Core Insights - Morgan Stanley's report compares the operational costs and profit margins of various AI solutions in inference workloads, highlighting that most multi-chip AI inference "factories" have profit margins exceeding 50%, with NVIDIA leading the pack [1][3]. Profit Margins - Among selected 100 MW AI "factories," NVIDIA's GB200 NVL72 "Blackwell" GPU platform achieved the highest profit margin of 77.6%, translating to an estimated profit of approximately $3.5 billion [3]. - Google's self-developed TPU v6e pod ranked second with a profit margin of 74.9%, while AWS's Trn2 UltraServer and Huawei's Ascend CloudMatrix 384 platform reported profit margins of 62.5% and 47.9%, respectively [3]. Performance of AMD - AMD's performance in AI inference is notably poor, with its latest MI355X platform showing a profit margin of -28.2%, and the older MI300X platform at a significantly lower -64.0% [4]. Revenue Generation - NVIDIA's GB200 NVL72 chip generates $7.5 per hour, while the HGX H200 chip produces $3.7 per hour. Huawei's Ascend CloudMatrix 384 platform generates $1.9 per hour, and AMD's MI355X platform only generates $1.7 per hour [4]. - Most other chips generate revenue between $0.5 and $2.0 per hour [4].

Core Viewpoint - The article discusses Huawei's advancements in AI training systems, particularly focusing on the MoE (Mixture of Experts) architecture and its optimization through the MoGE (Mixture of Generalized Experts) framework, which enhances efficiency and reduces costs in AI model training [1][2]. Summary by Sections Introduction to MoE and Huawei's Innovations - The MoE model, initially proposed by Canadian scholars, has evolved significantly, with Huawei now optimizing this architecture to address inefficiencies and cost issues [1]. - Huawei's MoGE architecture aims to create a more balanced and efficient training environment for AI models, contributing to the ongoing AI competition [1]. Performance Metrics and Achievements - Huawei's training system, utilizing the "昇腾+Pangu Ultra MoE" combination, has achieved significant performance metrics, including a 41% MFU (Model Floating Utilization) during pre-training and a throughput of 35K Tokens/s during post-training on the CloudMatrix 384 super node [2][26][27]. Challenges in MoE Training - Six main challenges in MoE training processes are identified: difficulty in parallel strategy configuration, All-to-All communication bottlenecks, uneven system load distribution, excessive operator scheduling overhead, complex training process management, and limitations in large-scale expansion [3][4]. Solutions and Innovations - **First Strategy: Enhancing Training Cluster Utilization** - Huawei implemented intelligent parallel strategy selection and global dynamic load balancing to improve overall training efficiency [6][11]. - A modeling simulation framework was developed to automate the selection of optimal parallel configurations for the Pangu Ultra MoE model [7]. - **Second Strategy: Releasing Computing Power of Single Nodes** - The focus shifted to optimizing operator computation efficiency, achieving a twofold increase in micro-batch size (MBS) and reducing host-bound issues to below 2% [15][16][17]. - **Third Strategy: High-Performance Scalable RL Post-Training Technologies** - The introduction of RL Fusion technology allows for flexible deployment modes and significantly improves resource utilization during post-training [19][21]. - The system's design enables a 50% increase in overall training throughput while maintaining model accuracy [21]. Technical Specifications of Pangu Ultra MoE - The Pangu Ultra MoE model features 718 billion parameters, with a structure that includes 61 layers of Transformer architecture, achieving high performance and scalability [26]. - The training utilized a large-scale cluster of 6K - 10K cards, demonstrating strong generalization capabilities and efficient scaling potential [26][27].

【主编观市】 四月最后一天恒指往上，给五月行情带来指引。 一般放长假海外市场上涨的概率偏大，美股有几个催化： 1，海外AI巨头数据超预期，假期内大涨。如微软、mate等。 2，美国4月非农数据超预期。新增17.7万，大幅超出预估的13.8万增量。 3，特朗普做预期管理，不断释放各种签署协议的所谓利好。 优必选(09880) 2024 年公司实现营收 13.05 亿元，同比+23.7%；毛利润 3.74 亿元，同比+12.4%。主要得益于教育智能 机器人和定制智能机器人产品收入增长。 但这依然只能作为短期来看，看下伯克希尔的现金储备从2024年底的约3340亿美元上升至创纪录的3477 亿美元，显示巴菲特仍在等待合适的投资机会。 当地时间5月7日，美联储将公布最新利率决议。目前市场一致预期，美联储将按兵不动。 对国内而言，汇率走强才是关键，5月5日，离岸人民币盘中一度升穿7.20关口，为去年11月以来首次， 创近半年以来新高。亚洲其它货币也延续上周五的涨势，集体向上脉冲，这意味着美国经济衰退概率上 升、未来利率可能走低。市场普遍预期美元可能续贬值。 财政部今年赤字率按4%安排，比去年提高1个百分点，赤字规模达到 ...