Flux Attention halves inference cost on long contexts

Flux Attention reduces the computational cost of long-context inference in large language models by allowing transformer layers to dynamically choose between dense and sparse attention, achieving up to 2.8 times speedup during prefill phases without compromising reasoning quality. This innovation is crucial for developers as it enables more efficient handling of extended context windows in production environments, potentially reducing interaction latency significantly.

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