Researchers at DeepSeek on Monday released a new experimental model called V3.2-exp, designed to have dramatically lower inference costs when used in long-context operations. DeepSeek announced the model with a post on Hugging Face , also posting a linked academic paper on GitHub.

The most important feature of the new model is called DeepSeek Sparse Attention, an intricate system described in detail in the diagram below. In essence, the system uses a module called a “lightning indexer” to prioritize specific excerpts from the context window. After that, a separate system called a “fine-grained token selection system” chooses specific tokens from within those excerpts to load into the module’s limited attention window. Taken together, they allow the Sparse Attention models to operate

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