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].

Summary of the Conference Call on PCB Industry Industry Overview - The global PCB output in Q4 2024 is expected to grow by 5.8% year-on-year, with the Americas and mainland China both experiencing a growth of 9%, while Europe sees a decline of 5.3% and Japan also faces challenges due to a lack of AI supply chain and declining fuel vehicle sales [3][4] Key Insights and Arguments - The growth in PCB output is significantly driven by the AI trend, particularly benefiting companies in mainland China and the Americas, while Japan and Europe are negatively impacted [3][4] - Domestic PCB companies such as Huadian Co. and Shenzhen South Circuit are projected to see over 70% growth in 2025, with Shun Cheng expected to achieve more than half of its 2024 annual performance in Q1 2025, indicating a strong pull from AI data centers [5] Impact of Major Companies - NVIDIA's H series servers, along with AMD, Google, and domestic AI servers, are major contributors to the increased demand for PCBs. Future products like NVIDIA's B series B200/B300 and the next-generation Ruby AI servers are expected to continue driving high-value PCB development [6] - The GB200 series is performing well with strong orders and production yields reaching mass production levels, benefiting companies like Shenghong [6] Technological Developments - The GB300 solution has introduced significant changes compared to the GB200, increasing the layer count from 24-layer HDI to 26-layer through-hole boards, indicating enhanced technical complexity and application scenarios [7] - The Robin 288 cabinet solution utilizes a 40-layer mixed pressure PDP backplane, with each cabinet requiring approximately 2 square meters of backplane area, creating new investment opportunities. Companies like Jingwang are already in the sampling and testing phase, with mass production expected in 2026 [8] Domestic Server Market - The domestic computing power chain is expected to perform well in 2025, with core domestic server manufacturers achieving over 50% of their annual targets in Q1. The demand for Ascend 910 chips is projected to exceed 800,000 units, with companies like Haiguang and Moore Threads likely to make breakthroughs, positively impacting domestic server PCB demand [9] Optical Module Market - The demand for 800G optical modules is expected to nearly double in 2025, with the 1.6T optical modules also set to ramp up quickly in the second half of the year. The market size is projected to exceed 4 billion RMB, benefiting companies like Shenzhen South Circuit and potentially others like Gengxing and Jingwang [10] Future Outlook - The PCB industry in both the global and mainland China markets is expected to remain in a high prosperity phase driven by demand for AI servers and network devices. High-end PCB capacity remains tight, and leading PCB manufacturers are showing significant quarter-on-quarter growth. Companies such as Shenfeng, Huadian, and Shengnan, as well as upstream CCL material companies like Nanya Technology and Shenyu Technology, should be closely monitored [11]

Investment Rating - The report maintains an "Outperform" rating for the computer industry [1]. Core Insights - The computer industry is experiencing a slowdown in revenue and profit growth, with software business revenue expected to reach 13.73 trillion yuan in 2024, a 10.0% year-on-year increase, down from 13.4% in 2023 [4][10]. - The report highlights significant capital expenditure increases from major tech companies, indicating a strong investment trend in AI and computing infrastructure [49][52]. Summary by Sections 1. Industry Data - The software industry in China is projected to see a revenue growth of 10.0% in 2024, down from 13.4% in 2023, with total profits expected to grow by 8.7% [4][10][11]. - Software exports are anticipated to increase by 3.5% in 2024, recovering from a decline in the previous year [11]. 2. High-Growth Sectors in 2024 - Integrated Circuit (IC) design is expected to be the highest growth sector, with a projected increase of 16.4% [13]. - Embedded system software is forecasted to grow by 11.8%, driven by ongoing AI advancements [14]. - E-commerce platform services are also expected to grow by 11.4% [15]. 3. Localization - The dependency on imported integrated circuits is at 78%, indicating a 22% localization rate, which has decreased by 2% [20][21]. - Nvidia's revenue from mainland China has decreased, reflecting the impact of U.S. sanctions [23]. 4. AI Developments - The launch of DeepSeek-R1 has intensified competition in the AI model space, with significant advancements in open-source models [25][27]. - DeepSeek's open-source initiative has garnered global attention and is expected to accelerate AI technology development [32][38]. 5. Computing Power - Domestic computing power investments are accelerating, with major tech firms planning substantial capital expenditures [49][52]. - Nvidia's new Blackwell chip has significantly contributed to its revenue growth, indicating strong demand for advanced computing solutions [55][56].

Investment Rating - The report maintains a "Buy" rating for the computer industry, specifically for companies like Kingsoft Office, Tonghuashun, and Yonyou Network [7][10][26]. Core Insights - DeepSeek has opened its Infra core code, enhancing model efficiency and hardware compatibility, particularly with domestic GPUs, which is expected to lower application costs and improve performance [1][2][3]. - The report highlights a divergence in strategies between domestic and overseas model companies, with overseas firms focusing on large computing power while domestic firms prioritize efficiency optimization [4]. - The potential for model capabilities to become fundamental resources akin to "water and electricity" is emphasized, suggesting significant advantages for companies leveraging these capabilities [5]. Summary by Sections Investment Rating - The report provides a "Buy" rating for Kingsoft Office (688111 CH), Tonghuashun (300033 CH), and Yonyou Network (600588 CH) with target prices of 351.05, 425.23, and 16.12 respectively [10][26]. DeepSeek Developments - DeepSeek's recent open-source initiatives include core optimizations in MLA, communication-computation, and matrix multiplication, which are expected to enhance global model training and inference efficiency [2][3]. - The report notes that DeepSeek's model training has been optimized for CUDA, with successful adaptations for domestic GPUs, indicating a growing ecosystem for local chip manufacturers [3]. Market Dynamics - The report identifies a trend where overseas companies like xAI and OpenAI are expanding their GPU clusters to enhance performance, while domestic companies are focusing on software and hardware efficiency improvements [4]. - The analysis suggests that the cost-profit margin for DeepSeek's services could reach 545% under optimal conditions, highlighting the financial viability of its model [1][22]. Recommended Companies - Companies with user, data, and scenario advantages are recommended, including Kingsoft Office, Tonghuashun, and Yonyou Network, as well as other relevant players in the 2B and 2C application sectors [5][10][26].

Group 1: Apple Foldable iPhone - Apple's first foldable iPhone is expected to be released in the fall of 2026, likely as part of the iPhone 18 series, with a base price estimated between 15,000 to 20,000 yuan and a top configuration potentially reaching 25,000 yuan, making it the most expensive iPhone ever [1] - Analysts suggest that if the pricing is below 12,000 yuan, Apple could capture 30% of the market share currently held by Samsung [1] - Morgan Stanley believes that Apple's "high-end anchoring strategy" will reinforce brand premium, positioning the foldable iPhone as the biggest consumer electronics highlight of 2026 [1] Group 2: Domestic Computing Power - The Beijing Digital Economy Computing Power Center has established a domestic computing power cluster with a PUE index of 1.146, supporting AI training and inference with full-stack domestic solutions [1] - Nine manufacturers, including Huawei and Haiguang, have completed compatibility certification for 11 domestic chips, indicating an improvement in the maturity of domestic chips in high-performance AI scenarios [1] Group 3: DeepSeek and RISC-V Architecture - DeepSeek is promoting the rise of the RISC-V architecture by open-sourcing the MoE model code library, which reduces computing power requirements and facilitates the migration of large models to the terminal [2] - The total cost of GPU leasing is estimated at $8,707.2 per day, while the theoretical daily revenue from all tokens calculated at DeepSeek R1's pricing could reach $56,202.7, resulting in a profit margin of 545% [2] - Analysts generally believe that the domestic computing power industry chain (chips/IP, servers, data centers) will benefit from policy support and accelerated localization, with a projected growth rate of over 50% for the domestic AI chip market by 2025 [2]

Financial Data and Key Metrics Changes - The demand for AI training chips has seen fluctuations, with a notable decrease in the urgency for GPU procurement compared to the previous year, attributed to high initial demand and tightening government budgets [16][19][20] - The price of GPUs has decreased significantly, with reductions of around 20% observed in the market [16] Business Line Data and Key Metrics Changes - Companies like Zhipu, Baichuan, and MiniMax primarily relied on third-party computing power leasing, with a gradual shift towards self-built infrastructures, although the transition is still in early stages [13][19] - The rental market remains dominated by NVIDIA's A100 and H100 models, with A800 also seeing increased usage due to better cost-performance ratios [15][16] Market Data and Key Metrics Changes - The market for AI chips is currently characterized by a cautious approach towards domestic alternatives, with companies actively testing local chips but still favoring NVIDIA due to supply stability concerns [20][25] - The overall supply of NVIDIA chips has been impacted by restrictions, leading to a heightened interest in domestic alternatives, although their availability remains inconsistent [24][25] Company Strategy and Development Direction - Companies are increasingly considering self-built computing clusters as a long-term strategy, driven by the need for greater control and customization in their AI training processes [11][19] - The competitive landscape is shifting, with major players like Alibaba and Tencent exploring both domestic chip options and self-research initiatives alongside traditional NVIDIA solutions [30][37] Management Comments on Operating Environment and Future Outlook - The management emphasizes the complexity of the current market, where rapid technological advancements necessitate flexible procurement strategies, including leasing and self-building [11][12] - There is a recognition that while domestic chips are being explored, the immediate reliance on NVIDIA remains due to performance and ecosystem advantages [20][23] Other Important Information - The performance of Huawei's 910B chip is reported to be around 80% of the A800's capabilities, but its higher cost and lower ecosystem support limit its attractiveness [30][38] - The integration of domestic chips into existing infrastructures is seen as a significant challenge, with many companies hesitant to invest heavily without guaranteed performance [31][41] Q&A Session Summary Question: What changes have been observed in the computing power foundation of AI companies? - The computing power foundation for companies like Zhipu and Baichuan has not seen a significant reduction in third-party leasing, but there is an ongoing search for new vendors [13] Question: What types of chips are being prioritized in the rental market? - The rental market is primarily focused on NVIDIA's A100 and H100, with A800 also gaining traction due to its cost-effectiveness [15] Question: How are companies approaching the integration of domestic chips? - Companies are actively testing domestic chips but remain cautious due to supply stability issues, with a preference for NVIDIA when available [20][25] Question: What is the outlook for self-built computing clusters? - There is a strong belief that companies will eventually move towards self-built clusters for better control and customization, despite the current reliance on leasing [11][19] Question: How does the performance of Huawei's chips compare to NVIDIA's? - Huawei's 910B is estimated to perform at about 80% of the A800's capabilities, but its higher cost and lack of ecosystem support hinder its adoption [30][38]