This presentation explores the opportunities offered by the latest GPU technologies for AI and deep learning applications, while addressing the scalability challenges and implementation methods of such systems. It begins with the fundamentals of single-GPU systems in development workflows, then gradually introduces the possibilities for building multi-GPU and multi-node infrastructures. Special emphasis is placed on the importance of modern hardware advancements—such as high-speed interconnects and specialized GPU architectures—that are essential for improving the efficiency of parallel processing. In addition, the talk will cover the integration of software solutions, including distributed computing frameworks, which are indispensable for building scalable AI systems.