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한국어(KO) Avi Chawla (@_avichawla) DeepSeek가 최근 공개한 V3.2 모델에 DeepSeek Sparse Attention(DSA)을 도입해, 어텐션 복잡도를 O(L²)에서 O(Lk)로 낮췄다는 내용이다. 긴 문맥 처리에서 효율성을 크게 개선하는 희소 어텐션 기술과 Lig

DeepSeek V3.2 model introduces Sparse Attention for improved long-context processing

By PulseAugur Editorial · [1 sources] ·

DeepSeek has introduced its V3.2 model, incorporating DeepSeek Sparse Attention (DSA). This innovation reduces attention complexity from O(L²) to O(Lk), significantly enhancing efficiency for processing long contexts. The model's architecture also leverages Lightning Indexer for further performance gains. AI

IMPACT Improves efficiency for long-context processing, potentially enabling new applications.

RANK_REASON Release of a new model version with a novel attention mechanism.

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DeepSeek V3.2 model introduces Sparse Attention for improved long-context processing

COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 한국어(KO) · [email protected] ·

    Avi Chawla (@_avichawla) introduces DeepSeek Sparse Attention (DSA) to DeepSeek's recently released V3.2 model, reducing attention complexity from O(L²) to O(Lk). Sparse attention technology significantly improves efficiency in long context processing and Lig

    Avi Chawla (@_avichawla) DeepSeek가 최근 공개한 V3.2 모델에 DeepSeek Sparse Attention(DSA)을 도입해, 어텐션 복잡도를 O(L²)에서 O(Lk)로 낮췄다는 내용이다. 긴 문맥 처리에서 효율성을 크게 개선하는 희소 어텐션 기술과 Lightning Indexer의 동작 원리를 소개한다. https:// x.com/_avichawla/status/204831 2925904052649 # deepseek # sparseattention # llm # …

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