Paper: https://arxiv.org/pdf/2411.17116)
The paper introduces Star Attention, a novel two-phase attention mechanism for efficient Large Language Model (LLM) inference on long sequences. It improves computational efficiency by sharding attention across multiple hosts, using blockwise-local attention in the first phase and sequence-global attention in the second. This approach achieves up to an 11x speedup in inference time while maintaining high accuracy (95-100%). The effectiveness of Star Attention is demonstrated through experiments on various LLMs and benchmarks, exploring the trade-off between speed and accuracy based on block size and anchor block design. The research also analyzes the algorithm's performance across different task categories.
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