对话「后摩智能」吴强：从科学家到创业者的惊险一跃

Core Viewpoint - The article emphasizes the significance of "storage-compute integration" as a key technology for edge AI chips, which is expected to revolutionize the last mile of large model computing, enabling efficient local processing and reducing reliance on cloud computing [2][4][6]. Group 1: Industry Trends - The AI model development has led to a two-tiered growth in computing power, with cloud computing expanding for model training and edge AI chips gaining traction for inference applications [4][5]. - The emergence of "super nodes" and edge AI chips was highlighted at WAIC 2025, showcasing the growing importance of localized computing solutions [3][4]. - The market for edge computing is anticipated to be larger than cloud computing, presenting opportunities for new players to emerge, potentially creating the "next Nvidia" [4][5]. Group 2: Company Insights - The company, Houmo Intelligent, founded by CEO Wu Qiang, focuses on developing AI chips based on storage-compute integration technology, aiming to address the challenges of traditional computing architectures [5][6]. - The newly launched M50 chip utilizes innovative architecture and compiler tools to enhance efficiency and ease of use, supporting mainstream deep learning frameworks [8][10]. - The M50 chip boasts impressive specifications, achieving 160 TOPS@INT8 and 100 TFLOPS@bFP16 with a power consumption of only 10W, making it suitable for various smart devices without cloud dependency [8][10]. Group 3: Market Strategy - The company is targeting multiple application areas, including consumer electronics, smart voice systems, and edge computing for telecom operators, with notable interest from clients like Lenovo and China Mobile [14][15]. - The transition from a focus on smart driving chips to general-purpose edge AI chips reflects a strategic pivot in response to market demands and opportunities in large model applications [11][13]. - The company aims to leverage its expertise in storage-compute integration to meet the growing needs for efficient AI processing in diverse sectors [17][18].