This research paper by DeepSeek introduces NSA, a Natively trainable Sparse Attention mechanism designed to improve the efficiency of long-context language models. NSA employs a dynamic hierarchical sparse strategy that combines token compression with fine-grained selection, optimizing for both performance and hardware utilization. Experiments demonstrate that NSA achieves comparable or superior performance to full attention models on various benchmarks while significantly accelerating decoding, forward propagation, and backward propagation. The key innovations include a hardware-aligned system optimized for modern GPUs and a training-aware design that enables end-to-end training. The study also explores alternative sparse attention strategies and visualizes attention patterns, providing insights for future research. Ultimately, NSA offers a promising approach to efficient long-context modeling by balancing model capability and computational efficiency.____#llm #artificialintelligence #gpt #ai Hosted on Acast. See acast.com/privacy for more information.

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