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Core Viewpoint - The article discusses Huawei's innovative Mixture of Grouped Experts (MoGE) architecture, which optimizes the traditional Mixture of Experts (MoE) model to enhance load balancing and computational efficiency in AI applications, particularly in large models [1][2][6]. Summary by Sections Introduction - The MoE model has evolved from its academic origins to become a competitive force in AI, with Huawei's MoGE architecture representing a significant advancement in this field [1]. MoGE Architecture - Huawei's Pangu Pro MoE model features 72 billion total parameters and 16 billion active parameters, achieving superior expert load distribution and computational efficiency [2]. - The model's performance is highlighted by its ranking in the SuperCLUE leaderboard, where it achieved a score of 59, placing it among the top domestic models with fewer parameters compared to competitors [2]. Technical Innovations - The MoGE architecture addresses the core challenge of load imbalance in traditional MoE models by implementing a grouped balanced routing mechanism, ensuring equal activation of experts within defined groups [6][12]. - This design leads to improved throughput and dynamic scalability, making it suitable for various applications [12]. Performance Metrics - The Pangu Pro MoE model demonstrates significant improvements in inference performance, achieving up to 321 tokens per second on the Ascend 300I Duo platform and 1528 tokens per second on the Ascend 800I A2 platform [16]. - The model's capabilities extend across multiple domains, showcasing strong performance in reasoning tasks and cross-language benchmarks [17][18]. Practical Applications - The introduction of Pangu Pro MoE signifies a shift from a focus on parameter quantity to practical effectiveness, enabling enterprises to leverage large models efficiently in real-time scenarios [23]. - Huawei aims to redefine the value of large models, providing a robust foundation for AI applications across various industries [23].