3 6 Ke·2026-02-02 08:04Core Insights - Aixin Yuanzhi, a Chinese edge AI chip unicorn, is set to launch its global IPO on February 10, 2024, on the Hong Kong Stock Exchange, offering approximately 105 million H-shares [1][3] - The company specializes in AI inference system chips (SoC) for edge computing and terminal devices, positioning itself as a key player in the "edge brain" sector of the physical AI wave [1][4] Company Overview - Founded in 2019, Aixin Yuanzhi ranks among the top five global suppliers of visual edge AI inference chips, with an estimated shipment of 9.12 million chips in 2024, capturing a market share of approximately 6.8% [3][11] - In the mid-to-high-end visual edge AI inference chip segment, the company holds a leading market share of 24.1% [3][11] - Major shareholders include Meituan's Beijing Kuxun Technology, Tencent, Lenovo Star, and the founder of Haowei Group, Yu Renrong [3] Technology and Product Development - Aixin Yuanzhi's core computing unit, the Aixin Tongyuan hybrid precision NPU, is designed to support mainstream visual and multimodal model architectures, achieving higher energy efficiency compared to general-purpose GPUs [6][7] - The company has developed five generations of SoCs, with cumulative deliveries exceeding 165 million units by September 30, 2025, indicating a successful transition from concept validation to mass production [9][11] - The product matrix includes various SoCs for visual perception, smart vehicles, and edge AI inference applications, with commercialized products like the AX520 series and M55H already in the market [8][16] Market Trends and Future Outlook - The AI industry is shifting focus from cloud-based AI to physical AI, emphasizing real-time performance, reliability, and environmental adaptability [4][17] - Aixin Yuanzhi's technology and production experience position it well to meet the growing demand for efficient and stable edge computing solutions, particularly in smart automotive applications [16][17] - The company is exploring edge computing solutions and adapting large models for deployment at the edge, further enhancing its competitive edge in the market [16]