DeepSeek releases ‘sparse attention’ model that cuts API costs in half

DeepSeek, a research company, has released a new experimental model called "sparse attention" that aims to significantly reduce the costs associated with API usage in long-context operations. The model is designed to achieve this by using a more efficient attention mechanism, which is a crucial component in many language models. The researchers claim that the sparse attention model can cut API costs in half compared to traditional models, making it a more cost-effective solution for organizations and developers working with large-scale language processing tasks. This could be particularly beneficial for applications that require processing of lengthy documents or multi-turn conversations. The release of this model is seen as a significant development in the field of natural language processing, as it has the potential to make high-performance language models more accessible and practical for a wider range of use cases. The researchers have made the model available for testing and evaluation, and they are encouraging the community to provide feedback and contribute to its further development.