International Journal of Science, Engineering and TechnologyAuthors: Sasane Sakshi Shirish, Professor Bhosale S.B, Professor Bangar A.P, Professor Khatri V.A
Abstract: AI PCs and on-device artificial intelligence are changing how everyday computers use AI. Instead of sending every request to a remote cloud server, a device can process suitable tasks locally using its CPU, GPU, and specialised neural processing unit (NPU). This can reduce response time, limit data transfer, support some functions without a stable internet connection, and give users more control over personal data. The practical value of these benefits depends on the task, the model, and the computer's hardware. Local processing also has to work within limits such as memory capacity, power consumption, heat, and model size. This review discusses three research works: a comprehensive survey of on-device AI models, a study proposing a collaborative consumer edge-AI ecosystem, and research on on-device AI for smart environments and Industry 4.0. The papers are compared by focus, approach, contribution, and limitation. The review identifies model optimisation, efficient hardware use, privacy-aware collaboration, and energy management as important areas. It also finds that the selected works do not establish a common benchmark specifically for AI PCs. A task-aware local/cloud architecture is proposed for future study; it has not been experimentally tested.
