The world of Large Language Models (LLMs) is constantly evolving. DeepSeek AI has recently released its latest offering: DeepSeek-V3, a powerful Mixture-of-Experts (MoE) model designed for efficient inference and cost-effective training. This article will delve into the specifics of DeepSeek-V3, its architecture, performance, and how you can run it locally.
DeepSeek-V3 stands out as a strong, open-source LLM boasting 671 billion total parameters, with 37 billion activated for each token. This Mixture-of-Experts (MoE) architecture allows for specialized processing of different types of data, leading to improved performance.
Key highlights of DeepSeek-V3 include:
DeepSeek-V3's innovative approach extends beyond its architecture into its training methodology, focusing on efficiency and scalability.
Key Architectural and Training Features:
DeepSeek-V3 is available in two primary versions:
The total size of the DeepSeek-V3 models on Hugging Face is 685GB, including the main model weights (671B) and Multi-Token Prediction (MTP) module weights (14B).
For those seeking to explore the model weights in detail, README_WEIGHTS.md provides valuable information. MTP support is being actively developed by the community, and contributions are encouraged.
DeepSeek-V3 demonstrates impressive performance across a range of benchmarks, often outperforming other open-source models and rivaling closed-source alternatives.
Key Performance Highlights:
For detailed evaluation results, refer to the DeepSeek-V3 Technical Report.
One of the key advantages of DeepSeek-V3 is the ability to run it locally. DeepSeek AI has partnered with open-source communities and hardware vendors to provide multiple deployment options. Here's a brief overview of the supported methods:
Since the model framework utilizes FP8 training natively, FP8 weights are provided. A conversion script is accessible in the inference directory if you require BF16 weights for experimentation.
Example of converting FP8 weights to BF16:
cd inference
python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights
Important Considerations Before Running Locally:
requirements.txt file in the inference folder of the DeepSeek-V3 GitHub repository lists these dependencies.Beyond local deployment, you can interact with DeepSeek-V3 through DeepSeek's official channels:
DeepSeek-V3 supports commercial use and is released under two licenses:
When using DeepSeek-V3, 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 represents a significant advancement in open-source language models. Its innovative architecture, efficient training methods, and strong performance make it a compelling option for a wide range of applications. With various deployment options available, including local execution and cloud-based APIs, DeepSeek-V3 is accessible to researchers, developers, and businesses alike. As the community actively develops MTP support and optimizes inference, DeepSeek-V3 is poised to become a leading force in the LLM landscape.
