中新网北京12月26日电 (记者 刘育英)随着人工智能大模型向十万亿级参数、全模态融合、异构化训推 方向演进，算力基础设施已从传统服务器集群迈入"超节点时代"。记者从25日举办的昇思人工智能框架 峰会了解到，AI基础设施已进入超节点时代，国产AI框架不断技术创新，构建产学研用协同生态，正 在加速产业落地。 在金融领域，招商银行基于昇思框架的多维混合并行策略，构建了百亿参数金融专精模型，实现了安全 合规、客户投诉处理等场景的高效落地，模型训练稳定运行周期可达1-2个月。 生态协同是超节点时代 AI框架发展的核心支撑。昇思 MindSpore开源五年来，已聚集5.2万名核心贡献 开发者，覆盖全球150多个国家和地区，累计下载量超1300万次，支持25类主流大模型及3100多个行业 应用。(完) 超节点是将多台物理机器通过高速互联技术深度整合，在逻辑层面形成具备资源池化、规模扩展与长稳 可靠特性的"超级计算机"，已成为支撑大模型训练推理的核心算力底座。 针对超节点架构下的技术痛点，昇思MindSpore打造HyperParallel架构。昇思MindSpore开源社区技术委 员会主席金雪锋介绍，该架构把超节点看成一 ...

12月25日，昇思人工智能框架峰会在杭州召开，本次大会的主题是"昇思MindSpore为超节点而生的AI框 架 "。会上，昇思MindSpore聚焦超节点技术创新，打造昇思HyperParallel架构，加速新模型结构和新训 推范式创新，引领AI框架迈入"超节点时代"。 华为中央软件院总裁谢桂磊在致辞中表示，人工智能发展浪潮奔涌向前，昇思MindSpore致力于打造超 节点亲和、全场景融合、架构开放、敏捷使能的人工智能框架，以更前沿的技术、更易用的体验、更开 放的社区，助力千行万业智能化转型。 昇思MindSpore开源社区技术委员会主席金雪锋在演讲中指出，昇思HyperParallel架构把超节点看成一 台"超级计算机"进行编程和调度，充分发挥超节点架构优势，实现HyperShard声明式并行编程、 HyperMPMD异构非规则并行、HyperOffload多级智能卸载等特性，加速Agentic AI、全模态等新模型结 构和训推范式创新。 昇思MindSpore开源社区理事长王紫东接受智通财经记者采访时表示："昇思这一次相当于比较高调地提 出我们就是为超节点而生的AI框架。" 王紫东解释称，超节点就是把一些 ...

Core Insights - The article emphasizes the importance of solidifying AI technology capabilities and addressing industry implementation challenges as key competitive advantages for companies in the AI sector [1][15] - Huawei's "Ascend" initiative aims to enhance partner capabilities through targeted training, focusing on practical AI applications and ecosystem development [1][13] Group 1: Event Overview - The "Ascend 'Forging Golden Seeds'" training event took place from November 27 to 29, attracting 132 AI developers from 38 partners, focusing on comprehensive technical training [1][10] - The training included hands-on sessions covering foundational architecture, model applications, and programming practices [1][10] Group 2: Training Content - The first day focused on AI application development and optimization, introducing the Ascend hardware and software platform, including the MindIE model inference engine and RAG SDK [3][10] - The second day delved into large model training technologies, featuring the MindSpeed distributed training acceleration suite and the MindSpore AI framework [5][6] - The third day concentrated on low-level CANN operator programming, highlighting the Ascend C programming language for high-performance operator development [8][10] Group 3: Training Methodology - The event broke away from traditional one-way training, integrating technical lectures, developer interactions, and collaborative demand creation [10] - Participants praised the clarity of instruction and the practical nature of the training, indicating a successful learning experience [10][15] Group 4: Future Outlook - Huawei aims to continue collaborating with partners and developers to deeply integrate AI technology into industry practices, accelerating smart upgrades and fostering innovation [15]

Core Viewpoint - The article discusses the challenges and strategies in achieving breakthroughs in artificial intelligence (AI) technology, particularly through the development of Huawei's "CloudMatrix 384 Super Node" computing cluster solution, which aims to overcome limitations in single-point technology by leveraging system engineering innovations [1][3]. Group 1: Huawei's Technological Advancements - Huawei's "CloudMatrix 384 Super Node" is built on 384 Ascend chips and can provide up to 300 PFLOPs of dense BF16 computing power, surpassing NVIDIA's B200 NVL 72 platform [3][4]. - The development of the "Super Node" reflects Huawei's foresight in addressing the diminishing returns of Moore's Law and the increasing costs associated with semiconductor advancements [4][9]. - The architecture of the "Super Node" features a fully interconnected high-speed bus system, enhancing communication bandwidth by 15 times and reducing latency significantly [8][9]. Group 2: System Engineering Innovations - Huawei's approach involves a comprehensive system-level redesign to address challenges in large-scale model training, focusing on resource allocation and communication efficiency [5][10]. - The implementation of global memory unified addressing allows for direct memory access across nodes, improving the efficiency of parameter synchronization during model training [8][9]. - The resource scheduling has been upgraded to enable dynamic task distribution based on model structure, optimizing computation and communication time [8][10]. Group 3: Collaborative Ecosystem Development - Huawei has mobilized a large team across various departments to enhance collaboration and innovation in AI infrastructure, showcasing a unique multi-industry cluster advantage [10][12]. - The company emphasizes the importance of ecosystem compatibility, ensuring that its Ascend architecture supports popular deep learning frameworks like PyTorch and TensorFlow [12][13]. - Huawei's commitment to improving the usability of its AI frameworks, such as MindSpore, aims to facilitate a smoother transition for developers accustomed to existing platforms [12][13]. Group 4: Future Prospects and Industry Impact - The advancements in Huawei's computing capabilities are positioned as a significant step for China's AI industry, potentially overcoming technological limitations and fostering innovation [12][13]. - The ongoing development of the Ascend ecosystem is expected to take time, but efforts are being made to enhance compatibility and support for developers [12][13]. - Huawei's recent achievements in large model training, including the Pangu Ultra MoE model, demonstrate the potential of its domestic computing platform to produce world-class AI models [10][12].

Core Viewpoint - The article emphasizes the rapid development of large models and the need for efficient migration and deployment solutions in the AI ecosystem, particularly through the use of MindSpore, which aims to facilitate seamless integration and performance optimization for developers [1][2]. Group 1: Migration Challenges - The first challenge is fast migration, enabling zero-cost migration of third-party framework models while ensuring complete alignment in model accuracy. MindSpore achieves this through a threefold compatibility approach, allowing for zero-code migration of mainstream models and improving training performance by 5% while maintaining distributed parallel strategies [4]. - The second challenge is rapid deployment, automating the entire training-to-inference process to make large model deployment as simple as executing a single command [2]. Group 2: Training and Inference Solutions - MindSpore supports Day 0 migration for training, providing a "no-sense intelligent translation" capability across frameworks. It utilizes tools like MindSpeed/Megatron for seamless PyTorch model migration, achieving near-zero migration loss for popular models [4]. - In inference deployment, the vLLM-MindSpore plugin allows for HuggingFace models to be deployed in under 30 minutes, with an 80% reduction in weight loading time for large models [5][6]. Group 3: Open Source and Community Engagement - Since its open-source inception on March 28, 2020, MindSpore has fostered a vibrant developer community, with over 1.2 million downloads and contributions from more than 46,000 developers across 2400 cities [7]. - The company promotes a collaborative ecosystem through community governance, providing free computing resources and knowledge sharing across 20+ technical special interest groups (SIGs) [8].

Core Viewpoint - The article emphasizes the rapid development of large models and the need for efficient migration and deployment solutions in the AI ecosystem, highlighting the capabilities of MindSpore in facilitating these processes for developers [1][2]. Group 1: Migration Capabilities - MindSpore supports Day0 migration for training, enabling seamless cross-framework "intelligent translation" capabilities, allowing zero-code migration of mainstream models with a performance improvement of over 5% in distributed training scenarios [3][4]. - The framework utilizes dynamic graph compilation optimization, enhancing single-card training efficiency by 40%, and implements distributed intelligent tuning to overcome training bottlenecks, achieving a linearity breakthrough of 96% [4]. Group 2: Deployment Efficiency - MindSpore enables one-click deployment for inference, allowing model services to be launched in minutes, with support for direct loading of Hugging Face weights without format conversion [5]. - The deployment process is optimized, reducing weight loading time by 80% for models with hundreds of billions of parameters, and achieving millisecond-level graph compilation delays [5]. Group 3: Open Source Ecosystem - Since its inception in March 2020, MindSpore has developed a robust open-source ecosystem, with over 50 mainstream large models and 12 million downloads, engaging over 46,000 developers across 130 countries [7][8]. - The company promotes community governance through a dual-driven model of a council and SIG groups, providing free computing resources and knowledge sharing opportunities for developers [8].

Core Viewpoint - The consensus in the AI large model era is that no single large model can dominate the market, leading to challenges for developers in navigating various mainstream models and AI technologies [1][2]. Group 1: MindSpore Overview - Huawei's open-source MindSpore offers a solution for developers to experience and migrate mainstream state-of-the-art (SOTA) large models with minimal code changes, ensuring precision and performance remain intact [3][4]. - The training to inference process is fully automated, allowing over 20 mainstream large models to be deployed out of the box, with loading times for models with billions of parameters under 30 seconds [5][19]. Group 2: Migration and Deployment Features - MindSpore's "translation tool" MSAdapter enables seamless migration of code from other frameworks to MindSpore, achieving nearly zero loss during the transition [8][10]. - The tool can automatically convert over 95% of interfaces, maintaining a user-friendly experience similar to the original framework [10]. Group 3: Technical Enhancements - MindSpore employs several unique techniques to accelerate training and debugging, including multi-stage processing of operators, JIT compilation for efficient code execution, and automatic strategy optimization, which improved performance by 9.5% in specific training scenarios [11][13][16]. - The code modification required for distributed task initiation is minimal, with Python script changes being less than 1% and automated through patch tools [14]. Group 4: Inference Deployment - The vLLM-MindSpore plugin allows for rapid deployment of HuggingFace models, achieving service readiness in under 30 minutes [18][23]. - For large models, MindSpore has restructured the inference process, achieving a throughput of 1020 tokens per second with a latency of less than 100ms for specific models [19]. Group 5: Performance Improvements - The loading time for model weights has been reduced by 80%, with billion-parameter models loading in under 30 seconds, and graph compilation delays minimized to the millisecond range [23].

Core Viewpoint - The consensus in the AI large model era is that no single large model can dominate the market, leading to challenges for developers in navigating various mainstream models and AI technologies [1][2]. Group 1: MindSpore Overview - Huawei's open-source MindSpore offers a solution for developers to experience mainstream large models within a unified framework [3]. - MindSpore enables "Day0 migration" of large models with minimal code changes while maintaining accuracy and performance [4]. Group 2: Migration and Deployment Features - The inference process is automated for one-click deployment, allowing over 20 mainstream large models to be used out of the box, with loading times for models with billions of parameters under 30 seconds [5][23]. - MindSpore's "translation tool" MSAdapter allows for seamless migration of code from other frameworks, achieving nearly zero loss in performance [8][10]. Group 3: Technical Enhancements - MindSpore employs several unique techniques to accelerate training and debugging, including multi-stage processing, JIT compilation, and automatic strategy optimization, resulting in performance improvements of up to 9.5% [11][13][16]. - The code modification required for distributed task initiation is minimal, with Python script changes being less than 1% [14]. Group 4: Inference Deployment - The vLLM-MindSpore plugin facilitates the deployment of HuggingFace models within half an hour, with significant reductions in loading times and latency [18][23]. - For large models like Pangu Pro MoE 72B, the deployment can achieve a throughput of 1020 tokens per second with a latency of under 100ms [19].

Core Viewpoint - The article emphasizes the capabilities of MindSpore in supporting large model training and deployment, highlighting its focus on seamless migration and efficient inference processes for developers in the AI ecosystem [2][3]. Group 1: Migration and Training Efficiency - MindSpore enables "zero-cost" migration of third-party framework models, ensuring model accuracy alignment while enhancing training performance by 5% under distributed parallel strategies [8]. - The framework supports zero-code migration for PyTorch models, allowing direct execution of training scripts and achieving near-zero migration loss for mainstream models like DeepSeek and PangU [8][9]. - The technology architecture of MindSpore facilitates rapid migration and training efficiency improvements, addressing the challenges of evolving model architectures [5][9]. Group 2: Inference Deployment - MindSpore allows for one-click deployment of models, with HuggingFace models being deployed in under 30 minutes using the vLLM-MindSpore plugin [11]. - The framework supports direct loading of HuggingFace weights without format conversion, optimizing service launch times by reducing weight loading time by 80% for models with hundreds of billions of parameters [12]. - The deployment process is designed to be agile, enabling model services to be initiated in minutes [11][12]. Group 3: Open Source Ecosystem - Since its open-source launch on March 28, 2020, MindSpore has fostered a vibrant developer community, with over 1.2 million downloads and contributions from more than 46,000 developers across 130 countries [13]. - The framework promotes innovation through features like dynamic graph compilation optimization, distributed intelligent tuning, and layer-wise precision alignment, enhancing training efficiency by 40% [14]. - MindSpore's community governance model includes a council and special interest groups (SIGs) to collaboratively define technical directions and share resources [15].

Core Viewpoint - The article emphasizes the rapid evolution of large models and the need for efficient migration and deployment solutions in the AI development ecosystem, highlighting the capabilities of MindSpore in facilitating these processes. Group 1: Migration and Deployment Solutions - MindSpore supports Day0 migration for training, enabling seamless cross-framework model transfer with zero-code migration and maintaining model accuracy, achieving a 5% improvement in training performance under distributed parallel strategies [2][5]. - The deployment process is automated, allowing for quick model service initiation, with HuggingFace models being deployable in under 30 minutes using the vLLM-MindSpore plugin [6][7]. Group 2: Ecosystem and Community Engagement - Since its open-source launch on March 28, 2020, MindSpore has fostered a vibrant developer community, with over 1.2 million downloads and contributions from more than 46,000 developers across 130 countries [8][9]. - The community-driven approach includes a governance model with a council and special interest groups (SIGs) to collaboratively define technical directions [9]. Group 3: Technical Innovations - MindSpore employs advanced techniques such as multi-level pipelining and just-in-time (JIT) compilation, resulting in a 40% increase in single-card training efficiency [10]. - The platform also features automated load balancing tools to address the "bottleneck effect" in large-scale training, achieving over 96% linearity in performance [10].