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Donate arxiv logo > cs > arXiv:2505.21411 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2505.21411 (cs) [Submitted on 27 May 2025 (v1), last revised 28 May 2025 (this version, v2)] Title:Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Authors:Yehui Tang, Xiaosong Li, Fangcheng Liu, Wei Guo, Hang Zhou, Yaoyuan Wang, Kai Han, Xianzhi Yu, Jinpeng Li, Hui Zang, Fei Mi, Xiaojun Meng, Zhicheng Liu, Hanting Chen, Binfan Zheng, Can Chen, Youliang Yan, Ruiming Tang, Peifeng Qin, Xinghao Chen, Dacheng Tao, Yunhe Wang (and Other Contributors) View a PDF of the paper titled Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity, by Yehui Tang and 21 other authors View PDF HTML (experimental) Abstract:The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because only a small fraction of parameters are activated for each input token. However, it is commonly observed that some experts are activated far more often than others, leading to system inefficiency when running the experts on different devices in parallel. Therefore, we introduce Mixture of Grouped Experts (MoGE), which groups the experts during selection and balances the expert workload better than MoE in nature. It constrains tokens to activate an equal number of experts within each predefined expert group. When a model execution is distributed on multiple devices, this architectural design ensures a balanced computational load across devices, significantly enhancing throughput, particularly for the inference phase. Further, we build Pangu Pro MoE on Ascend NPUs, a sparse model based on MoGE with 72 billion total parameters, 16 billion of which are activated for each token. The configuration of Pangu Pro MoE is optimized for Ascend 300I Duo and 800I A2 through extensive system simulation studies. Our experiments indicate that MoGE indeed leads to better expert load balancing and more efficient execution for both model training and inference on Ascend NPUs. The inference performance of Pangu Pro MoE achieves 1148 tokens/s per card and can be further improved to 1528 tokens/s per card by speculative acceleration, outperforming comparable 32B and 72B Dense models. Furthermore, we achieve an excellent cost-to-performance ratio for model inference on Ascend 300I Duo. Our studies show that Ascend NPUs are capable of training Pangu Pro MoE with massive parallelization to make it a leading model within the sub-100B total parameter class, outperforming prominent open-source models like GLM-Z1-32B and Qwen3-32B. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2505.21411 [cs.CL] (or arXiv:2505.21411v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2505.21411 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hang Zhou [view email] [v1] Tue, 27 May 2025 16:40:21 UTC (710 KB) [v2] Wed, 28 May 2025 10:42:15 UTC (710 KB) Full-text links: Access Paper: View a PDF of the paper titled Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity, by Yehui Tang and 21 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.CL < prev | next > new | recent | 2025-05 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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