Star Attention: Efficient LLM Inference over Long Sequences
Authors: name: Shantanu Acharya, name: Fei Jia, name: Boris Ginsburg

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.