This Stanford CS336 course provides a deep technical journey into how modern language models are built from the ground up. It focuses on the full lifecycle of language modeling, starting from basic concepts and progressing toward advanced system-level engineering used in large-scale AI models.
The course begins with an introduction to language modeling and tokenization, explaining how text is processed into machine-readable formats. It then moves into implementation details using PyTorch, resource management, and the practical constraints of training large models.
Students will explore model architectures and hyperparameter design, including advanced concepts like Mixture of Experts, which improves efficiency and scalability in large neural networks. The course also covers GPU utilization, kernel optimization with Triton, and low-level performance engineering.
In addition, learners will study parallelism techniques used to distribute training across multiple devices, as well as scaling laws that explain how model performance improves with size and data. The final lectures focus on inference systems, showing how trained models are deployed and optimized for real-world usage.
By the end of this course, students will understand how language models are designed, trained, optimized, and deployed from scratch, giving them strong foundations in both theoretical and systems-level AI engineering.
