gpu-mode/lectures resource

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6505 stars · 649 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of supplementary materials (notebooks, slides, and code) for the GPU MODE lecture series on GPU programming and CUDA kernel development. It covers topics from CUDA fundamentals to advanced techniques like Flash Attention, quantization, and sparsity.

Use cases

• learn CUDA programming from scratch
• optimize PyTorch kernels with CUDA or Triton
• understand GPU memory and compute architecture
• implement flash attention and ring attention
• profile and speed up deep learning workloads on GPUs
• study quantization and sparsity techniques for LLMs

When to choose

• you want free, hands-on lecture material with runnable notebooks for GPU programming
• you are a Python developer learning to write and optimize CUDA kernels
• you want guided deep dives into attention mechanisms and PyTorch performance

When to avoid

• you need production-ready GPU libraries rather than educational material
• you are looking for a tool or framework rather than tutorials
• you need structured courses with certifications or instructor support

Facets

learning-resource · maturity active

gpu-computing machine-learning developer-tools gpu-computing deep-learning tutorials education python cross-platform cuda triton pytorch jupyter-notebooks kernel-optimization lectures flash-attention profiling gpu

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markdown · JSON · MCP: product_card(name="gpu-mode/lectures")