Core Insights - The year 2025 is anticipated to be a pivotal year for the deployment of large AI models, with integrated machines serving as a crucial component for enterprises to implement these applications effectively [1][2] - The demand for AI integrated machines is driven by both market needs and technological advancements, providing a complete solution that lowers the barriers for enterprises to adopt AI [1][2] Industry Trends - AI integrated machines are designed to facilitate the application and deployment of large AI models, combining hardware, software, and algorithms into a single device, making them more accessible for small and medium enterprises [2][4] - The trend indicates that AI integrated machines will evolve towards higher efficiency, lower energy consumption, and easier deployment, with specialized products tailored to various industry needs [1][2] Market Demand - The energy sector is experiencing a surge in demand for AI applications, with integrated machines like the Langxin Jiugong AI Energy Integrated Machine being introduced to address challenges such as computational power shortages and complex model deployments [2][3] - The deployment of AI integrated machines is expected to grow significantly, with projected demand reaching 150,000 units in 2025, 390,000 units in 2026, and 720,000 units in 2027, indicating a substantial market opportunity [4] Technological Advancements - The Langxin Jiugong AI Energy Integrated Machine features a self-developed AI model that integrates time-series prediction and AI agent technologies, offering nine core functionalities for optimizing energy management [2][3] - The introduction of integrated machines is seen as a solution to the challenges of computational power supply and the need for a reliable, controllable technology base in critical sectors like energy [3][5] Policy Support - Local governments are increasing support for AI applications, with initiatives such as issuing computing power vouchers and subsidies for renting intelligent computing resources to stimulate the AI ecosystem [6][7] - The Shanghai Municipal Economic and Information Commission has introduced measures to lower the cost of intelligent computing and support the development of AI models, indicating a strong governmental push towards AI integration [6][7] National Strategy - The push for a fully domestic AI industry chain is viewed as essential for national security, with a focus on developing self-sufficient technologies in critical areas such as chips and algorithms [5][8] - The industry is encouraged to balance core technology innovation with open collaboration to avoid dependency on foreign technologies while participating in international standards [8]

Investment Rating - The report does not explicitly state an investment rating for the industry or specific companies. Core Insights - The 2025 World Artificial Intelligence Conference (WAIC) highlighted a transformative evolution in China's AI computing industry, emphasizing "full-stack autonomy, deepened scenario integration, and diversified ecosystems," marking a shift from technological catch-up to ecological leadership [10][11][12] - The focus has shifted from "parameter competition" to integrating computing capabilities into specific industry solutions, reflecting industry maturity and confidence [11][12] - Chinese computing firms are actively constructing a fully autonomous ecosystem, addressing external technological constraints and supply chain risks [12][13] Summary by Sections Event Overview - The WAIC 2025 showcased a collective transformation in China's AI computing industry, with leading firms like Huawei, Sugon, and Moore Threads presenting competitive foundational technologies and a complete value loop from computational infrastructure to industry applications [10][11] Industry Trends - A notable trend is the acceleration of "end-to-end domesticization," with firms developing a comprehensive ecosystem from chip architecture to software services [12] - The industry is characterized by a diversified development strategy, with various companies pursuing different technological paths, such as Huawei's "384 Super Node" and Zhonghao Xinying's TPU chips, which improve energy efficiency [13] Company Highlights - Huawei's Ascend is compatible with over 80 large-scale AI models, integrating deeply into sectors like telecom, finance, and healthcare [11] - Sugon showcased its "Five Smart" system, demonstrating extensive delivery capabilities across various AI solutions [12] - New products like the Xiyun C600 GPU and the M50 AI chip from Houmo Intelligence reflect the industry's focus on localized and low-power AI solutions [13]

Core Viewpoint - The World Artificial Intelligence Conference (WAIC) highlights the importance of embodied intelligence and the underlying computing infrastructure, which includes chips, boards, servers, and computing clusters, as a critical topic for the AI industry [2][5]. Group 1: Computing Infrastructure Transformation - This year's WAIC showcased a shift from a focus on "parameter competition" to practical discussions on "fragmented computing resource coordination," "low power and low cost," and "vertical product integration" [5]. - The trend of "full-chain localization" in computing infrastructure is gaining attention, driven by global supply chain disruptions and the need for self-sufficiency in core technologies [7][8]. - Domestic computing infrastructure manufacturers are expanding localization efforts beyond individual chips to encompass architecture design, software and hardware ecosystems, and industry implementation [9]. Group 2: Notable Companies and Innovations - Muxi, a representative of this localization trend, showcased its latest GPU, the Xiyun C600, which features a self-developed architecture and is designed for cloud AI training and inference [10]. - The Xiyun C600 is equipped with HBM3e memory, significantly enhancing memory bandwidth for large model training and inference [12]. - The previous generation Xiyun C500 series has already achieved full-chain localization in its server solutions, including compilers and interconnect protocols [13]. Group 3: High-Performance Computing Alternatives - Zhonghao Xinying presented its "Shan" series TPU, which utilizes controllable IP cores and a self-developed instruction set, achieving a 30% reduction in energy consumption for the same AI computing tasks [15]. - The "Taize" computing cluster system, based on the "Shan" TPU, can support over 400P (TF32) of floating-point operations, suitable for large AI model computations [17]. Group 4: Industry Applications and Collaborations - Huawei's "384 Super Node" was unveiled, showcasing its capability to interconnect 384 cards for large model adaptation, with over 80 models already developed [20]. - The collaboration between Huawei and partners spans various industries, including finance, healthcare, and transportation, demonstrating the practical applications of their computing solutions [20]. - Moore Threads presented 12 industry-specific demos, including a video super-resolution technology that enhances low-resolution video quality, showcasing the versatility of their solutions [21][22].

Core Insights - The World Artificial Intelligence Conference (WAIC) in Shanghai highlighted the growing importance of embodied intelligence, showcasing robots capable of physical interaction and complex tasks [1] - The focus has shifted from performance parameters to practical applications and solutions in the computing infrastructure sector, indicating a more mature industry approach [3][4] Group 1: Computing Infrastructure Trends - The computing infrastructure exhibition at WAIC displayed a significant change, moving away from the "parameter competition" seen in previous years [3] - Technical specifications are now integrated into industry solutions rather than being prominently displayed, reflecting a more application-oriented mindset [4] Group 2: Domestic Production and Self-Sufficiency - The push for "full-link domestic production" in computing infrastructure is gaining momentum, driven by global supply chain disruptions and technology challenges [5] - Domestic manufacturers are expanding their focus from individual chips to a comprehensive self-sufficient ecosystem, including architecture design and software integration [5] Group 3: Notable Companies and Innovations - Muxi, a representative company, showcased its latest GPU, the Xiyun C600, which features a self-developed architecture aimed at AI training and inference tasks [6] - The Xiyun C600 is equipped with HBM3e memory, enhancing bandwidth for large model training, although it was not yet available in server form at the exhibition [10] - Zhonghao Xinying presented its "Shan" TPU series, which boasts a fully controllable IP core and a design that reduces energy consumption by 30% for AI tasks [12] Group 4: Industry Applications and Collaborations - Huawei's "384 Super Node" technology was unveiled, showcasing its capability to support over 80 large models across various sectors, including finance and healthcare [18][19] - The collaboration between Huawei and partners aims to create solutions across multiple industries, enhancing the adaptability of their computing infrastructure [19] - Moore Threads demonstrated its video super-resolution technology, which significantly improves video quality and can be integrated into various applications, stimulating industry collaboration [20][23]