Large Language Model, Transformers, Long Context, Efficient Inference, Local and Global Attention
TL;DR: Inference technique combining local and global attention giving upto 11x speedup while retaining 95-100% accuracy.
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 95-100% of accuracy.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 8336
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