Group 1 - Huawei announced the open-source release of the Pangu model with 70 billion parameters and the Pro MoE 72B model with 720 billion parameters, enhancing the development of large model technology on domestic computing platforms [1] - The Pro MoE 72B model achieves superior performance comparable to trillion-parameter models by dynamically activating a network of experts, with only 160 billion parameters activated during operation [1] - The latest Super CLUE ranking places Huawei's large model as the top domestic model within the trillion-parameter category, indicating significant advancements in the field [1] Group 2 - Huawei's Ascend chips and CANN heterogeneous computing architecture are part of a fully autonomous and optimized closed-loop solution, marking a shift in global AI computing competition towards large-scale system efficiency and ecosystem development [2] - The Ascend super node has been commercially deployed in data centers such as China Telecom, contributing to the growth of the domestic supply chain [2] Group 3 - Huasen Tiancheng has collaborated with Huawei on Ascend chips and AICC intelligent contact center initiatives, indicating a strategic partnership in AI technology [3] - Softcom Power has launched the Softcom Ascend AI workstation to enhance local AI inference and production efficiency, reflecting the industry's push towards localized AI solutions [4]

Core Insights - The Guangdong-Hong Kong-Macao Greater Bay Area is experiencing strong growth in the computing power industry, with Shaoguan becoming the largest smart computing cluster in South China [1][2] - The total computing power scale of the eight major hubs in China's "East Data West Computing" initiative reached 215.5 EFLOPS, accounting for over 70% of the national total [1] - Shaoguan has established a capacity of 120,000 standard racks and attracted 22 smart computing center projects with a total investment exceeding 60 billion [2] Industry Development - A series of technology innovation platforms were launched at the conference, including the Shaoguan Data Investment & Huawei Joint Innovation Laboratory, which will focus on data elements, talent cultivation, and adaptation centers [3] - The AI Edge Alliance was officially established to promote the integration of AI and edge computing, aiming to inject new vitality into the smart computing industry's upgrade [3] - Shaoguan's network latency has improved significantly, with data transmission to Guangzhou taking only 1.3 ms and to Shenzhen 1.66 ms, meeting most computing power business needs in the Greater Bay Area [2] Investment and Projects - The Greater Bay Area Computing Scheduling Center has been completed, and the Guangzhou Data Exchange (Shaoguan) service base is operational [2] - China Telecom plans to invest over 2.4 billion in Shaoguan to build 38,500 standard racks, while Softcom Power Information Technology will develop a computing power scheduling platform [3]

Core Viewpoint - The article discusses the advancements in AI, particularly focusing on Huawei's innovations in the MoE (Mixture of Experts) architecture and the introduction of RL (Reinforcement Learning) post-training techniques, which aim to enhance the efficiency and performance of large language models (LLMs) in the competitive AI landscape [1][3]. Group 1: MoE Architecture and Huawei's Innovations - The MoE model, originally proposed by Canadian scholars, has evolved significantly, with Huawei introducing the MoGE architecture that addresses inefficiencies in the traditional MoE model, leading to cost reduction and improved training and deployment [1]. - Huawei's approach emphasizes the importance of creating a collaborative ecosystem to foster the growth of the Ascend ecosystem in China [1]. Group 2: RL Post-Training Techniques - RL post-training has emerged as a critical pathway to enhance LLM performance, with models like OpenAI's o1 and DeepSeek-R1 leveraging this technique to improve reasoning capabilities in complex tasks [3][5]. - The RL post-training phase currently consumes 20% of the total computational resources, projected to rise to 50%, significantly impacting model performance and costs [3]. Group 3: Challenges in RL Post-Training - The traditional On-Policy algorithms create a "computational black hole" due to the alternating execution of training and inference tasks, leading to underutilization of resources [6][7]. - The complexity of task scheduling in large-scale clusters, exacerbated by the adoption of various parallel strategies, poses significant challenges for efficient resource utilization [8]. Group 4: Innovations in Resource Utilization - Huawei's RL Fusion technology allows a single card to handle both training and inference tasks simultaneously, effectively doubling resource utilization and throughput [9][10]. - The StaleSync mechanism enables near-asynchronous execution of tasks, achieving over 90% efficiency in horizontal scaling across CloudMatrix 384 super nodes [16][20]. Group 5: Performance Metrics and Results - The combination of RL Fusion and StaleSync has led to a significant increase in efficiency, with single-node throughput improving by 78.5% and overall performance enhancement of 1.5 times [30][31]. - StaleSync's implementation in cluster scaling shows a linear throughput increase from 35k tokens/s to 127k tokens/s as the number of super nodes increases, demonstrating its effectiveness in enhancing scalability [32]. Group 6: Conclusion - The advancements in RL post-training techniques by Huawei represent a significant leap in AI efficiency, positioning the company as a key player in the next generation of AI technology [33].

Core Insights - The rapid growth of large models in AI is driving a new era of computing power demand, highlighting the limitations of traditional cluster architectures in efficiently training these models [1][2] - Traditional architectures face significant challenges, including communication bottlenecks, inefficient resource allocation, and reliability issues, which hinder the training efficiency of large models [2][3] Summary by Sections Challenges in Traditional Architectures - Communication bottlenecks have worsened exponentially, with MoE models increasing inter-node communication demands, leading to delays of over 2ms in traditional 400G networks [1][2] - Resource allocation is static and unable to adapt to dynamic changes in model structure, resulting in a 30% decrease in overall training efficiency due to uneven load distribution [1][2] - Reliability is compromised as the probability of node failure increases with scale, causing significant resource waste during lengthy recovery processes, with some companies losing over a million dollars per training interruption [2] Emergence of Ascend Supernode Architecture - The Ascend Supernode architecture represents a fundamental restructuring of computing power systems, characterized by a "three-dimensional integration" approach [3][5] - A breakthrough in hardware interconnectivity allows multiple NPUs to work as a single computer, increasing inter-node communication bandwidth by 15 times and reducing latency from 2ms to 0.2ms [3][5] - Unified global memory addressing through virtualization enables direct memory access across nodes, enhancing efficiency in parameter synchronization during model training [5][6] Innovations in Resource Management and Reliability - Intelligent resource scheduling allows for fine-grained dynamic task allocation based on the MoE model structure, improving the compute-to-communication time ratio from 1:1 to 3:1 [5][6] - The reliability of the system has been significantly improved, with average uptime increasing from hours to days, and recovery times reduced from hours to 15 minutes [5][6] Industry Impact and Future Prospects - The Ascend Supernode architecture has achieved a threefold increase in training performance compared to traditional nodes, establishing a new benchmark in AI computing [8] - The introduction of MindIE Motor enhances large-scale expert parallel capabilities, achieving four times the throughput of traditional server stacks [8] - Huawei's commitment to architecture innovation is seen as a new form of Moore's Law, positioning the company as a leader in the AI computing landscape [9]

Group 1: Power Industry - The overall electricity consumption in April is expected to maintain a growth rate between 4.5% and 5.5% year-on-year, indicating a positive trend for the power sector [5] - Several companies in the power sector have shown significant stock performance, including Huayin Electric (9.91% increase), Huaneng Energy (10.00% increase), and others [5][6] - New energy companies like Xineng Taishan and Jineng Technology also reported strong stock performance, with increases of 10.14% and 9.93% respectively [4][5] Group 2: Computing Power Industry - The launch of the world's first commercial intelligent computing super node in the Guangdong-Hong Kong-Macao Greater Bay Area is expected to boost the computing power sector [7] - Companies such as Zhongdian Xilong and Tianyu Shuke have seen stock increases of 10.00% and 10.07% respectively, driven by developments in computing power [8] Group 3: Robotics Industry - UBTECH has signed a large procurement contract for industrial humanoid robots, which is likely to enhance the robotics sector [9] - Stocks like Dongbei Group have shown strong performance with a 10.00% increase, reflecting positive market sentiment in the robotics industry [9] Group 4: Chemical Industry - Recent fluctuations in chemical product prices have drawn market attention, impacting related companies positively [10] - Companies such as United Chemical and Ding Jixin have reported stock increases of 11.23% and 9.98% respectively, benefiting from the chemical market dynamics [13] Group 5: Advanced Materials - The rise of humanoid robots is expected to benefit upstream core chemical new materials, including PEEK and other high-end engineering plastics [14] - Stocks like Kent Shares and New Trend Materials have seen increases of 14.33% and 13.82% respectively, driven by the demand for advanced materials [15]

智通财经4月28日电，大位科技、鸿博股份涨停，宏景科技、恒润股份、杭钢股份、科华数据、奥飞数 据、云赛智联等涨超5%。消息面上，《科创板日报》记者获悉，4月26日下午，全球首个商用智算昇腾 超节点在中国电信粤港澳大湾区（韶关）算力集群正式商用上线。 算力概念股逆势反弹 大位科技涨停 ...

Group 1: Artificial Intelligence Development - The Central Committee of the Communist Party of China emphasized the importance of strengthening AI development and regulation, advocating for a self-reliant and application-oriented approach to ensure healthy and orderly progress in AI technology [1] - The first Embodied Intelligent Robot Games opened in Wuxi, featuring over 150 robots and participation from more than 100 research teams and companies, alongside discussions on technological innovation [1] Group 2: Major AI Financing - Elon Musk's xAI is in talks to raise approximately $20 billion, which could elevate the company's valuation to over $120 billion, potentially aiding in debt repayment from Twitter's privatization [4] - If successful, this financing round would be the second-largest for a startup, following OpenAI's $40 billion round earlier this year [4] Group 3: AI Model Upgrade - OpenAI announced an upgrade to its GPT-4o model, enhancing its ability to understand complex instructions and improving its performance in professional contexts such as legal consulting and academic writing [5] Group 4: Education and AI - The Ministry of Education held a training program for school leaders to better understand the role of education in China's modernization and the impact of technological advancements on education [6][7] - Emphasis was placed on optimizing knowledge systems, enhancing teacher skills, and developing AI curricula to improve educational outcomes [7] Group 5: Automotive Industry Trends - China's imported car volume has been declining, with a reported 39% decrease in imports for the first quarter of 2025 compared to the previous year, reflecting a broader trend of reduced imports since 2017 [8] Group 6: Technological Innovations - The world's first commercial intelligent computing node was launched by China Telecom in the Guangdong-Hong Kong-Macao Greater Bay Area [3] - A domestically developed wearable single-person flying device successfully completed its first flight in Hangzhou, showcasing advanced features and potential applications in emergency rescue and low-altitude operations [8] Group 7: Corporate Financial Performance - Sichuan Changhong reported a 12.89% increase in revenue and a 96.68% increase in net profit for Q1 2025, attributed to the fair value increase of its investment in Huafeng Technology [11]