DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models · HackerPapers discussions

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models — mentioned by blackbear_

Sat, 06 Jun 2026 06:55:00 GMT

The GPT3 paper is a good starting point Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165 I also enjoyed the papers for DeepSeek and GLM for an overview of all the tricks you need to make these things work DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models https://arxiv.org/abs/2512.02556 GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models https://arxiv.org/abs/2508.06471

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DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models — mentioned by Palmik

Sat, 23 May 2026 08:17:16 GMT

There are several things at play: Inference stack efficiency: Many of these providers take off the shelf sglang / vllm / trtllm and hope for the best. Meanwhile DeepSeek team is known for pushing the boundary of optimizations. Now, sglang and vllm are great pieces of software, but take DeepSeek's Sparse Attention (DSA). Introduced 1.5 years ago (https://arxiv.org/abs/2512.02556), used by DeepSeek 3.2, GLM 5, DeepSeek V4. Only now is it slowly strating to get optimized in the major inference engines: (https://github.com/sgl-project/sglang/issues/19380…

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DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models — mentioned by andy12_

Wed, 04 Feb 2026 16:13:14 GMT

It really isn't sub N^2. The main attention is only O(Nk), but only thanks to a lightning indexer that still has complexity O(N^2). So overall it still has the same complexity; just with a smaller constant factor [1] > DSA reduces the core attention complexity of the main model from O(L^2) to O(Lk), where k (<< L) is the number of selected tokens. Although the lightning indexer still has a complexity of O(L^2), it requires much less computation compared with MLA in DeepSeek-V3.1-Terminus [1] https://arxiv.org/pdf/2512.02556

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DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models — mentioned by esafak

Wed, 10 Dec 2025 02:08:08 GMT

Yeah, DeepSeek Sparse Attention. Section 2: https://arxiv.org/abs/2512.02556

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