在数字化浪潮席卷全球的今天，开源开放已成为激发产业创新活力、凝聚发展合力的关键路径。华为作 为信息通信业的领军企业，自2010年起逐步布局开源领域，从最初的 "使用开源""参与开源"，成长为 如今的 "主导开源"，构建起了覆盖昇腾、鲲鹏、欧拉等核心平台的全链条开源体系。 赋能产业协同共生 相对于通用计算，在智能计算领域，随着大模型规模不断扩大、输入长度持续增加、数据需求指数级增 长，算力需求呈现爆发式增长。"国家去年发布的日token消耗达300万亿，未来可能突破每日千万亿， 近日，在以"算力创新领航开源开放共创"为主题的沙龙上，华为计算产品线多位高管对《中国经营报》 记者表示，华为正通过开源开放不断推动产业生态的繁荣发展，不仅在技术层面实现了深度共享与协同 创新，更在商业模式、生态构建等方面展现出前瞻性的布局。通过开源，华为有效促进了产业链上下游 企业的紧密合作，共同探索算力创新的新路径，为数字经济的蓬勃发展注入了强劲动力。 构建全链条产业生态 "华为计算产业从2019年正式创立至今，始终坚守'硬件开放、软件开源、使能伙伴、发展人才'的十六 字方针，这不是一句口号，而是我们六年来始终践行的核心战略。"华为计 ...

Core Viewpoint - NVIDIA currently holds a near-monopoly in the AI training and inference chip market, driven by advanced technology and an unmatched ecosystem, making it the highest-valued public company globally with a market capitalization of approximately $4.5 trillion as of November 2025, and a year-over-year revenue growth of about 62% in Q3 2025 [2]. Competitive Landscape - NVIDIA faces competition from traditional chip giants like AMD and Intel, as well as tech companies like Google and Amazon with their custom chips, and emerging players like Cerebras and Groq. However, none have significantly challenged NVIDIA's leadership position so far [2]. - The AI compute chip market has two main applications: training and inference, with training being the core bottleneck in the early and mid-stages of large model development [4][5]. Training Dominance - NVIDIA's dominance in training compute stems from advanced technology and a monopolistic ecosystem. The training of large models requires massive computational power, necessitating large-scale chip clusters and a comprehensive software system to connect engineers, chips, and models [6]. - Key requirements for training chips include single-chip performance, interconnect capabilities, and software ecosystem [6]. - NVIDIA excels in single-chip performance, but competitors like AMD are closing the gap. However, this alone does not threaten NVIDIA's lead in AI training [7]. - Interconnect capabilities are crucial for large model training, with NVIDIA's proprietary NVLink and NVSwitch enabling efficient interconnectivity at a scale of tens of thousands of chips, while competitors struggle to achieve similar scales [7]. Ecosystem Advantage - NVIDIA's ecosystem advantage is primarily software-based, with CUDA being a well-established programming platform that fosters a strong developer community and extensive resources, enhancing user stickiness [8][9]. - The ecosystem's network effects mean that as more developers engage with CUDA, its value increases, creating a significant barrier to entry for competitors [10]. Inference Market Dynamics - Inference requires significantly fewer chips than training, leading to reduced interconnect demands. Consequently, NVIDIA's ecosystem advantage is less pronounced in inference compared to training [12]. - Despite this, NVIDIA still holds over 70% of the inference market share due to its competitive performance, price, and development costs [13]. Challenges to NVIDIA - Competitors must overcome both technical and ecosystem challenges to compete with NVIDIA. If they cannot avoid ecosystem disadvantages, they must achieve significant technological advancements [15]. - In the U.S., challengers are focusing on custom AI chips (ASICs), with Google's TPU showing promising results. However, the ecological disadvantage remains a significant hurdle [16]. - In China, U.S. export restrictions on advanced chips have created a protected market, limiting NVIDIA's ecosystem influence and presenting opportunities for local chip manufacturers [17][18]. Strategic Considerations - The geopolitical landscape has led to a potential rise of strong domestic competitors in China, as developers begin to adapt to local ecosystems like CANN, despite initial challenges [19]. - The U.S. government's recent policy shift allowing NVIDIA to sell advanced chips to China under specific conditions reflects a recognition of the need to maintain NVIDIA's competitive edge while managing technological disparities [19]. - A balanced approach is necessary for China to foster its AI chip industry while allowing for essential imports to support core AI projects [19].

Core Viewpoint - The article discusses Huawei's recent announcement regarding the open-source of its Ascend CANN software, which aims to lower the barriers for AI tool development and foster a new AI computing ecosystem [2][30]. Group 1: CANN Open Source and Developer Empowerment - CANN, which stands for Compute Architecture for Neural Networks, serves as a bridge between AI training frameworks and underlying AI chips, allowing developers to utilize computing power without needing to understand chip details [2][5]. - The open-source nature of CANN has garnered significant attention in the industry, as it empowers developers to define computing capabilities and customize their AI models [2][6]. - CANN supports seamless integration with major AI frameworks such as PyTorch, TensorFlow, MindSpore, and PaddlePaddle, enhancing developer flexibility [5][6]. Group 2: Development Paths Offered by CANN - CANN provides three development paths for different types of developers: 1. For those familiar with Python, CANN integrates with the Triton ecosystem, allowing easy migration of existing code [9]. 2. For system-level programmers seeking high performance, Ascend C offers low-level resource management capabilities [10]. 3. For developers looking for ease of use, the CATLASS operator template library simplifies the creation of matrix multiplication operators [11][13]. - The MLAPO fusion operator, part of the CATLASS library, significantly reduces computation time and enhances performance in large models [15]. Group 3: Architectural Innovations - CANN's architecture features a layered decoupling approach, allowing independent evolution of components, which reduces integration complexity for developers [21][22]. - The decoupling enables developers to selectively upgrade specific components based on their needs, facilitating easier customization and integration [23][29]. - CANN has transitioned from a monolithic software structure to a modular one, with independent components for various functionalities, enhancing flexibility and performance [24][26]. Group 4: Open Source Community and Growth - The open-source initiative of CANN is actively progressing, with over 27 sub-projects and a total of more than 3,700 stars on its repositories [35]. - The community-driven approach invites developers to contribute, thereby expanding the ecosystem and enhancing the technology's value through collaborative efforts [31][32]. - CANN's repositories include a variety of core libraries and tools, providing developers with ready-to-use resources for AI application development [16][36].

Core Insights - The launch of the Ascend 384 super node computing cluster in Shenzhen marks a significant advancement in AI computing capabilities, transitioning from traditional CPU-based systems to a more integrated and efficient architecture [1][2]. Group 1: Project Overview - The Ascend 384 super node is a collaborative project between Huawei and the Shenzhen Longgang District Urban Investment Group, designed to enhance AI computing power [1]. - This project represents a shift in AI computing clusters from "patchwork integration" to "system fusion," addressing issues like high communication latency and low collaboration efficiency [1]. Group 2: Technical Advancements - The new cluster features 384 high-performance NPUs and integrates 192 of the latest high-performance Kunpeng CPUs, achieving a threefold increase in training performance and a fourfold increase in inference performance [1][2]. - The architecture allows any processor within the cluster to access data center resources as if they were local memory, significantly improving overall system performance [1]. Group 3: Strategic Implications - The deployment of the Ascend 384 super node is a key initiative in the Longgang District's strategy to develop a comprehensive AI resource pool, aiming to provide robust support across various sectors such as government, finance, energy, and manufacturing [2]. - The project aligns with Longgang's "All in AI" strategy, which includes initiatives like the CANN open-source strategy and the establishment of a data company to leverage extensive data resources [2]. Group 4: Ecosystem Development - The collaboration between CANN as the AI computing engine and the open-source HarmonyOS as the operating system aims to create a seamless flow of computing resources across multiple devices [3]. - This integrated approach is expected to attract global AI algorithm developers and HarmonyOS application developers to Shenzhen, fostering a collaborative technology ecosystem and accelerating the development of the AI industry in the Greater Bay Area [3].

Core Insights - The core theme of the forum is "Open Drives Innovation," emphasizing the shift from competition to collaboration in the realm of intellectual property and innovation [1][10][12] Group 1: Forum Overview - The sixth Huawei Innovation and Intellectual Property Forum gathered representatives from various international organizations and companies to discuss the role of intellectual property in fostering collaborative innovation [1][2] - Huawei's Chief Legal Officer, Song Liuping, highlighted that the essence of intellectual property is not exclusivity but rather the orderly and efficient dissemination of innovative results [2][4] Group 2: Huawei's Contributions - As of the end of 2024, Huawei has obtained over 150,000 valid patents globally, with R&D investment exceeding 20% of annual revenue, totaling over 1.2 trillion yuan in the past decade [4][7] - The forum showcased Huawei's "Top Ten Inventions" for 2024, which include significant technological advancements such as the Scale Up ultra-large-scale computing platform and the HarmonyOS full-stack architecture [4][6] Group 3: Knowledge Sharing and Collaboration - The upgraded "Chasi Patents" platform was introduced, enhancing patent search and analysis capabilities, thereby accelerating knowledge flow and innovation [6][9] - Huawei's commitment to open innovation is reflected in its extensive participation in global ICT standards and its collaboration with international licensing platforms, generating over $630 million in patent licensing revenue in 2024 [8][9] Group 4: Open Innovation Strategy - Huawei's strategy emphasizes that knowledge sharing enhances social value rather than diminishing rights, promoting a cycle where patent protection leads to commercial returns that fund further R&D [9][10] - The forum underscored the importance of establishing standardized interfaces and shared platforms to make innovation more efficient and inclusive [10][12] Group 5: Future Implications - The discussions at the forum suggest that open innovation is becoming a dominant theme in global technological collaboration, with the potential to significantly enhance cross-industry integration [10][12] - The evolving landscape of technology, including AI and quantum computing, presents challenges in establishing sustainable cooperation mechanisms, highlighting the need for intellectual property to serve as a bridge rather than a barrier [10][12]

Core Viewpoint - The ultimate competition in AI between China and the US may not be about computing power but rather about electricity supply, as AI's increasing demands for power will determine who can sustain their operations longer [5][10]. Group 1: US AI Industry Challenges - The bottleneck for the US AI industry has shifted from chip availability to electricity supply, with major tech companies struggling to secure sufficient power for their operations [9][10]. - AI applications, such as ChatGPT, consume significant amounts of electricity, with estimates indicating that daily operations could power 17,000 American households [11]. - The aging US power grid, built decades ago, is unable to meet the surging electricity demands, leading to potential power shortages in certain regions by 2030 [11][12]. Group 2: China's Energy Advantage - China is leveraging its energy advantages to compensate for product performance disadvantages in the AI sector, particularly through initiatives like "East Data, West Computing" [15][18]. - This initiative aims to transfer data processing from energy-rich western regions to eastern areas where demand is high, optimizing the use of clean and inexpensive electricity [16][18]. - The recent electricity subsidy for AI data centers in China is seen as a strategic move to enhance competitiveness against US firms by reducing operational costs [5][7][25]. Group 3: Cost Structure and Subsidies - The electricity subsidy fundamentally alters the cost structure for companies considering domestic chips, making it more attractive to switch from foreign to local alternatives [25][32]. - The new cost formula for AI operations in China now includes reductions in energy costs due to the "energy scheduling dividend" and fiscal subsidies, making domestic chips more competitive [31][32]. - The subsidy is designed to cover the ecological migration costs associated with transitioning to domestic chips, providing critical time for the development of local ecosystems [32]. Group 4: Strategic Implications - The combination of energy advantages, subsidies, and domestic chip development forms a closed-loop system aimed at challenging Nvidia's dominance in the AI ecosystem [33][40]. - As AI competition evolves from a focus on chip performance to resource management, China's ability to maintain lower operational costs could provide a significant competitive edge [41][45]. - The strategic focus is not only on defense but also on offensive maneuvers against established players like Nvidia, with the goal of redefining the competitive landscape [42][46].

Core Viewpoint - The article discusses the transformation of the AI industry, emphasizing that the competition has shifted from hardware capabilities to a battle for software, developers, and ecosystem building, with Huawei's Ascend and its heterogeneous computing architecture CANN at the forefront of this change [1][4]. Summary by Sections CANN Open Source Announcement - Huawei's rotating chairman Xu Zhijun announced that the CANN hardware enabling will be fully open-sourced by December 30, 2025 [2]. Significance of CANN Open Source - The open-sourcing of CANN represents a profound self-revolution in the domestic AI infrastructure, aiming to break the closed model traditionally dominated by hardware manufacturers and embrace a more open and community-driven future [4][19]. - The success of the ecosystem relies on attracting academic innovation and creating a stable, universal, and efficient foundational tool for developers [5][18]. Developer Perspectives on CANN - Developers describe CANN's evolution as a challenging journey, with early versions requiring low-level programming skills, which hindered productivity [10][11]. - The introduction of the Ascend C programming language marked a significant improvement, aligning more closely with mainstream programming practices [15]. Challenges Faced by Developers - Early developers faced high technical barriers and a lack of stable architecture, leading to a difficult development environment [11][13]. - Systemic issues persisted, such as the inability to reproduce model accuracy across different frameworks due to a lack of transparency in the underlying systems [17]. The Role of Open Source - Open sourcing CANN is seen as a means to break down technical barriers and empower developers by providing transparency and control over the platform [21][23]. - The open-source model aims to foster a vibrant community where developers can contribute and innovate, moving away from reliance on a few official experts [29]. Ecosystem Empowerment - Open source provides unprecedented opportunities for deep integration between academia and industry, allowing researchers to address real-world problems and convert solutions into academic contributions [26]. - The shift from users to contributors is expected to cultivate a new generation of developers who can engage in high-quality projects [28]. Future Outlook for CANN - The current focus is on matching CUDA's capabilities while fostering original innovations within the CANN ecosystem [44]. - Huawei has committed to investing significant resources, including 1,500 petaflops of computing power and 30,000 development boards annually, to support the open-source community [45].

Core Insights - Huawei unexpectedly revealed its future chip roadmap during the Huawei Connect 2025 event, showcasing several new chips including the Ascend 950, 960, and 970 series for AI computing, as well as the Kunpeng 950 and 960 processors for general computing [1][3][10] Group 1: Chip Developments - The Ascend 950 series chips will support low-precision data formats and achieve computing power of 1P and 2P, enhancing training efficiency and inference throughput [3][10] - The Ascend 960 is planned to double the performance of the Ascend 950 and will support Huawei's self-developed HiF4 data format, set to launch in Q4 2027 [7] - The Ascend 970 will further enhance specifications compared to the Ascend 960, with plans for release in Q4 2028 [7] Group 2: Supernode and Cluster Innovations - Huawei introduced the Atlas 950 supernode, which will consist of 8192 Ascend 950DT chips, achieving FP8 computing power of 8E FLOPS and FP4 computing power of 16E FLOPS, set to launch in Q4 2026 [11][13] - The Atlas 960 supernode, planned for Q4 2027, will be based on 15488 Ascend 960 chips, with FP8 computing power reaching 30E FLOPS and FP4 computing power reaching 60E FLOPS [13] - The Atlas 950 SuperCluster will consist of 64 Atlas 950 supernodes, achieving FP8 computing power of 524 EFLOPS, making it the world's strongest computing cluster [18] Group 3: Software and Ecosystem Development - Huawei aims to develop a robust software ecosystem to complement its hardware, with the CANN deep learning framework and MindSpore framework serving as alternatives to NVIDIA's CUDA [21][22] - The company plans to open-source its CANN compiler and virtual instruction set interface by the end of 2025, along with the Mind series application tools [22][24] - Huawei's strategy emphasizes hardware evolution through existing chip technology while fostering an open-source ecosystem to address challenges posed by U.S. sanctions [24]

Core Insights - The conference highlighted advancements in AI capabilities, particularly through the launch of the Ascend Super Node and CANN ecosystem, which aims to enhance computational power and efficiency in AI applications [1] Group 1: AI City Solutions - Longgang District introduced the AI CITY benchmark solution, leveraging Ascend computing power to integrate government and public service data, focusing on urban management, public services, and industrial empowerment [1] - Traffic efficiency on main roads improved by 18%, and the accuracy of AI-assisted diagnoses in community hospitals exceeded 92% [1] - The "4T Digital Living Space" initiative centers on four digital rights, providing T-level storage, network, public resources, and intelligent computing support, transitioning from "urban governance" to "citizen services" [1] Group 2: Technological Advancements - The Ascend Super Node has overcome traditional cluster communication bottlenecks, achieving training performance three times that of conventional nodes, supporting the training of trillion-parameter models [1] - CANN has been fully open-sourced, promoting deep adaptation of AI frameworks and significantly enhancing development efficiency through automatic operator generation technology [1] - The plan to cultivate 2 million Ascend developers over the next two years aims to strengthen the talent foundation in the industry [1] Group 3: Collaboration and Applications - The conference also announced the second batch of "City + AI" application scenarios in Longgang District, covering 21 fields with a total of 424 scenarios [1] - Longgang Urban Investment signed a cooperation agreement with Huawei to jointly build a local and cloud-based computing resource pool [1]

Core Insights - The article discusses the implementation of AI technology in Shenzhen's Longgang District, focusing on enhancing digital governance and improving residents' quality of life through AI-driven solutions [3][6][13] Group 1: AI CITY Initiative - The Longgang District has launched the AI CITY benchmark solution, utilizing Ascend computing power to integrate government data and public services with citizen needs [3][6] - The "4T for Home" project aims to create a comprehensive digital living space, covering various aspects of digital life, including governance, healthcare, and local services [3][10] Group 2: AI Applications in Governance - Longgang has developed an AI application system termed "1+1+4+N," which includes a government AI platform and multiple application scenarios across key areas such as citizen services and urban governance [6][12] - The introduction of the "Super Pod" concept enhances computational capabilities, significantly improving communication bandwidth by 15 times and reducing latency by 10 times, thus addressing the computational demands of AI applications in governance [7][12] Group 3: Digital Rights and Resources - The initiative aims to ensure digital equity by providing citizens with T-level storage, network traffic, public resources, and access to advanced AI models, thereby safeguarding their digital rights [9][10] - The government is committed to building a "digital home" for residents, ensuring their survival, communication, development, and wisdom rights in the digital realm [9][10] Group 4: Expanding AI Ecosystem - Longgang has released a second batch of "city + AI" application scenarios, covering 21 key areas and 424 specific scenarios, promoting the integration of AI into urban governance and public services [11][12] - The district plans to invest 10 billion in government orders over three years to support the application of new technologies and products, fostering a robust AI ecosystem [12][13]