DeepSeekMoE: Towards Ultimate Expert Specialization in
  Mixture-of-Experts Language Models
Damai Dai Chengqi Deng Chenggang Zhao R. X. Xu Huazuo Gao Deli Chen Jiashi Li Wangding Zeng Xingkai Yu Y. Wu Zhenda Xie Y. K. Li Panpan Huang Fuli Luo Chong Ruan Zhifang Sui Wenfeng Liang
In the era of large language models, Mixture-of-Experts (MoE) is a promising
architecture for managing computational costs when scaling up model parameters.
However, conventional MoE architectures like GShard, which activate the top-$K$
out of $N$ experts, face challenges in ensuring expert specialization, i.e.
each expert acquires non-overlapping and focused knowledge. In response, we
propose the DeepSeekMoE architecture towards ultimate expert specialization. It
involves two principal strategies: (1) finely segmenting the experts into $mN$
ones and activating $mK$ from them, allowing for a more flexible combination of
activated experts; (2) isolating $K_s$ experts as shared ones, aiming at
capturing common knowledge and mitigating redundancy in routed experts.
Starting from a modest scale with 2B parameters, we demonstrate that
DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5
times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly
approaches the performance of its dense counterpart with the same number of
total parameters, which set the upper bound of MoE models. Subsequently, we
scale up DeepSeekMoE to 16B parameters and show that it achieves comparable
performance with LLaMA2 7B, with only about 40% of computations. Further, our
preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently
validate its substantial advantages over the GShard architecture, and show its
performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%)
of computations.