Core Viewpoint - Huawei is building a full-stack AI competitive advantage through software and hardware collaboration, leading to a technological revolution in the communication equipment industry [1] Group 1: Huawei's AI Strategy - Huawei's AI strategy has shifted from benchmarking SOTA models to customizing architectures for Ascend hardware, introducing two innovative pathways: Pangu Pro MoE and Pangu Ultra MoE [1] - These pathways address load imbalance issues and enhance hardware efficiency through a mixture of expert groups (MoGE) architecture and system-level optimization [1] Group 2: New AI Infrastructure - The new generation AI infrastructure, CloudMatrix, utilizes a unified bus (UB) network to create a distributed high-speed memory pool, reducing cross-node communication discrepancies [1] - It supports PDC separation architecture and large-scale expert parallelism (LEP), focusing on distributed system efficiency challenges as large models transition from dense to sparse MoE architectures [1] Group 3: Industry Implications - The communication equipment industry is evolving towards a fully collaborative technical system, with Huawei expanding its software and hardware innovation into AI system engineering [1] - The communication ETF (515880) tracks the communication equipment index (931160), which focuses on the manufacturing and related services of communication equipment, reflecting the overall performance of listed companies in this sector [1] - The index is characterized by high technical content and growth potential, making it a relevant investment focus for those interested in the communication equipment sector [1]

Group 1 - Huawei is building a full-stack AI competitiveness through soft and hard collaboration, shifting its strategy from benchmarking industry SOTA models to customizing model architecture for self-developed Ascend hardware [1] - Huawei has introduced two innovative paths at the large model level: Pangu Pro MoE and Pangu Ultra MoE, addressing load imbalance issues through the mixture of experts (MoGE) architecture and system-level optimization [1] - The new AI infrastructure CloudMatrix creates a distributed high-speed memory pool via a unified bus network, reducing performance discrepancies in cross-node communication, which provides a physical basis for upper-layer software innovation [1] Group 2 - The Growth Enterprise Market Artificial Intelligence ETF from Guotai (159388) tracks the Growth Enterprise Market Artificial Intelligence Index (970070), with a daily fluctuation limit of up to 20% [2] - The index selects listed companies involved in AI technology development and intelligent services from the Growth Enterprise Market, reflecting the overall performance of AI-related listed companies [2] - The index components cover various subfields, including software and hardware research and development, and intelligent application solutions, showcasing significant technological innovation attributes [2]

Group 1 - Huawei is building a full-stack AI competitiveness through soft and hard collaboration, transitioning from industry SOTA models to self-developed Ascend hardware tailored model architectures [1] - The Pangu Pro MoE adopts a mixture of experts (MoGE) architecture to address load imbalance issues, while Pangu Ultra MoE optimizes system-level adaptation for Ascend hardware [1] - The new AI infrastructure CloudMatrix constructs a distributed high-speed memory pool via a unified bus (UB) network, reducing cross-node communication discrepancies and supporting software innovations like PDC separation architecture [1] Group 2 - The software ETF (515230) tracks the software index (H30202), which selects listed company securities involved in software development, system integration, and internet services to reflect the overall performance of the software industry [1] - The index components cover application software, system software, and other segments within the information technology field, showcasing the technological innovation capability and market growth potential of software service companies [1] - Investors without stock accounts can consider the Guotai Zhongzheng All-Index Software ETF Connect A (012636) and Guotai Zhongzheng All-Index Software ETF Connect C (012637) [1]

Core Insights - The article discusses the evolution and optimization of the Mixture of Experts (MoE) model, highlighting Huawei's innovative MoGE architecture that addresses inefficiencies in the original MoE model and enhances cost-effectiveness and deployment ease [1][3]. Group 1: MoE Model Evolution - The MoE model has become a key path for improving large model inference efficiency due to its dynamic sparse computing advantages [3]. - Huawei's Pangu Pro MoE 72B model significantly reduces computational costs and ranks first domestically in the SuperCLUE benchmark for models with over 100 billion parameters [3]. - The Pangu Pro MoE model achieves a 6-8 times improvement in inference performance through system-level optimizations and can reach a throughput of 321 tokens/s on the Ascend 300I Duo [3][30]. Group 2: Optimization Strategies - Huawei's H2P (Hierarchical & Hybrid Parallelism) strategy enhances inference efficiency by allowing specialized communication within task-specific groups rather than a "full team meeting" approach [5][6]. - The TopoComm optimization focuses on reducing communication overhead and improving data transmission efficiency, achieving a 35% reduction in synchronization operations [8][10]. - The DuoStream optimization allows for concurrent execution of communication and computation, significantly improving inference efficiency [11]. Group 3: Operator Fusion - Huawei has developed two specialized fusion operators, MulAttention and SwiftGMM, to optimize resource access and computation scheduling, leading to substantial performance improvements [15][17]. - MulAttention enhances attention computation speed by 4.5 times and achieves over 89% data transfer efficiency [17]. - SwiftGMM accelerates GMM computation by 2.1 times and reduces end-to-end inference latency by 48.7% [20]. Group 4: Inference Algorithm Acceleration - The PreMoE algorithm dynamically prunes experts in the MoE model, improving throughput by over 10% while maintaining accuracy [25]. - The TrimR algorithm reduces unnecessary inference steps by 14% by monitoring and adjusting the model's reasoning process [26]. - The SpecReason algorithm leverages smaller models to enhance the efficiency of larger models, resulting in a 30% increase in throughput [27]. Group 5: Performance Breakthroughs - The Ascend 800I A2 platform demonstrates exceptional performance with a single-card throughput of 1528 tokens/s under optimal conditions [30][31]. - The Ascend 300I Duo platform offers a cost-effective solution for MoE model inference, achieving a maximum throughput of 321 tokens/s [32][33]. - Overall, Huawei's optimizations have established a robust foundation for high-performance, large-scale, and low-cost inference capabilities [33].