This tutorial focuses on the computational aspects of training and deploying large-scale AI models using high-performance computing (HPC). Topics include distributed training, multi-GPU and multi-node acceleration, mixed-precision optimization, efficient data handling, and resource management on supercomputing clusters. Special attention is given to transformer-based architectures, self- and semi-supervised learning, and energy-efficient AI practices. Participants will gain both theoretical insights and hands-on experience with frameworks such as PyTorch and DeepSpeed, learning best practices for scalable and sustainable AI research on HPC infrastructures.