GPU Mode Lectures is a collection of educational material and supplementary resources for learning GPU programming and optimization.
The project addresses the challenge of learning practical GPU programming by providing structured lecture materials covering CUDA kernel development, PyTorch integration, memory architecture, and performance optimization. The approach combines video lectures with accompanying Jupyter notebooks, slides, and code examples that demonstrate concepts ranging from foundational CUDA programming to advanced techniques like Flash Attention and Ring Attention. Materials reference the PMPP book as a theoretical foundation while providing hands-on implementations.
The collection suits developers who want to move beyond introductory GPU programming toward production-ready optimization work. It covers both breadth and depth: early lectures establish CUDA fundamentals and profiling techniques, while later ones tackle specialized topics like quantization, sparsity, and attention mechanisms. The materials work well for self-paced learning through Jupyter notebooks, with some lectures offering Colab versions for immediate experimentation. This is most valuable for those already comfortable with Python and PyTorch who want to understand GPU-level performance characteristics and implementation details.
The project maintains an active lecture series with materials spanning foundational CUDA concepts through advanced optimization techniques. Notebooks and code examples are provided alongside slides for most lectures, enabling learners to experiment with implementations discussed in videos. The repository includes a Discord community link, indicating ongoing engagement with learners. Materials cover both theoretical foundations and practical implementation patterns, with recent lectures addressing contemporary techniques in the GPU computing landscape.
