星辰天合递表港交所 分布式存储赛道竞争白热化

Core Viewpoint - Beijing Xingchen Tianhe Technology Co., Ltd. has submitted its listing application to the Hong Kong Stock Exchange, capitalizing on the exponential growth in AI storage demand driven by large model training and intelligent applications [1][2]. Industry Overview - The demand for data storage performance, capacity, and compatibility is experiencing exponential growth, making AI storage a core infrastructure [1]. - The Chinese distributed AI storage market is projected to grow from 10 billion yuan in 2024 to 63.1 billion yuan by 2030, with a compound annual growth rate (CAGR) of 35.9% [2]. - The installed capacity in this market is expected to expand from 10.3 exabytes in 2024 to 60.5 exabytes by 2030, with a CAGR of 34.3% [2]. Company Position - Xingchen Tianhe holds a 10.4% market share, ranking second in China's distributed AI storage market and is the largest independent supplier [1][2]. - The company has experienced significant revenue growth, with revenues of 167 million yuan in 2023, 173 million yuan in 2024, and 195 million yuan for the first nine months of 2025, marking a 65.4% increase compared to the same period in 2024 [5]. Competitive Landscape - Major competitors include Huawei and Inspur, which leverage their comprehensive hardware advantages and extensive channel networks [3]. - Independent storage vendors need to create differentiated barriers by focusing on technology, scenarios, and ecosystems to avoid price competition [3]. Business Model - Xingchen Tianhe's core business focuses on providing enterprise-level AI storage solutions, helping companies efficiently integrate data and operations during their AI transformation [2][7]. - The company offers solutions through two delivery models: a pure software model and an integrated hardware-software model [7]. Challenges and Risks - The rising component prices are causing supply chain imbalances, potentially leading to a market shakeout where smaller vendors may struggle to survive [4][5]. - Data storage security risks are evolving, requiring a comprehensive lifecycle approach to risk management, particularly in the context of AI [8][9].