Summary of Capital Online Conference Call Company Overview - Capital Online is undergoing a comprehensive transformation towards intelligent computing business, with a projected growth of 60%-80% in GPU business by 2025, benefiting from the acceleration of multimodal applications [2][3][7] Strategic Initiatives - The company's strategy is defined as "One Cloud, Multiple Pools; One Cloud, Multiple Models; One Cloud, Multiple Chips" [2][3] - Launched the GPU g customer platform, charging based on nodes and tokens, with models like Deep Seek and Zhipu already online, and plans to launch an overseas version in Q3 or Q4 of 2025 [2][3] Infrastructure Expansion - Actively expanding computing power infrastructure, including: - Expansion of the Wanka cluster in Gansu Qinyang - Completion of the Hebei Huailai base by the end of 2025, with a planned capacity of 50 megawatts - Construction of the Anhui Wuhu node starting in 2026, with a planned capacity of 100 megawatts - Planning a 15-megawatt node in Dallas, USA, addressing energy issues [2][5] Chip Management and Investment - The company manages and owns 21,000 chips, including models 4,090, 5,090, and H200 [2][4] - Plans to invest 300-500 million yuan in chip purchases in 2025, having already spent approximately 200 million yuan by the end of Q1, mainly on models 4,090 and H200 [2][6] Financial Projections - Expected profit for 2025 is approximately 1.5 billion yuan, an increase from 1.3 billion yuan in 2024, but still in a loss-reduction phase [2][6] - Anticipates achieving profitability in 2026 due to government subsidies, reduced GT saturation, cost declines, and improved gross margins from economies of scale [2][6][7] Market and Customer Insights - The GPU business is expected to grow at a rate of 60%-80% in the next one to two years, while CPU business growth is projected at around 10% [3][7] - IDC business growth is limited in 2025 but expected to grow by 0-5% in 2026, with potential growth of 5-10% in the following year [7] - Major customers in the AI application explosion include Zhipu, Horizon, Squirrel Technology, and Meitu, focusing on inference-side demand [3][8] - The company aims to expand its customer base to include high-volume clients like Kuaishou, offering bare metal and cloud computing services with software capabilities [8] Industry Trends - The primary customers in the GPU sector are from AIGC, large model applications, education, finance, and government sectors, with limited conversion from the internet industry [9] - The company is considering entering the computing power leasing business if internal demand cannot be fully met [10] Additional Insights - Current data flow usage for large models like text-to-text and text-to-image remains low, with many government and education clients still in pilot phases [11] - Anticipated gradual increase in data flow in the second half of the year, driven by the release of multimodal models and new large applications [11]

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Core Viewpoints - The article discusses the differences in AI narratives and computational needs between China and North America, highlighting China's focus on practical applications and cost-effectiveness in AI deployment, while North America aims for advanced models and AGI [1][2][3]. China AI Narrative - China's AI narrative emphasizes the democratization of AI through open-source models and the development of smaller distilled models for edge applications, leading to widespread implementation [1]. - The focus is on practical applications that do not necessarily require high-end GPUs, with companies leveraging existing infrastructure to achieve rapid deployment and monetization [3][4]. China Computational Needs - The article suggests that for many AI applications, especially those that are not highly complex, existing Chinese chips like H20 and domestic ASICs are sufficient [4]. - There is a discussion on the potential of using simpler architectures, such as FPGA combined with RISC-V, for edge AI applications [4]. North America AI Narrative - North America's AI narrative continues to push for breakthroughs towards AGI, with a focus on multimodal high-order models and trillion-parameter models [2]. - The article notes that the progress in North America is slower compared to China, leading to skepticism about the necessity of high-end NVIDIA chips in certain applications [3][9]. North America Computational Needs - High-end NVIDIA GPUs are still in high demand, particularly for applications requiring high concurrency and real-time generation, such as multimodal AI applications [5][6]. - The need for advanced chips is emphasized for training large models and applications in fields like AI for science, where low latency is critical [7][8]. Key Comparisons - The article highlights that while China is achieving rapid results with lower-cost solutions, North America may face challenges in meeting the demands of high-performance applications without high-end GPUs [3][9]. - The potential of DS's AI infrastructure capabilities is noted as a variable that could impact the reliance on NVIDIA chips in the future [10].