Lilly Tech Systems
Learn GPU Programming for AI
Master GPU acceleration for deep learning workloads. From CUDA kernels and cuDNN to PyTorch GPU optimization and multi-GPU training — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why GPUs for AI? CPU vs GPU architecture, parallelism, and the GPU computing ecosystem.
2. CUDA Basics
CUDA programming model, threads, blocks, grids, memory hierarchy, and writing your first kernel.
3. cuDNN
NVIDIA cuDNN library for accelerated convolutions, RNNs, normalization, and attention layers.
4. PyTorch GPU
Moving tensors to GPU, mixed precision training, torch.compile, and profiling with PyTorch.
5. Multi-GPU
DataParallel, DistributedDataParallel, NCCL, NVLink, and scaling across multiple GPUs.
6. Best Practices
Memory optimization, profiling, debugging CUDA, performance tuning, and production deployment.
What You'll Learn
By the end of this course, you will be able to:
Understand GPU Architecture
Grasp how GPU parallelism works and why it accelerates deep learning by orders of magnitude.
Write CUDA Kernels
Build custom CUDA kernels and understand the thread, block, and grid execution model.
Optimize PyTorch Training
Use mixed precision, torch.compile, and GPU profiling tools to speed up model training.
Scale to Multi-GPU
Distribute training across multiple GPUs using DDP, NCCL, and modern scaling techniques.