国产 AI 芯片厂商竞争格局、产品力与市场情况 20251123 摘要 国产 AI 芯片在部分模型性能上可达英伟达 A100 的 50%-100%，推理 侧华为、寒武纪、昆仑芯达 H100 的 50%-70%，但尚无芯片达到 H100 水平。受美国限制，国产芯片依赖海外产能，少数如华为可获国 内 7 纳米（N+2）产能，多数厂商仅能获得 12 纳米（N+1）制程。 国内 AI 芯片厂商计划在 2026 年 Q1-Q3 量产新一代全国产 12 纳米 （N+1）制程芯片，华为、寒武纪、海光预计上半年实现 7 纳米 （N+2）制程量产，但这些产品多为上一代加强版。华为和寒武纪已开 发出基于光电直连的新型互联方式，部分超越英伟达 NVLink 技术。 二三线 AI 芯片设计公司采用迷你超节点技术，通过定制高速网卡连接 32-64 张 GPU 卡以提高性能。百度昆仑已完成万卡集群调试，并开发 类似平头哥超节点系统，通过与上下游合作提升竞争力。 2025 年 Smith N+2 芯片月产量 4,000-5,000 片晶圆，良率接近 40%，预计 2026 年产量增至每月超 1 万片，但不超 1.5 万片，良率上 限 50 ...

t 1 1 t personal in 0 g t the start a All 其中，从摩尔线程IPO申请获得受理，到摩尔线程IPO顺利过会，仅用了88天时间。从IPO顺利过会，到获准注册，也只用了一个月的时间。换言之，摩尔线 程从受理到获准注册，整个IPO过程只用了122天时间。 122天走完了整个IPO流程，创造出A股IPO的时间纪录。对摩尔线程而言，这一次成功IPO，为企业的高速发展提供了极大的帮助。 2022年，摩尔线程的营收还不到5000万元，但在2023年之后，摩尔线程却开始加速发展。在营收飙升的同时，摩尔线程的研发投入也在水涨船高，甚至出现 了研发大于收入的情况。 在公司加大研发投入的背后，仅依靠公司现有的现金流，恐怕无法满足公司高速发展的需求。因此，摩尔线程的成功上市，极大解决了公司的资金问题。 作为国产GPU第一股的摩尔线程，即将登陆科创板市场。从公开数据显示，摩尔线程的发行价格为每股114.28元，募资80亿元，预计上市时的股票市值约为 537.15亿元人民币。 截至目前，寒武纪的总市值为5267亿元人民币，与摩尔线程的市值差距接近10倍。摩尔线程上市首日，会否遭到市场资金的炒作？甚至 ...

Group 1 - The core viewpoint of the articles indicates that the technology sector is expected to attract incremental capital, with 16 technology-themed funds approved on November 21, signaling strong investor interest in this area [1][5][8] - The approved funds include the first batch of science and technology entrepreneurship artificial intelligence ETFs from seven fund companies, reflecting a focus on companies involved in AI [2][4] - The issuance of these funds is anticipated to bring at least 30 billion yuan in new capital, as the majority of the funds are not initiated funds, with only one being an initiated fund [5][6] Group 2 - The recent trend shows a significant recovery in new fund issuances, with 73 equity funds established in November alone, averaging around 600 million yuan in issuance size [6][8] - There is a notable demand for technology-themed funds, with some experiencing oversubscription, such as the E Fund Technology Pioneer Mixed Fund, which had effective subscription applications exceeding its 2 billion yuan cap [6][8] - Institutional investors remain optimistic about the technology sector, despite recent market volatility, with continued inflows into AI-themed ETFs, indicating a belief in the long-term growth potential of the AI industry [9][10]

Core Viewpoint - The current period may represent a strategic entry point for technology stocks, particularly those with strong profitability, as the technology bull market continues [1] Group 1: Investment Opportunities - Among stocks with ratings from 10 or more institutions and a consensus forecast of over 30% net profit growth in the next two years, 21 stocks have seen a price correction of over 20% from their yearly highs, while maintaining a net asset return rate exceeding 8% in their Q3 reports [1] - The identified 21 stocks have a high "technology content," with 14 of them projected to have R&D expenditures exceeding 100 million yuan in 2024, and companies like Haiguang Information, Shennan Circuit, Shengyi Technology, and Cambricon-U expected to spend over 1 billion yuan on R&D in 2024 [1] Group 2: Profitability and Growth - From a profitability perspective, Shenghong Technology reported an average net asset return rate of nearly 27% after deducting non-recurring items in the first three quarters of this year, leading the group [1] - Other companies such as Shijia Photon, Cambricon-U, and Yingshi Innovation also reported net asset return rates exceeding 15% in the same period [1] - In terms of growth potential, institutions predict that Cambricon-U, Huafeng Technology, and Taicheng Light will achieve net profit growth rates exceeding 40% in the next two years [1]

Core Insights - The first batch of seven AI-focused ETFs has been approved, tracking the newly established Zhongzheng Sci-Tech Entrepreneurship AI Index, which will include 50 companies involved in AI-related sectors [1] - The AI-related ETFs primarily track four major indices, each offering differentiated tools for investing in the AI industry chain [1] - The Zhongzheng Sci-Tech Entrepreneurship AI Index combines companies from both the Sci-Tech and Growth Enterprise markets, enhancing exposure to both communication and semiconductor sectors [1] Index Performance - As of November 21, the Zhongzheng Sci-Tech Entrepreneurship AI Index has seen a remarkable increase of 72.04% since the beginning of the year, outperforming other AI indices [3] - The index also recorded the lowest maximum drawdown compared to its peers, indicating strong resilience [3] Market Context - Recent market fluctuations have raised concerns among investors, primarily due to liquidity worries stemming from the Federal Reserve's anticipated interest rate decisions and fears of an AI bubble [5] - Despite these concerns, the Chinese market does not exhibit signs of excessive AI capital expenditure, contrasting with the situation in the U.S. [5] - The upcoming economic work conference in December is expected to provide further policy guidance, which could positively influence market sentiment [5] Investment Opportunities - Penghua Fund has been approved for two new products focusing on the Sci-Tech Entrepreneurship AI and Sci-Tech Chip Design themes, expanding the range of investment tools available for investors in the tech sector [5] - This expansion aims to facilitate efficient investment in high-tech sectors and direct more capital towards companies with core technologies that drive industrial upgrades [5]

Core Insights - The AI computing industry is transitioning into an "application dividend period," driven by the integration of artificial intelligence and high-performance computing, with a projected global market size of $234 billion in 2024 and expected to exceed $2.75 trillion by 2032, reflecting a compound annual growth rate of 36.8% [5][6][10] AI Computing Industry Overview - AI computing (Intelligent Computing) combines AI with high-performance computing, utilizing dedicated hardware and distributed architecture to provide efficient and scalable computing power for AI tasks [2][3] - The industry is characterized by a three-pronged model of "computing power + algorithms + data," which supports complex AI model training and inference, making it a critical infrastructure for the digital economy [2][3] Global and Chinese AI Computing Development - The global AI market is evolving from a "model dividend period" to an "application dividend period," becoming a core engine for the digital economy, with significant advancements in computing power architecture [5][6] - In 2024, the total global computing power is expected to reach 2207 EFLOPS, with intelligent computing power contributing 1610 EFLOPS, marking a year-on-year growth of 63.8% [5][6] - China's total computing power is projected to reach 280 EFLOPS in 2024, with intelligent computing power at 90 EFLOPS, indicating a significant gap between supply and future demand [6][10] AI Computing Industry Chain in China - The AI computing industry chain in China consists of upstream hardware (GPUs, NPUs), midstream service providers (telecom operators, cloud service providers), and downstream application sectors (internet, finance, manufacturing) [8][10] - The market for AI acceleration chips is expected to grow from 17.56 billion yuan in 2020 to 142.54 billion yuan in 2024, with a compound annual growth rate of 68.8% [9][10] Competitive Landscape - Major players in the infrastructure layer include Huawei, which leads in domestic chip production, and Inspur, which dominates the AI server market [10] - Telecom operators and cloud service providers, such as China Telecom, Alibaba Cloud, and Tencent Cloud, are key players in the computing service layer [10] Future Trends in AI Computing Industry - The industry is expected to evolve towards collaborative evolution, with a focus on efficient resource utilization and deep integration with the real economy [11][12] - The emphasis will shift from model performance to creating measurable business value through large-scale applications in key sectors such as manufacturing, finance, and healthcare [13]

Core Insights - Nvidia's latest earnings report has created significant market volatility, with a notable stock price fluctuation following the announcement, reflecting concerns about future growth sustainability [1][3] - The tech sector's downturn in the US has impacted the A-share market, leading to a decline in related sectors such as computing power and AI chips [4] Market Trends - The computing power sector is experiencing a reevaluation of valuations and fundamentals, with a potential short-term profit-taking pressure as year-end approaches [4] - Major tech companies like Microsoft, Google, Meta, and Amazon are increasing capital expenditures, indicating sustained demand in the computing power supply chain [4][6] Domestic Developments - The domestic computing power ecosystem is rapidly evolving, with a significant shift towards self-sufficiency in chip supply, as evidenced by the projected decrease in reliance on foreign chips from 63% to 42% by 2025 [7] - Domestic chip manufacturers are showing substantial revenue growth, with companies like Cambricon reporting a 2386.38% increase in revenue year-on-year [7] Technological Advancements - The focus in the computing power industry is shifting from hardware accumulation to efficiency optimization, with significant reductions in inference costs for AI models [9][10] - Innovations in system architecture, such as the adoption of supernode technology, are enabling better performance despite limitations in chip capabilities [10] Industry Opportunities - The AI computing power sector is becoming a primary growth engine for the communications industry, with investment opportunities emerging in areas like optical modules and storage chips [13][14] - The liquid cooling technology market is expected to grow significantly, with a projected compound annual growth rate of 46.8% from 2024 to 2029 [10][14] Investment Shifts - Investment in the computing power industry is transitioning from infrastructure to application innovation, reflecting a maturation of the sector [14] - Companies that can accurately navigate technological trends are likely to gain a competitive advantage during the upcoming industrial transformation [14]

Group 1 - China and the U.S. should lead bilateral economic and trade relations based on the consensus reached during the recent meeting between the two countries' leaders [5] - The U.S. stock market saw all three major indices rise, with the Dow Jones up 1.08%, S&P 500 up 0.98%, and Nasdaq up 0.88% [5] - Bitcoin has experienced a significant decline, currently priced at $84,626, down over 2% and more than 22% for the month, marking its worst performance since 2022 [6] - International oil prices fell, with WTI crude down 1.73% to $57.98 per barrel, and Brent crude down 1.42% to $62.48 per barrel [7] - Gold prices decreased by 0.3%, settling at $4,064.28 per ounce, while COMEX silver futures fell by 1.27% to $49.66 per ounce [8] Group 2 - China's Ministry of Commerce reported that from January to October, the country attracted foreign investment of 621.93 billion yuan, a year-on-year decrease of 10.3% [12] - The first batch of AI-focused ETFs and several chip-related ETFs have been approved, expected to attract new capital into the market [14] - Eli Lilly became the first healthcare company to reach a market capitalization of $1 trillion, with a stock price increase of over 38% this year [26][27] - Xiaopeng Motors has officially launched its first land carrier prototype, marking a significant step in the production of its flying car [28]

Core Insights - The report titled "2025 Domestic AI Chip Software Ecosystem White Paper" highlights the competitive landscape of domestic AI chips, emphasizing the shift in user focus from hardware performance to the maturity, compatibility, and usability of the software ecosystem [1][10] - The software ecosystem is structured into four layers: foundational support layer, core tools layer, framework adaptation layer, and management monitoring layer, which collectively determine the value realization and commercialization of AI chips [1][14] - Domestic AI chips are categorized into specialized acceleration chips, general-purpose computing chips, and graphics computing chips, with notable manufacturers focusing on different aspects of the ecosystem [1][10] Group 1: Background and Significance - The report outlines the context of increasing international technological competition, which has led to significant advancements in domestic AI chip technology and market expansion [10][11] - The primary goal of the white paper is to systematically assess the development status of the domestic AI chip software ecosystem, providing a technical reference for industry, academia, and government [12][13] Group 2: AI Chip Software Ecosystem Composition - The AI chip software ecosystem serves as a technical hub connecting hardware capabilities with upper-layer applications, structured into four main modules: foundational support, core tools, framework adaptation, and management monitoring [14][20] - Each module plays a critical role in the training and inference processes of AI models, with the foundational support layer abstracting hardware complexity and providing standard interfaces for upper-layer tools and applications [17][19] Group 3: Current State of Domestic AI Chip Software Ecosystem - The report identifies various domestic AI chip categories and representative manufacturers, highlighting their unique software ecosystem resources and the need for improvement in completeness and community engagement [3][10] - The current ecosystem has progressed from "basic usability" to "specific scenario usability," with two main paths emerging: "full-stack ecosystem" and "compatible ecosystem" [1][10] Group 4: Future Development Directions - The report emphasizes the need for standardization, open-source collaboration, and industry-academia synergy to enhance the domestic AI chip software ecosystem from "usable" to "excellent" [1][10] - The ultimate goal is to establish a robust and controllable technological system that supports the commercialization of domestic AI chips [1][10]

Core Insights - The report highlights the rapid growth and potential of the Chinese smart chip industry, driven by advancements in artificial intelligence, IoT, and 5G technology, with a projected market size exceeding 600 billion yuan by 2030 [1][2][3] Industry Overview and Background Analysis - Smart chips are defined as highly integrated circuits capable of perception, computation, and decision-making, integrating AI algorithms and big data analysis to respond to complex environments [8][9] - The smart chip industry in China has evolved from reliance on imports to developing domestic capabilities, with significant government support and technological breakthroughs in recent years [19][23][24] Market Size and Growth Trends - The Chinese smart chip market has experienced a compound annual growth rate (CAGR) of over 20% from 2018 to 2022, driven by demand in consumer electronics, smartphones, and IoT applications [33][35] - The AI chip segment is the largest, accounting for over 40% of the market, with rapid growth in autonomous driving and edge computing chips [36][41] Technological Development and Innovation Trends - Key technologies in smart chips include Neural Processing Units (NPUs) optimized for AI tasks, edge computing for real-time data processing, and innovations in high-performance computing architectures [51][54][57] - The report emphasizes the importance of hardware-software co-design and the integration of advanced semiconductor processes to enhance performance and reduce power consumption [52][58]