Core Insights - Huang Renxun's visit to Beijing highlights that Nvidia's influence in the Chinese market has diminished significantly, as China no longer relies on Nvidia for AI chip technology [1][14] - The Chinese AI chip industry has rapidly developed, with companies like Huawei, Cambricon, and Alibaba producing competitive chips that can rival Nvidia's offerings [3][9] Industry Developments - The Chinese AI chip market has seen the emergence of strong domestic players, with Huawei's Ascend 910, Cambricon's Shiyuan 290, and Alibaba's Hanguang 800 leading the charge [3][5] - Major Chinese tech firms such as Baidu, Alibaba, and Tencent have shifted to using domestic chips for training AI models, previously reliant on Nvidia [7][9] Market Dynamics - Nvidia's attempts to continue selling in China with modified versions of their chips (A800 and H800) have not been well received, leading to a loss of trust among Chinese consumers [5][10] - The demand for Huawei's Ascend chips has surged, with orders reportedly extending into the second half of next year, indicating a supply shortage and competitive pricing compared to Nvidia [7][9] Strategic Implications - Huang Renxun's visit was intended to explore opportunities for collaboration, but the Chinese market has made it clear that it no longer needs Nvidia's products [9][14] - The development of a complete AI industry chain in China, from chip design to application, poses significant challenges for Nvidia to re-enter the market [9][10]

Core Viewpoint - Huawei's Ascend CloudMatrix 384 super node has demonstrated performance that surpasses NVIDIA's products in certain aspects, indicating a significant advancement in domestic AI chip capabilities [1][11][13]. Group 1: Huawei's Ascend Chip Overview - Ascend is a dedicated AI processing chip (NPU) designed specifically for AI tasks, with the Ascend 910 being its main product [3][6]. - Previously, Ascend chips were used as backup options due to the unavailability of high-end NVIDIA and AMD chips, but they have now emerged as leaders in the domestic chip market [3][6]. - The Ascend chips have primarily been utilized in AI inference tasks, with limited use in model training due to performance and ecosystem limitations [4][6]. Group 2: Performance and Capabilities - In 2024 and 2025, Huawei transformed Ascend from a backup option to a primary player capable of training large models, achieving significant results documented in research papers [5][6]. - Ascend has successfully trained models with 135 billion parameters using 8192 chips and 718 billion parameters using over 6000 chips, showcasing the ability to train large-scale models with domestic chips [6][10]. - Key performance indicators such as MFU (Modeling Function Utilization) reached over 50% for the dense model and 41% for the MoE model, indicating high efficiency in resource utilization [9][10]. Group 3: Competitive Comparison with NVIDIA - In direct comparisons, Ascend's 384 super node demonstrated comparable performance to NVIDIA's H100 and H800 in real-world applications, achieving the best utilization rates [11][12]. - Although a single Ascend chip's performance is only one-third of NVIDIA's Blackwell, the overall system performance of the 384 super node exceeds NVIDIA's GB200 due to the higher number of chips used [13][21]. - This indicates that Ascend is not just a replacement but has the potential to lead in certain performance metrics [13]. Group 4: Technological Innovations - The CloudMatrix 384 super node consists of 384 Ascend 910 chips and 192 Kunpeng CPUs, interconnected using advanced optical communication technology, which enhances data transmission efficiency [16][30]. - Huawei's approach focuses on a system-level engineering breakthrough rather than relying on single-chip performance, utilizing a combination of communication, optical, thermal, and software innovations [21][22]. - The architecture allows for high-speed, peer-to-peer communication among chips, significantly improving data transfer rates compared to traditional copper connections used by competitors [28][30]. Group 5: Market Position and Future Outlook - Despite still trailing behind NVIDIA in chip technology and software ecosystem, Huawei's Ascend has gained traction in the Chinese market, especially as companies adapt to domestic chips due to restrictions on NVIDIA products [36][38]. - The domestic semiconductor industry is evolving under pressure, with Huawei's strategy representing a unique "technology curve" that prioritizes system optimization over individual chip performance [38][39]. - The advancements made by Ascend may signify the beginning of a significant shift in the AI computing landscape, positioning domestic capabilities for a potential resurgence in the global market [40].

Core Viewpoint - Huawei's Ascend 384 Super Node has demonstrated performance that surpasses NVIDIA's products in certain aspects, indicating a significant advancement in domestic AI chip capabilities [2][3]. Group 1: Product Overview - Ascend is an AI chip developed by Huawei, specifically designed for AI tasks as an NPU, distinguishing it from traditional GPUs and CPUs [4]. - The main product, Ascend 910, has transitioned from being a backup option to a primary solution for training large models due to restrictions on high-end chips from NVIDIA and AMD [4][6]. Group 2: Performance Metrics - In recent developments, Huawei has successfully trained large models using Ascend chips, achieving a dense model with 135 billion parameters and a MoE model with 718 billion parameters [6]. - The key performance indicator, MFU (Modeling Function Utilization), reached over 50% for the dense model and 41% for the MoE model, indicating efficient utilization of computational resources [9]. Group 3: Competitive Analysis - In a direct comparison with NVIDIA's H100 and H800 during the deployment of large models, Ascend demonstrated comparable performance, achieving the best utilization rate in the competition [10]. - Although a single Ascend chip's performance is only one-third of NVIDIA's Blackwell, the 384 Super Node configuration, which utilizes five times the number of chips, results in an overall computational power that exceeds NVIDIA's GB200 [10].

Group 1 - Bill Gates expressed concerns about the uncertainty created by U.S. tariff policies, stating that they affect long-term planning for new factories and industries, particularly in pharmaceuticals and electronics [1] - The recent U.S.-China trade talks in London resulted in positive outcomes, with both sides agreeing to work together to maintain dialogue and implement consensus [1] - The U.S. Commerce Department issued a warning regarding the use of Huawei's AI chips, which has put global manufacturers in a difficult position between compliance and maintaining their own choices [3] Group 2 - NVIDIA CEO Jensen Huang stated that U.S. export controls on AI to China have failed, noting a significant drop in NVIDIA's market share in China from 95% to 50% since Biden took office [5] - Huang criticized the U.S. strategy as fundamentally flawed, arguing that it could lead to a loss of technological leadership for the U.S. [5] - The Chinese government emphasized the importance of adhering to international trade rules and supporting global technological cooperation, while warning of legal consequences for those complying with U.S. measures [5] Group 3 - The New York Times highlighted that China has reached a critical point in surpassing the U.S. in various technology sectors, including solar energy, electric vehicles, and 5G [7] - The article criticized the lack of strategic vision in many policies from the Trump administration, suggesting that this has weakened the U.S. position [7] - It was noted that China's advancements in manufacturing have made U.S. containment efforts increasingly ineffective [7]

Core Viewpoint - China's self-sufficiency rate in AI chips is expected to exceed 80% within three years, driven by the need to overcome U.S. semiconductor export controls, which have catalyzed the strengthening of China's semiconductor ecosystem [1][2]. Group 1: AI Chip Self-Sufficiency - As of last year, China's self-sufficiency rate in AI chips was only 34%, but it is projected to soar to 82% by 2027 [1]. - The external pressure from U.S. sanctions has accelerated China's efforts to achieve self-sufficiency, leading to the rapid establishment of a self-sustaining ecosystem [1]. Group 2: Talent and Strategic Investment - Approximately half of the world's AI researchers are based in China, which is a significant driver for the explosive growth of the AI sector [2]. - China is investing heavily in its AI ecosystem through substantial R&D funding and policies favoring domestic procurement, leveraging its large domestic market to support local companies [2]. Group 3: Robotics Market Potential - The humanoid robot market is expected to grow to $5 trillion by 2050, with China projected to capture 30% of the global supply due to cost competitiveness from domestic AI chip procurement [3]. - Manufacturing humanoid robots in China could reduce production costs to one-third of those using global supply chains [3]. Group 4: Ecosystem and Industry Growth - Leading companies in China's AI rise include Huawei, SMIC, Alibaba, Tencent, and others, all contributing to accelerated AI innovation [3]. - By 2030, the core AI industry in China is expected to grow to 1 trillion RMB (approximately 190 trillion KRW) [3]. - The competitive landscape is shifting from merely acquiring high-spec semiconductor chips to effectively integrating hardware with software and systems to create value [3].

Core Viewpoint - The article discusses the growth and investment opportunities in the AI computing center market in China, particularly focusing on the Huawei Ascend ecosystem and its associated companies across four key areas: complete machines, power supply, cooling, and connectivity [2]. Group 1: Complete Machines - The newly added computing power in 2024 is expected to reach approximately 20,000 PFlops, with the investment scale of China's intelligent computing center market projected to reach 288.6 billion yuan by 2028. In 2023, the market size was 87.9 billion yuan, showing a year-on-year growth of over 90% [3]. - As of August 2024, there are over 300 intelligent computing center projects in China, with a total announced computing power exceeding 500,000 PFlops. About one-third of these projects are planned to have a computing power greater than 500 PFlops, mainly funded by government or telecom operators [3]. Group 2: Power Supply - AI servers utilize three power supply methods: external cabinets, racks, and trays. The power supply unit (PSU) converts high-voltage AC from the grid to 48V DC, which is then further converted to 12V for CPUs and 0.8V for GPUs [15]. - The GB200 NVL72 cabinet is equipped with 48 5.5kW PSUs, providing a total power of 132kW. The increasing power demand in AI servers is expected to expand the AI power supply market [16][21]. Group 3: Cooling - The power consumption of single cabinets has increased from 4-6 kW in traditional computing centers to 20-40 kW or higher in intelligent computing centers. Liquid cooling technology is becoming the preferred choice due to its efficiency and low energy consumption [27]. - The market size for liquid cooling data centers in China was 8.63 billion yuan in 2023, with a growth rate of 26.2%, expected to reach 18.01 billion yuan by 2026 [29]. Group 4: Connectivity - Backplane connectors are crucial for high-performance servers and communication devices, supporting high-speed data transmission and ensuring signal integrity [38]. - The Chinese communication connector market is projected to grow at a compound annual growth rate of 30%-35%, with expectations to exceed 60 billion yuan by 2025, where AI-related connectors will account for over 70% of the market [40].

Core Viewpoint - The article discusses the significant returns generated by hard technology projects in China, particularly highlighting the success of Cambrian and CATL as exemplary cases of investment opportunities in the sector [4][24]. Group 1: Cambrian's Journey - Cambrian, founded by Chen Yunji and Chen Tian Shi, began its journey in AI chip development at the Chinese Academy of Sciences, proposing the idea of AI chip research as early as 2010 [7][8]. - The company gained prominence after its AI processor was used in Huawei's Mate 10 smartphone in 2017, leading to substantial revenue from IP licensing, primarily from Huawei [11]. - Following a strategic shift in 2019 due to declining revenues, Cambrian expanded its product offerings to include cloud and edge computing solutions [12][15]. Group 2: Financial Performance - Cambrian's stock price experienced a significant decline post-IPO, with a drop of 84.35% from its peak, but began to recover in 2023, eventually reaching a new high of 777.77 yuan per share [16][17]. - The company reported a remarkable revenue increase of 4230.22% year-on-year in Q1 2025, achieving a net profit of 3.55 billion yuan, marking its first consecutive profitable quarters since going public [18][19]. - As of Q1 2024, Cambrian's inventory and advance payments indicated strong market confidence and readiness for increased demand [19]. Group 3: Investment Landscape - Early investors in Cambrian, such as Yuanhe Origin and Lenovo Ventures, recognized the potential of AI technology and made significant investments during its early funding rounds [21][23]. - Despite facing challenges and market fluctuations, investors who remained committed to Cambrian have seen substantial returns, illustrating the long-term nature of hard technology investments [24][25]. - The article emphasizes that Cambrian's success serves as a motivation for more investors to engage in hard technology, showcasing the potential for significant financial rewards in this sector [25].

Core Viewpoint - The article discusses the significant returns generated by hard technology projects in China, particularly highlighting the success of Cambrian, which has seen its stock price surge and market capitalization exceed 320 billion yuan, marking it as a standout case in the hard tech investment landscape [1]. Group 1: Company Background - Cambrian was founded by two brothers, Chen Yunji and Chen Tian Shi, who both had strong academic backgrounds in AI chip research at the Chinese Academy of Sciences [3][6]. - The company was established in 2016 after the team developed the world's first deep learning-specific processor prototype in 2015, marking a significant milestone in AI chip development [3][6]. Group 2: Financial Performance - Cambrian experienced a dramatic rise in stock price, reaching a peak of 777.77 yuan per share in 2024, with a market capitalization increase of over 200 billion yuan within a year [12]. - The company reported a net profit of 2.82 million yuan in Q4 2024 and 3.55 million yuan in Q1 2025, marking its first consecutive profitable quarters since its IPO [12][13]. Group 3: Market Dynamics - Cambrian's revenue growth is attributed to its strategic pivot towards a "cloud-edge-end" layout and the introduction of new products, responding to the increasing demand for AI computing power in China [7][15]. - The company is preparing for increased market demand by maintaining a substantial inventory balance of 2.755 billion yuan and prepayments of 973 million yuan, indicating confidence in future orders [14][15]. Group 4: Investment Landscape - Early investors in Cambrian, such as Yuanhe Origin and Lenovo Capital, recognized the potential of AI technology and made significant investments, which have now yielded substantial returns [17][18]. - The article emphasizes the long investment cycles associated with hard technology, suggesting that patience and perseverance are essential for achieving significant returns in this sector [19].