Objectif du cours

This course introduces the foundations and practices of training modern Large Language Models (LLMs) at scale. Students will learn how deep learning models are trained across multiple GPUs, nodes, and clusters, and why distributed training is the key to enabling today’s largest AI systems.

We will cover:

• Core techniques for distributed training
• Modern frameworks and scaling strategies
• Practical implementations with real-world toolchains
• Theoretical underpinnings of large-scale learning
• Inference and applications

As LLMs grow in complexity and impact, understanding how they are built and deployed has become essential for future researchers and engineers. This series bridges engineering and theory, offering students both the practical skills and deeper insights needed to work with frontier AI systems.