Core Insights - The 25th China International Industrial Expo showcased several manufacturers presenting industrial edge AI computing products, highlighting a shift towards edge AI computing that integrates AI algorithms for real-time data processing and analysis near data sources [1] Group 1: Transition from Cloud to Edge Computing - Edge computing is defined as processing data near its source rather than relying on centralized cloud computing, addressing issues of latency, privacy, and bandwidth [2] - The first step in digital transformation for industrial enterprises is equipment networking, which leads to the need for edge computing to gather and process data locally before sending it to the cloud [2] Group 2: Benefits of Edge AI Computing - Edge AI computing devices are typically compact and energy-efficient, reducing data transmission distances and frequencies, which lowers energy consumption [3] - An example provided indicates that a small edge gateway can cover an entire charging station, performing tasks such as sensor data collection and image recognition [3] Group 3: Industrial Large Model Deployment - The demand for inference in industrial applications is increasing, leading to the development of edge AI that allows for real-time inference at the edge [4] - Large models require significant computational resources, but they can be more cost-effective in development compared to small models, which need extensive data for training [5] Group 4: Hardware and Chip Development - The integration of powerful AI chips like GPUs, ASICs, and NPUs into edge devices is essential for supporting large models, with companies like Dawning Network and Advantech leading in this area [5][6] - The demand for low-latency, high-efficiency custom chips is rising, with new products being developed to support industrial-grade applications [6]