In the field of artificial intelligence, the DeepSeek team recently released a breakthrough research result, launching an innovative sparse attention mechanism called NSA (Native Sparse Attention). The core goal of this technology is to revolutionize the development and application of AI models by optimizing modern hardware performance and significantly improving the speed of long-context training and inference.
The launch of NSA technology marks a significant improvement in the training efficiency of artificial intelligence models. Through deep optimization of modern computing hardware, NSA not only greatly improves inference speed, but also significantly reduces the cost of pre-training. More importantly, while improving efficiency, NSA still maintains high levels of model performance, ensuring its outstanding performance in a variety of tasks.
The DeepSeek team adopted a layered sparse strategy in the study, dividing the attention mechanism into three key branches: compression, selection, and sliding windows. This design allows the model to capture both global context and local details, significantly improving the model's processing power for long text. In addition, NSA optimization in memory access and computing scheduling has greatly reduced the computational delay and resource consumption of long context training.
NSA demonstrates its outstanding performance in a series of general benchmarks. Especially in long context tasks and instruction-based reasoning, NSA performance is even comparable to the full attention model, and in some cases is better. The release of this technology not only marks another leap in AI training and reasoning technology, but also injects new impetus into the future development of artificial intelligence.
NSA paper (https://arxiv.org/pdf/2502.11089v1).
The introduction of NSA technology significantly improves the speed of long context training and reasoning and reduces pre-training costs. The layered sparse strategy is adopted to divide the attention mechanism into compression, selection and sliding windows, which enhances the model's processing ability of long text. NSA performed well in several benchmarks, in some cases surpassing the traditional full attention model.