科技牛带动指数突破3800点后，工信部发声支持GPU，国产GPU六龙争霸！ 1.摩尔线程——逐光飞龙 全功能 GPU，MTT S80 跑 3A 大作，驱动月更；S2000 智算集群推动 OISA 标准，Pre-IPO 估值 255 亿。 2.景嘉微——霸权陆龙 全市场唯一一个cpu，gpu（dpu)双龙头，也是唯一一个形成生态的通用芯片公司，产品包括海光7000 CPU和深算一号/二号GPU（DCU协处理器），广泛应用于服务器、金融、互联网及AI领域，非常具有 有稀缺性。 "黄埔军校"，JM9 接近 GTX1050，党政/金融国产替代占比40%，JM11 瞄准通用计算，堪称国芯基石。 虽然国产GPU短期很难赶上英伟达，AMD，英特尔等，毕竟发展这么多年，多少人想进去分一杯羹， 国外也就只有英伟达跟AMD，连英特尔想做都进不去，国内初创的几家，10个亿几百人两三年的量级 研发肯定还是有巨大差距的，但是就像家电、汽车、手机等行业一样，10年或者20年后还是有可能的， 我觉得人工智能很像当初互联网行业，会深刻改变人类的生活。会有一大批的高增长的企业。而且时间 会非常的长。在微软刚上市时买入，或者也可以等互联网泡沫破 ...

Group 1 - The core viewpoint is that China's computing power infrastructure is rapidly advancing, positioning the country as a global leader in digital infrastructure by mid-2025, with a total computing power scale ranking second worldwide [1] - As of June 2023, China has established 4.55 million 5G base stations and has 226 million gigabit broadband users, indicating significant growth in digital infrastructure [1] - The development of artificial intelligence is heavily reliant on computing power, which is considered a fundamental "energy" for AI advancements [3] Group 2 - Computing power is categorized into supercomputing, general computing, and intelligent computing, with intelligent computing being crucial for the iterative development of AI technologies [4] - China's total computing power is projected to reach 280 EFLOPS by the end of 2024, with intelligent computing expected to grow over 40% by 2025, reaching 90 EFLOPS, which constitutes 32% of the total [5] Group 3 - Recent advancements in hardware and software capabilities have been highlighted, including Huawei's "Ascend 384 Super Node" and the introduction of competitive domestic GPU products, indicating a robust ecosystem for computing power [6] - The integration of the integrated circuit industry has progressed, creating a complete supply chain from design to manufacturing, enhancing the usability of domestic computing power [6] Group 4 - The energy consumption of data centers is a significant concern, with predictions indicating that by 2030, China's data center energy consumption will exceed 400 billion kilowatt-hours [7] - Various regions are implementing energy-efficient practices, such as using liquid cooling technology and renewable energy sources, to enhance the energy efficiency of computing power centers [8] Group 5 - By the end of 2024, over 9 million standard racks will be in use across data centers in China, with an average Power Usage Effectiveness (PUE) of 1.46, showcasing improvements in energy efficiency [8] - The overall outlook for China's computing power infrastructure is positive, with rapid market growth, expanding demand scenarios, and accelerated technological advancements, which are essential for establishing a competitive advantage in the AI era [8]

Core Viewpoint - The article highlights the significant performance improvement of HanGuangJi, a leading AI chip company in China, with its stock price nearing that of Kweichow Moutai, indicating strong market interest and growth potential in the AI chip sector [2]. Group 1: Company Performance - HanGuangJi's stock reached 1243.20 yuan, with a trading volume exceeding 15.8 billion yuan, reflecting robust market activity [2]. - The company reported a revenue of 9.89 billion yuan and a net profit of 2.81 billion yuan in Q4 of the previous year, marking its first quarterly profit since going public [2]. - In Q1 of this year, HanGuangJi's revenue surged to 11.11 billion yuan, a significant increase from 0.26 billion yuan in the same period last year, with a net profit of 3.55 billion yuan [2]. Group 2: Industry Developments - DeepSeek recently launched DeepSeek-V3.1, which is designed for the upcoming generation of domestic chips, marking a step towards the agent era in AI [2]. - Several AI chip manufacturers, including Moole Technology and Suiyuan Technology, have introduced chips that support FP8 precision computing, enhancing performance in large model training by 20% to 30% [3][4]. - The domestic AI industry is increasingly utilizing local computing power, with companies like iFLYTEK testing large models based on fully domestic computing resources [4]. Group 3: Market Outlook - Research reports from various brokerages indicate a stable growth in the semiconductor supply chain, with rising wafer foundry capacity and a positive outlook for the semiconductor market [5]. - The introduction of new precision formats by DeepSeek suggests a growing application of domestic AI chips in training and inference processes, especially in light of international supply chain fluctuations [5].

克雷西 一水 发自 凹非寺 量子位 | 公众号 QbitAI DeepSeek V3.1发布后，一则官方留言让整个AI圈都轰动了： 新的架构、下一代国产芯片，总共短短不到20个字，却蕴含了巨大信息量。 国产芯片企业股价也跟风上涨，比如寒武纪今日早盘盘中大涨近14%，总市值跃居科创板头名。 半导体ETF，同样也是在半天的时间里大涨5.89%。 （不知道作为放出消息的DeepSeek背后公司幻方量化，有没有趁机炒一波【手动狗 头】） | Cambricon | 其武红 | + | | | | | | | | | | | 每日Ali "股票i | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | | SH 688256 ■ Level1基础行情 ■ 上海交易所 ■ 沪港通标的股票 ■ 科创板 ■ 融资融券标的 | | | | | | | | | | 所属行业 × 半导体 +2.68% > | | | | | | | | | | | | | | | 1164.45元 +128. ...

Group 1 - Multiple stocks in the computing power sector experienced significant gains, with SMIC rising 6.29% and Chipone increasing by 5.39% [1] - AI-related stocks saw even larger increases, with Haiguang Information up 17.19%, Zhongke Shuguang up 10%, and Cambricon up 12.4%, reaching a new high of over 1170 CNY per share and a market capitalization exceeding 490 billion CNY [1][3] Group 2 - Cambricon, a leading domestic AI chip company, reported improved performance, achieving a revenue of 989 million CNY and a net profit of 281 million CNY in Q4 of last year, marking its first quarterly profit since going public [3] - In Q1 of this year, Cambricon's revenue surged to 1.111 billion CNY, a significant increase from 26 million CNY in the same period last year, with a net profit of 355 million CNY [3] Group 3 - DeepSeek recently launched DeepSeek-V3.1, which is designed for the upcoming generation of domestic chips, utilizing UE8M0 FP8 precision [3][4] - Several domestic AI chip manufacturers are supporting FP8 precision calculations, with companies like Moore Threads and Suiyuan Technology introducing chips that enhance performance in large model training by 20% to 30% [4] Group 4 - Recent reports from various brokerages highlight the stability and growth of the semiconductor supply chain, with rising wafer foundry capacity and a continued favorable semiconductor market [5] - The potential for increased application of domestic AI chips in training and inference based on the DeepSeek model was noted, especially in light of international supply chain fluctuations [5]

Group 1: AI Industry Development - China's AI industry is currently in the "scale-up" phase, with a market size expected to exceed 1 trillion yuan by 2030, contributing approximately 10% to GDP over the next decade[2][46] - The AI+ industry is projected to achieve a compound annual growth rate (CAGR) of over 15%[2][46] - By the end of 2024, the AI industry in China is expected to surpass 700 billion yuan, maintaining a growth rate of over 20% annually[11] Group 2: Industrial and Consumer Integration - China's industrial base provides rich scenarios for AI applications, with the country expected to account for 45% of global industrial output by 2030[28][33] - The AI consumer hardware market is projected to exceed 1.17 trillion yuan in 2024, with a growth rate of approximately 10%, significantly outpacing the overall consumption growth rate of 3.4%[39] - AI applications are expected to expand from traditional industrial and consumer sectors to deeper integration across various industries[46][50] Group 3: Key Growth Areas - The digital native sector, represented by large internet companies, is poised for rapid growth due to its established data infrastructure and user base[51] - High-penetration industries such as finance, healthcare, and transportation are expected to see accelerated AI adoption, with 88% of financial institutions in the U.S. already deploying AI[51] - Industrial AI tools and platforms are anticipated to evolve, requiring deep integration of algorithms with industry knowledge to achieve comprehensive autonomy[52] Group 4: Risks and Challenges - Potential risks include slower-than-expected policy implementation, volatility in financial markets, and uncertainties in AI technology iterations[2][53]

Group 1: AI Industry Development - China's AI industry has entered a "scale-up" phase, with a market size expected to exceed 700 billion RMB by the end of 2024, maintaining a growth rate of over 20% annually[7] - The "AI+" action plan emphasizes three main paths: open scene leadership, solidifying industrial foundations, and maintaining safety defenses[4] - The AI consumer hardware market is projected to surpass 1.17 trillion RMB in 2024, with a growth rate of approximately 10%[34] Group 2: Industrial and Consumer Applications - China's industrial robot installations reached 276,300 units in 2023, accounting for 51% of global new installations, with a projected annual growth rate of over 15%[30] - The AI application landscape is expanding in both industrial and consumer sectors, with significant opportunities in digital-native fields and high-penetration industries like finance and healthcare[42] - By 2030, the "AI+" industry is expected to achieve a compound annual growth rate (CAGR) of 15%, contributing approximately 10% to China's GDP growth over the next decade[40] Group 3: Data and Infrastructure - China's data volume is projected to grow from 51.78 ZB in 2025 to 136.12 ZB by 2029, with a CAGR of 26.9%[15] - The AI infrastructure, including computing power and data processing capabilities, is continuously improving, with domestic AI chip performance rapidly catching up to international standards[13] - The number of AI companies in China is over 4,500, covering critical areas such as chips, algorithms, and applications[7]

Core Insights - Huawei's Ascend 384 supernode has made its debut, achieving a computing power of 300 Pflops through self-developed Matrix-Link technology, positioning it as a competitor to NVIDIA's latest GB200 NVL72 [1] - Domestic AI chip manufacturers are collaborating to build a robust ecosystem, with significant advancements showcased at the World Artificial Intelligence Conference [2] - The performance of domestic AI chip companies has surged, with notable revenue increases reported across the sector [2][3] Group 1: Huawei and Its Innovations - Huawei's Ascend 384 supernode has been recognized as a "treasure of the exhibition" at the World Artificial Intelligence Conference, attracting significant attention [1] - The supernode's architecture allows 384 cards to operate as a single computer, showcasing its advanced capabilities [1] - The deployment of Huawei's technology is already in multiple data centers across the country, providing services to various sectors including internet and automotive [2] Group 2: Domestic AI Chip Developments - Companies like Nucypher and Suirun Technology are developing advanced chips, with the C600 model set for small-scale production in Q4 of this year [1] - The domestic AI chip industry is experiencing rapid growth, with companies like Moore Threads reporting a 256% increase in revenue, driven by AI computing business [2] - Cambrian Technology has turned a profit for the first time in years, with a 40-fold increase in revenue and over 2-fold profit growth in Q1 of this year [3] Group 3: Industry Trends and Collaborations - The domestic AI chip industry is moving towards a trend of adopting local chips, with significant collaborations among companies like Xizhi Technology and ZTE [2] - The introduction of light-interconnected GPU supernodes is expected to reduce power consumption and latency significantly [2] - The overall performance of domestic AI chips has led to a substantial increase in market presence, indicating a shift away from reliance on foreign technology [3]

Group 1: Core Insights - The emergence of Huawei's Ascend 384 Super Pod signifies a potential shift towards domestic computing power in China, indicating that the era of domestic computing capabilities may have arrived [1][2] - The breakthroughs in single-chip computing power and large-scale cluster technology have been achieved, with notable advancements from companies like Huawei and Muxi [2][3] Group 2: Domestic Computing Power Developments - Huawei's Ascend 384 Super Pod features a total computing power of 300 PFlops, surpassing NVIDIA's similar system, which has a total computing power of 180 PFlops, making Huawei's performance 1.7 times greater [3] - The Ascend 384 Super Pod consists of 12 computing cabinets and 4 bus cabinets, achieving the industry's largest scale of 384-card high-speed bus interconnection, enhancing data transmission efficiency [2][3] - Other companies, such as Muxi and Hengwei Technology, are also making significant strides in the domestic computing power sector, with Muxi's new GPU product and Hengwei's TPU architecture [4][5] Group 3: International Expansion of Domestic Computing Power - The Chinese government emphasizes that artificial intelligence can be an international public good, promoting technology sharing to bridge global intelligence gaps, particularly for developing countries [8] - Companies like Feiteng are actively pursuing international markets, with plans to embed domestic chips into overseas infrastructure projects [7][8] Group 4: Advantages of Domestic Computing Power Going Global - The full-stack solution capability of Chinese computing power is maturing, allowing for the establishment of a replicable technology ecosystem that includes algorithm optimization and talent training [10] - China's complete industrial chain in AI provides a competitive edge in deploying solutions across various sectors, including consumer and business applications [11] Group 5: Significance of the Global Expansion of Domestic Computing Power - The global expansion of domestic computing power reduces reliance on Western technologies and demonstrates that domestic technologies can compete internationally [12] - The promotion of China's AI ethical framework as a potential international standard could reshape global governance in AI [12] - The initiative to build offshore data resource pools can enhance domestic model training by providing diverse data sources [14]

Core Insights - The rapid development of large models driven by AI demands significant computational power, leading to the emergence of the "SuperPod" as a key solution for efficient AI training [1][2] - The transition from traditional computing architectures to SuperPod technology signifies a shift in the AI infrastructure competition from isolated breakthroughs to a system-level ecosystem [1][5] Industry Trends - The SuperPod, proposed by NVIDIA, represents a Scale Up solution that integrates GPU resources to create a low-latency, high-bandwidth computing entity, enhancing performance and energy efficiency [2][4] - The traditional air-cooled AI servers are reaching their power density limits, prompting the adoption of advanced cooling technologies like liquid cooling in SuperPod designs [2][5] Market Outlook - The market for SuperPods is viewed positively, with many domestic and international server manufacturers selecting it as the next-generation solution, primarily utilizing copper connections [2][4] - Major Chinese tech companies, including Huawei and Xizhi Technology, are actively developing SuperPod solutions, showcasing significant advancements in AI computing capabilities [5][6] Technological Developments - The ETH-X open standard project, led by the Open Data Center Committee, aims to establish a framework for SuperPod architecture, combining Scale Up and Scale Out networking strategies [4] - Companies like Moer Thread are building comprehensive AI computing product lines, emphasizing the need for efficient collaboration among large-scale clusters to enhance AI training infrastructure [6]