The world of large language models (LLMs) is constantly evolving, and DeepSeek-V3 represents a significant advancement. Developed by DeepSeek AI, this Mixture-of-Experts (MoE) model boasts impressive performance, rivaling even closed-source models while maintaining an open-source ethos. This article delves into the key features of DeepSeek-V3, its architecture, performance, and how to run it locally.
DeepSeek-V3 is a powerful language model with 671 billion total parameters, utilizing a Mixture-of-Experts (MoE) architecture. This means that while the model has a vast number of parameters, only a subset (37 billion) are activated for each token processed. This approach allows for efficient inference and cost-effective training.
Key Highlights:
DeepSeek-V3 incorporates several architectural and training innovations that contribute to its impressive performance.
Innovative Load Balancing: The model utilizes an auxiliary-loss-free strategy for load balancing, minimizing performance degradation typically associated with encouraging load balancing, thus leading to superior results.
Multi-Token Prediction (MPP): Improving model competence to the next level, it allows its usage with speculative decoding techniques for superior performance.
FP8 Mixed Precision Training: DeepSeek-V3 validates the feasibility and effectiveness of FP8 training on extremely large-scale models, leading to ultimate efficiency.
Communication Optimization: Through co-design of algorithms, frameworks, and hardware, the model overcomes communication bottlenecks in cross-node MoE training, achieving near-full computation-communication overlap.
Knowledge Distillation: Reasoning capabilities of the DeepSeek R1 series models, have been incorporated, through verification and reflection patterns into DeepSeek-V3 improving reasoning performance.
DeepSeek-V3 is available in two primary versions:
Both models can be downloaded from Hugging Face.
The total size of the DeepSeek-V3 models on Hugging Face is 685B, including 671B for the Main Model weights and 14B for the Multi-Token Prediction (MTP) Module weights. The DeepSeek team encourages developers to explore the README_WEIGHTS.md file for detailed information on the Main Model weights and the Multi-Token Prediction (MTP) Modules.
DeepSeek-V3 demonstrates strong performance across a range of benchmarks, excelling in areas like math and code.
Key Highlights from Evaluation
For a full list of evaluation details, check the official DeepSeek-V3 Technical Report.
Deploying DeepSeek-V3 locally unlocks greater control and customization. The DeepSeek team has partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. Supported platforms include:
Detailed instructions for running DeepSeek-V3 on each of these platforms can be found in the official documentation. Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation. An example is provided in the original material.
The code repository for DeepSeek-V3 is licensed under the MIT License, allowing for broad use and modification. The use of the DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.
When using DeepSeek-V3 in your research or applications, please cite the following:
@misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, }
DeepSeek-V3 marks a significant step forward in open-source language models. Its innovative architecture, efficient training methodologies, and impressive performance make it a valuable tool for researchers, developers, and anyone interested in exploring the capabilities of large language models. With multiple deployment options and an open-source license, DeepSeek-V3 empowers users to harness the power of cutting-edge AI technology.
