贯穿2025全年的半导体IPO热潮于年末进入密集兑现期——继摩尔线程、沐曦股份等明星企业上市之后，又一家国产GPU龙头行将登陆资本市场。 港交所12月17日文件披露，上海壁仞科技股份有限公司(以下简称"壁仞科技")正式通过上市聆讯，有望成为港股国产GPU第一股。据招股书，成立于2019 年的壁仞科技专注于GPGPU(通用图形处理器)芯片及智能计算解决方案，为AI模型训练与推理提供全场景基础算力。 壁仞科技团队告诉北京商报记者，国产GPU行业处于高投入、商业化探索阶段，公司依托GPGPU方案在AI模型算力上的技术壁垒已实现营收快速增长； 本次募资将投向研发升级，伴随产品矩阵放量，订单有望支撑营收持续增长。 01.国产替代风口下的冲刺底气 北京社科院副研究员王鹏向北京商报记者分析，当前国产GPU企业密集上市，证明技术验证和规模商用已经逐渐形成良性循环。 需求端的动能亦在快速释放——据摩根士丹利年中发布的报告预测，到2027年，我国国产GPU自给率将从2024年的34%跃升至82%；该机构12月最新报告 进一步看好中国AI GPU的收入增长，将2026年和2027年的预期从940亿元、1360亿元分别上调至1130 ...

2025年，无疑是国产GPU行业的一个关键分野。 6月30日，摩尔线程与沐曦股份同日获得科创板受理，吹响了IPO冲锋的号角。摩尔线程随后以惊人的"科创板速度"——89天火速过会，并于12月5日登陆 科创板，首日暴涨468.78%，市值一度突破3000亿元，创下年内新股纪录。 沐曦股份紧随其后，于12月7日完成申购，中签率低至0.033%，市场热情甚至超过摩尔线程。壁仞科技、燧原科技等也相继传出明确的港股或A股上市计 划……"这波密集上市潮，本质上是一场由资金需求驱动的突围战。"新鼎资本董事长张驰指出。 财务数据反映了GPU行业的共同特点：摩尔线程与沐曦股份过去三年累计研发投入超60亿元，营收尚处爬坡阶段。"上市几乎成为穿越漫长研发投入期、 维系生存与发展权的关键通道。"张驰认为。 然而，上市仅是拿到了下一阶段的入场券。回顾2025年，国产GPU厂商面临的核心挑战愈发清晰：供应链"卡脖子"风险正突破设计端，向制造乃至封装环 节持续蔓延。而CUDA生态的壁垒，在AI应用爆发之年显得愈加坚不可摧。 与此同时，竞争逻辑也在2025年发生根本转变。商业化落地能力取代PPT参数，成为检验真金的唯一标准。 ""现在说谁是中 ...

Core Insights - Nvidia's CEO Jensen Huang visited South Korea after 15 years, signaling a strategic partnership with major Korean conglomerates to address supply chain concerns [1][3] - Nvidia recently became the first company to surpass a market capitalization of $5 trillion, highlighting its dominance in the AI sector [1] - The meeting with Samsung and Hyundai leaders was not just a casual gathering but a strategic move to strengthen Nvidia's supply chain and market position [1][3] Group 1: Strategic Partnerships - Huang's meeting with Samsung's Lee Jae-Yong and Hyundai's Chung Eui-sun at a casual venue symbolizes a deeper partnership beyond mere supplier-client relationships [1][6] - Nvidia aims to secure a stable supply of GPUs and HBM (High Bandwidth Memory) from Samsung, which is crucial for its production capabilities [2][3] - The partnership includes significant GPU allocations: 50,000 units each for the South Korean government, Samsung, and SK Group, and 60,000 units for NAVER Cloud [7] Group 2: Market Dynamics - The global AI arms race has intensified, making wafer production and HBM supply critical bottlenecks for Nvidia's capacity expansion [3] - Competitors in China, such as Huawei and Alibaba, are forming alliances to create an independent AI computing ecosystem, posing a long-term threat to Nvidia's market share [6][7] - The importance of building a comprehensive ecosystem is emphasized, as future demand for inference will surpass training needs, making cost-effectiveness and ecosystem integration vital [6] Group 3: Risks and Challenges - Despite the strategic alliances, Nvidia's heavy reliance on a single region for its supply chain presents inherent risks [8] - The potential for cracks in these alliances could lead to vulnerabilities in Nvidia's market position, highlighting the need for diversification [8]

Group 1 - The company Longxin Zhongke (688047.SH) is accelerating its research and development of GPGPU technology [1]

Core Viewpoint - The article emphasizes the importance of unifying instruction set architecture (ISA) for the development of China's computing chips, suggesting that RISC-V should be adopted as a standard to enhance innovation and resource efficiency in the semiconductor industry [5][30]. Group 1: Evolution of Computing Architecture - Over the past 40 years, the development of processor chips has followed a "negation of negation" spiral, oscillating between self-research and abandonment [4]. - The last five years have seen a resurgence of machine and platform manufacturers entering the "chip war," shifting from CPU-centric homogeneous computing systems to heterogeneous computing involving CPUs and xPUs [5]. - The computing evolution has transitioned from centralized processing to distributed systems, with the current core CPUs dominated by x86 and ARM architectures [9][10]. Group 2: Challenges in Architecture Innovation - The article discusses the difficulty of architecture innovation and the greater challenge of building an ecosystem, highlighting that software and collaboration barriers are significant [14]. - The dominance of x86 architecture is attributed to its ability to adapt and expand its instruction set to meet new application demands, while RISC architectures have struggled due to high costs and inability to disrupt existing ecosystems [11][13]. - The article notes that the software development costs significantly exceed hardware costs, making it challenging for new architectures to gain traction in the market [19]. Group 3: Future of RISC-V and ARM - RISC-V faces commercialization challenges despite its potential, with successful applications primarily in simple software scenarios like embedded systems [21]. - The article predicts that x86 CPUs will continue to dominate the server market for the foreseeable future, while ARM's success will depend on its ability to penetrate the x86-dominated landscape [20]. - The article suggests that the future of RISC-V in general-purpose computing will require overcoming significant hurdles, particularly in software and ecosystem development [24]. Group 4: Unified Instruction Set as a Key Pathway - The article advocates for a unified instruction set as a critical pathway for scaling China's computing chips, with cloud service providers being more successful in self-developing chips due to their control over the entire stack [25][26]. - It highlights that successful self-developed chips, like those from Apple, are not just about hardware but also about the integration of software and ecosystem capabilities [27][28]. - The call for RISC-V as a unified instruction system aims to avoid redundant efforts and resource wastage in chip development, promoting a more efficient innovation landscape [30].

Core Insights - The AI and robotics industry in China is experiencing rapid development, with significant breakthroughs and advancements among key enterprises, leading to increased global market share [2][3] - Despite progress, there remains a notable gap in scale, long-term viability, and algorithms compared to developed countries, but domestic companies are actively working to close this gap through innovation and collaboration [3][11] AI Computing Foundation Breakthroughs - Companies like Haiguang Information, Baiwei Storage, and Gai Lun Electronics are providing critical support for the acceleration of AI development in China, focusing on chip design, storage, and EDA software [4] - Haiguang Information has successfully commercialized multiple generations of products, widely applied in key industries such as finance and education, and is transitioning from "computing-centric" to "AI-enhanced" solutions [4][12] - Baiwei Storage is expanding into emerging fields like AI wearables and smart vehicles, projecting over 1 billion yuan in revenue from AI-related businesses in 2024 [5][11] Robotics and Applications - Companies such as Green Harmonic, Aifute, and Buke Co. are involved in various aspects of robotics, from core components to system integration, with a focus on adapting to the evolving demands of the industry [7][8] - Green Harmonic has broken Japan's monopoly in harmonic reducers, achieving the second-largest global market share and is expanding into new areas like mechatronic transmission [8][20] - Aifute is focusing on self-developed core technologies and intelligent solutions, with plans to establish an independent company for AI technology development by 2024 [9][17] Challenges and Strategies - The domestic storage industry faces challenges in brand trust, scale, and technological maturity, with less than 10% market share in China and under 5% globally [11][14] - Companies are optimistic about catching up by leveraging new market opportunities in AI and wearable technology while increasing R&D investments [11][14] - EDA companies like Gai Lun Electronics are focusing on enhancing their technical capabilities and collaborating with leading firms to drive growth in the domestic EDA industry [13][16] Long-term Vision and Market Dynamics - The robotics sector is expected to see a consolidation of suppliers, with a focus on building long-term technological barriers and adapting to new market demands [18][19] - Companies are balancing short-term responses to emerging demands with long-term capability building to ensure survival in a competitive landscape [19][21] - The environmental sector is also facing challenges in talent acquisition and developing sustainable business models, particularly in AI applications [23]

Core Insights - The AI and robotics industry in China is experiencing rapid development, with significant breakthroughs and advancements among key enterprises, driven by AI's influence on the industrial chain [1][2] - Domestic companies are actively working to close the technology gap with international counterparts through collaborative research and continuous innovation [2][10] AI Computing Foundation Breakthroughs - Companies like Haiguang Information, Baiwei Storage, and Gai Lun Electronics are providing critical support for China's AI development across various domains, including chip design and storage [3][4] - Haiguang Information has successfully commercialized multiple generations of products, widely applied in key industries such as finance and education, and is transitioning from a "compute-centric" to an "AI-enhanced" model [3][4] - Baiwei Storage is focusing on semiconductor storage, with projected AI-related revenue exceeding 1 billion yuan in 2024, and is expanding into emerging fields like AI wearables and smart vehicles [4][10] Robotics and Application Innovations - Companies like Green Harmonic, Efort, and Buke are involved in the entire robotics value chain, from core components to system integration, while Yuanchen Technology focuses on AI applications in environmental management [6][7] - Green Harmonic has achieved a significant market share in harmonic reducers, breaking Japan's monopoly and expanding into new areas such as mechatronic transmission and electric-hydraulic applications [6][7] - Efort is advancing its core technologies and collaborating with customers and universities to drive innovation in robotics, with plans to establish an independent AI technology company [7][16] Challenges and Strategies in Technology Gap - Despite advancements, there remains a notable gap in technology, scale, and ecosystem collaboration between domestic and international players, particularly in the AI computing foundation and robotics sectors [10][12] - Domestic storage companies face challenges in brand trust and scale, with less than 10% market share in China and under 5% globally, but are optimistic about capturing new opportunities in AI applications [10][11] - The EDA sector is also experiencing growth, with companies like Gai Lun Electronics focusing on enhancing their technical capabilities and collaborating with leading firms to drive innovation [12][15] Long-term Vision and Development Paths - Companies are adopting dual strategies of internal optimization and external collaboration to accelerate technological advancements and market presence [11][13] - Baiwei Storage aims for breakthroughs in technology and scale by 2028, focusing on AI applications and maintaining a high growth rate in revenue and R&D investment [12][13] - The robotics sector is addressing the challenge of balancing short-term demands with long-term technological barriers, emphasizing the need for deep understanding of robotics and intelligent systems [17][18]

Core Viewpoint - India is aiming to develop its own 2nm GPU by 2030 to reduce reliance on foreign companies like Nvidia and strengthen its domestic AI capabilities [1][2]. Group 1: Development Plans - The Bangalore-based Centre for Development of Advanced Computing (C-DAC) has received $200 million in funding to develop the GPU, with a preview expected by the end of 2025 [1][2]. - The early prototypes of the GPU will be produced by the end of this year, with plans to create around 29 units for testing [8]. - The goal is to have the GPU integrated into C-DAC's cloud servers and supercomputers by 2030, enabling local researchers and startups to build AI models [4][9]. Group 2: Cost and Production - The cost of India's GPU is projected to be 50% lower than Nvidia's current chip prices [2]. - India is likely to collaborate with TSMC for large-scale production, as it may not have a domestic manufacturing facility ready within the next five years [2][4]. - C-DAC's annual capital fund allocation for FY24 is 10.56 billion INR ($1.22 million), with an increase expected in FY25 [5]. Group 3: Geopolitical Context - The U.S. has the ability to restrict access to critical chips for countries like India, prompting the need for India to develop its own chips [4]. - The push for indigenous chip development is also driven by security concerns related to dependence on foreign supply chains, particularly from China [5][9]. Group 4: Challenges and Industry Insights - Building indigenous chips faces challenges such as high licensing costs for electronic design automation (EDA) tools and the complexity of GPU design processes [10]. - Industry experts emphasize the importance of creating proprietary semiconductor IP to enhance domestic value and reduce reliance on foreign patents [10].

Summary of Key Points from the Conference Call Industry Overview - The discussion revolves around the competition between GPGPU (General-Purpose Graphics Processing Unit) and ASIC (Application-Specific Integrated Circuit) chips in the AI and computing industry [2][4][16]. Core Insights and Arguments - **Performance Comparison**: - ASIC chips focus on low precision tasks and have better power consumption and efficiency compared to GPGPU, but struggle to match GPGPU performance in certain metrics. For instance, NVIDIA's GB200 achieves 5,000 in FP16 mode, significantly outperforming contemporaneous AI chips [2][3]. - NVIDIA's GB200 utilizes HBM3 technology, providing over 13,000 GB/s bandwidth, which is crucial for handling large-scale data [2]. - Google’s TPU V6E shows high memory utilization efficiency in specific tasks, but domestic ASIC chips still lag behind NVIDIA in memory bandwidth and capacity [2]. - **Cost and Resource Optimization**: - Large enterprises are increasingly developing their own AI chips to optimize resources and reduce costs. Estimates suggest that shipping approximately 45,000 to 70,000 cards can cover initial investments [4][8]. - The demand for training clusters has surpassed 100,000 cards, indicating a significant market opportunity for self-developed chips [4][9]. - **Interconnect Capabilities**: - NVIDIA's NV Link demonstrates superior interconnect capabilities, achieving 1.8 TB/s speeds, while competitors primarily use PCIe protocols, which are significantly slower [6][7]. - Innovations like LPU with 230 MB FRAM integration can overcome traditional GPU memory bottlenecks, enhancing performance for low arithmetic intensity tasks [6]. - **Market Trends**: - The AI training and inference market is expanding, with major companies building large GPU clusters. For example, Meta has constructed two 24K GPU clusters, and XAI plans to expand to 1 million cards by 2026 [9]. - The inference segment is projected to grow, with NVIDIA reporting that 40% of its data center revenue comes from inference business [9]. Important but Overlooked Content - **Company Collaborations**: - Marvell has signed a five-year agreement with Amazon to provide customized AI chips, indicating a strategic partnership that could influence the AI chip market significantly [12]. - Broadcom maintains a strong position in the interface interconnect sector, offering differentiated solutions for various AI cluster sizes and has launched a 5nm CMOS technology for high-speed Ethernet NIC devices [5][10]. - **Future Market Expectations**: - Broadcom anticipates its AI Networking (AIN) business revenue to reach between $60 billion and $90 billion by 2027, showcasing robust growth potential [11]. - Marvell is expected to capture at least 20% of the AI chip market by 2028, driven by increasing demand from major clients like Amazon [12]. - **Technological Innovations**: - ZTE is leading in GPGPU chip development and has made significant advancements in high-performance computing infrastructure, including 400G and 800G data switches [13]. - New研股份 is positioned as a key player in custom services and IP licensing, maintaining strong connections with major internet companies [15]. - **Domestic Chip Development**: - While domestic GPGPU and ASIC chips have certain advantages, they still face performance challenges. However, the trend of large enterprises developing their own chips is expected to continue, particularly in the inference era [16].

Investment Rating - The report maintains a "Buy" rating for the companies discussed, including recommendations for specific stocks such as Eft-U and Changsheng Bearings [9][10][25]. Core Insights - The report highlights the ongoing competition between GPGPU and ASIC in the chip industry, noting that while ASICs excel in low-precision tasks with better power efficiency, they still struggle to match GPGPU performance in high-precision applications [22]. - The emergence of AI applications is driving demand for AI inference, with major companies investing in self-developed AI chips to meet this growing need [22]. - The report discusses the recent advancements in brain-machine interface technology, emphasizing the establishment of pricing guidelines by the National Healthcare Security Administration to support the clinical application of these technologies [7][8][24]. Summary by Sections Macro Strategy - Recent U.S. economic data presents mixed signals, with non-farm employment slightly below expectations, alleviating some recession fears [12]. - The "tight fiscal" approach from the Trump administration is impacting market sentiment, leading to declines in U.S. stocks and the dollar [12][17]. Fixed Income - The report discusses the upcoming issuance of Haohan Convertible Bonds, with an expected listing price range of 118.73 to 132.27 yuan [20]. Industry Analysis - The competition between GPGPU and ASIC is analyzed, with GPGPU maintaining a strong market position due to superior interconnect capabilities [22]. - Major companies are increasingly investing in self-developed AI chips, with significant R&D expenditures required to cover initial costs [22]. - The report identifies key players in the AI chip manufacturing space, including Broadcom and Marvell, highlighting their competitive advantages [22]. Medical and Biological Industry - The successful implementation of brain-machine interface technology is noted, with new pricing projects established to facilitate its clinical use [7][8][24]. - The report suggests potential investment opportunities in companies involved in brain-machine interface technologies, both listed and unlisted [24].