DeepSeek-V4.1 Flash is a multimodal mixture-of-experts model that has been making waves in the AI community with its impressive performance. The model's architecture is designed for aggressive KVCache compression, with a parameter scale of 552B and native support for multimodal input. This allows it to support contexts of up to 1 million tokens, making it a significant improvement over previous models.
The model's performance is due in part to its Causal Encoder-Decoder (CED) architecture, which reduces prefill computation by nearly half while maintaining performance comparable to the baseline. The CED architecture is inspired by the YoCo concept, which reduces prefill computation by letting the upper-half layers directly share the KV Cache produced by the lower-half layers.
Another key component of the model is its CSA2 (Cross-Layer Sparse Attention) mechanism, which jointly utilizes three dimensions to reduce KV cache storage and attention computation. The CSA2 mechanism shares the main KV and Indexer K across layers, allowing layers to reuse Top-K indices while decoupling cache sharing and index reuse.
The model also features a Hierarchical Sparse Indexer (HSI) processing approach, which constructs a block-level selection as the Candidate Pool for subsequent Reindex Mode CSA2. This approach reduces the overhead of the sparse attention indexer and improves training efficiency.
Overall, DeepSeek-V4.1 Flash is a significant improvement over previous models, with its impressive performance and reduced KVCache storage making it a major breakthrough in the field of AI.
Cet article a été rédigé avec l'assistance de l'IA.
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