Running AI models on edge devices brings several key advantages. By processing data locally rather than relying solely on centralized cloud infrastructure, latency is significantly reduced and real‐time decision making is enabled. In addition, edge deployments improve data privacy and lower bandwidth requirements since sensitive information does not have to travel over networks. This localized processing also enhances system scalability and reliability, making it ideal for applications in smart homes, autonomous vehicles, and industrial IoT settings.