Core Viewpoint - The article discusses the challenges and innovations in deploying large models, particularly focusing on Huawei's approach to enhance efficiency and user experience in the context of large language models and the Mixture of Experts (MoE) architecture [1][2]. Group 1: Challenges in Large Model Deployment - The MoE architecture faces significant hardware costs and efficiency issues, making it difficult for Chinese companies to accelerate in the competitive landscape of AI [1]. - As the scale of MoE models continues to grow, the number of experts and total parameters increases exponentially, leading to severe challenges in storage and scheduling [7]. - Traditional communication strategies like AllReduce are inadequate in high concurrency scenarios, leading to inefficiencies in large model inference [8]. Group 2: Innovations by Huawei - Huawei's multi-stream parallel technology breaks the serial constraints of computation, allowing for simultaneous processing of different data streams, significantly reducing key path latency [12][15]. - The AllReduce operation has been innovatively restructured to improve communication efficiency, reducing data transmission volume by 35% and enhancing inference performance by 22-26% [15][17]. - Huawei's FlashComm technology optimizes communication in large model inference by leveraging low-dimensional data characteristics, thus improving end-to-end inference performance [21]. Group 3: Future Directions - Huawei plans to continue innovating in areas such as multi-stream parallelism and automatic weight pre-fetching to further enhance the performance of large model inference systems [21].