Flash Sparse Attention (FSA) implements efficient kernel designs for Native Sparse Attention (NSA) in LLMs. The library offers a novel approach to sparse attention computation optimized for modern GPUs, enabling faster training and inference by batching query heads that share key-value head configurations. It includes a beta version of one-step decoding and tools for benchmarking the attention module performance against standard implementations.