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arXiv · 2412.19437

DeepSeek-V3 Technical Report

DeepSeek-AI·Aixin Liu·Bei Feng·Bing Xue·Bingxuan Wang·Bochao Wu·Chengda Lu·Chenggang Zhao·Chengqi Deng·Chenyu Zhang·Chong Ruan·Damai Dai·Daya Guo·Dejian Yang·Deli Chen·Dongjie Ji·Erhang Li·Fangyun Lin·Fucong Dai·Fuli Luo·Guangbo Hao·Guanting Chen·Guowei Li·H. Zhang·Han Bao·Hanwei Xu·Haocheng Wang·Haowei Zhang·Honghui Ding·Huajian Xin·Huazuo Gao·Hui Li·Hui Qu·J. L. Cai·Jian Liang·Jianzhong Guo·Jiaqi Ni·Jiashi Li·Jiawei Wang·Jin Chen·Jingchang Chen·Jingyang Yuan·Junjie Qiu·Junlong Li·Junxiao Song·Kai Dong·Kai Hu·Kaige Gao·Kang Guan·Kexin Huang·Kuai Yu·Lean Wang·Lecong Zhang·Lei Xu·Leyi Xia·Liang Zhao·Litong Wang·Liyue Zhang·Meng Li·Miaojun Wang·Mingchuan Zhang·Minghua Zhang·Minghui Tang·Mingming Li·Ning Tian·Panpan Huang·Peiyi Wang·Peng Zhang·Qiancheng Wang·Qihao Zhu·Qinyu Chen·Qiushi Du·R. J. Chen·R. L. Jin·Ruiqi Ge·Ruisong Zhang·Ruizhe Pan·Runji Wang·Runxin Xu·Ruoyu Zhang·Ruyi Chen·S. S. Li·Shanghao Lu·Shangyan Zhou·Shanhuang Chen·Shaoqing Wu·Shengfeng Ye·Shirong Ma·Shiyu Wang·Shuang Zhou·Shuiping Yu·Shunfeng Zhou·Shuting Pan·T. Wang·Tao Yun·Tian Pei·Tianyu Sun·W. L. Xiao·Wangding Zeng·Wanjia Zhao
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Abstract

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

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DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang, Jin Chen, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Litong Wang, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qiancheng Wang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runxin Xu, Ruoyu Zhang, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Shuting Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wanjia Zhao. 2025-02-18. DeepSeek-V3 Technical Report. https://arxiv.org/abs/2412.19437

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