DeepSeek-V3.2-Exp: Boosting Long-Context Efficiency with DeepSeek Sparse Attention

High-Level Architectural Shift

graph TD
    subgraph A["DeepSeek-V3.1-Terminus (Dense Attention)"]
        direction TB
        style A fill:#f9f,stroke:#333,stroke-width:2px
        Input1[Input Query] --> Attention1{Core Attention};
        All_KV1[All Key-Value Pairs from Context] --> Attention1;
        Attention1 --> Output1[Output];
        Complexity1["Complexity: O(L²)"] -.-> Attention1;
    end

    subgraph B["DeepSeek-V3.2-Exp (Sparse Attention)"]
        direction TB
        style B fill:#ccf,stroke:#333,stroke-width:2px
        Input2[Input Query] --> DSA{"DeepSeek Sparse Attention (DSA)"};
        All_KV2[All Key-Value Pairs from Context] --> DSA;
        DSA -- "Filters to Top-K Pairs" --> Attention2{Core Attention};
        Input2 --> Attention2;
        Attention2 --> Output2[Output];
        Complexity2["Complexity: O(Lk)"] -.-> Attention2;
    end

Inside DeepSeek Sparse Attention (DSA)

graph TD
    subgraph "DSA Internal Workflow"
        A[Input: Query Token & Full Context] --> B[1.Lightning Indexer];
        B -- "Computes relevancy scores for all tokens" --> C[2.Top-k Selector];
        C -- "Selects only the most relevant k tokens" --> D[Output: Sparse Key-Value Pairs];
    end

    D --> E{Main Attention Mechanism};
    F[Original Query Token] --> E;
    E --> G[Final Output];

Core Innovation and Benefits

mindmap
  root((DeepSeek Sparse Attention))
    ::icon(fa fa-lightbulb)
    Core Innovation: Selective Token Processing
      Lightning Indexer
        ::icon(fa fa-bolt)
        Rapidly scores token relevance
      Top-k Selector
        ::icon(fa fa-filter)
        Picks only the highest-scored tokens
    Problem Solved
      High Computational Cost of Dense Attention
        Complexity is O(L²)
        Scales poorly with long contexts
    Key Benefits
      ::icon(fa fa-rocket)
      Improved Efficiency
        New Complexity is O(Lk)
        Faster inference for long sequences
        Reduced API & compute costs
      Comparable Performance
        Maintains model quality despite sparsity

Sources:

• DeepSeek-V3.2-Exp: Boosting Long-Context Efficiency with DeepSeek Sparse Attention