DeepSeekMoE is an advanced Mixture-of-Experts (MoE) architecture designed to achieve ultimate expert specialization in large language models. It introduces two key strategies: fine-grained expert segmentation and shared expert isolation. Fine-grained segmentation allows for more flexible and precise knowledge distribution among experts, while shared expert isolation reduces redundancy by dedicating certain experts to capture common knowledge. These innovations enable DeepSeekMoE to achieve high performance with fewer parameters and computations. DeepSeekMoE 2B demonstrates comparable performance to GShard 2.9B, with a significantly lower computational cost. It nearly matches the performance of its dense counterpart with the same number of parameters, setting a new upper bound for MoE models. Scaling up to 16B parameters, DeepSeekMoE 16B achieves performance comparable to LLaMA2 7B with only 40% of the computations. Further scaling to 145B parameters shows consistent advantages over GShard and performance comparable to DeepSeek 67B with much lower computational costs. The architecture is validated through extensive experiments on various benchmarks, including language modeling, understanding, reasoning, reading comprehension, code generation, and question answering. DeepSeekMoE outperforms other MoE models and dense baselines, particularly in tasks requiring specialized knowledge. Ablation studies confirm the effectiveness of both fine-grained segmentation and shared expert isolation in enhancing model performance. DeepSeekMoE 16B is also evaluated on the Open LLM Leaderboard, where it consistently outperforms models with similar activated parameters and achieves comparable performance to LLaMA2 7B. It shows strong performance in math reasoning and code generation, and excels in Chinese benchmarks due to its pretraining on bilingual data. The model is also aligned for chat tasks through supervised fine-tuning, achieving performance comparable to LLaMA2 SFT 7B and DeepSeek Chat 7B. DeepSeekMoE's architecture enables efficient parameter usage and high performance, making it a promising solution for large language models. The model is publicly available, and its performance demonstrates the potential of MoE architectures in achieving high specialization and efficiency.DeepSeekMoE is an advanced Mixture-of-Experts (MoE) architecture designed to achieve ultimate expert specialization in large language models. It introduces two key strategies: fine-grained expert segmentation and shared expert isolation. Fine-grained segmentation allows for more flexible and precise knowledge distribution among experts, while shared expert isolation reduces redundancy by dedicating certain experts to capture common knowledge. These innovations enable DeepSeekMoE to achieve high performance with fewer parameters and computations. DeepSeekMoE 2B demonstrates comparable performance to GShard 2.9B, with a significantly lower computational cost. It nearly matches the performance of its dense counterpart with the same number of parameters, setting a new upper bound for MoE models. Scaling up to 16B parameters, DeepSeekMoE 16B achieves performance comparable to LLaMA2 7B with only 40% of the computations. Further scaling to 145B parameters shows consistent advantages over GShard and performance comparable to DeepSeek 67B with much lower computational costs. The architecture is validated through extensive experiments on various benchmarks, including language modeling, understanding, reasoning, reading comprehension, code generation, and question answering. DeepSeekMoE outperforms other MoE models and dense baselines, particularly in tasks requiring specialized knowledge. Ablation studies confirm the effectiveness of both fine-grained segmentation and shared expert isolation in enhancing model performance. DeepSeekMoE 16B is also evaluated on the Open LLM Leaderboard, where it consistently outperforms models with similar activated parameters and achieves comparable performance to LLaMA2 7B. It shows strong performance in math reasoning and code generation, and excels in Chinese benchmarks due to its pretraining on bilingual data. The model is also aligned for chat tasks through supervised fine-tuning, achieving performance comparable to LLaMA2 SFT 7B and DeepSeek Chat 7B. DeepSeekMoE's architecture enables efficient parameter usage and high performance, making it a promising solution for large language models. The model is publicly available, and its performance demonstrates the potential of MoE architectures in achieving high specialization and efficiency.