What a GPU Actually Is (and Why ML Stole It)

1. 1Explore the architecture of a GPU to understand its parallel processing capabilities
2. 2Run a simple matrix multiplication on both a CPU and a GPU to compare performance
3. 3Configure your ML model to use CUDA and measure the speedup
4. 4Test the limits of your GPU's memory and compute resources
5. 5Apply GPU acceleration to your ML workflows to improve training times

ML engineers and data scientists can benefit from understanding how GPUs work to optimize their models and workflows

💡 GPUs are designed for parallel processing, making them ideal for matrix operations and other compute-intensive tasks in machine learning

🤖 Did you know a 4096×4096 matrix multiply finishes in 12ms on a GPU but takes 800ms on a CPU? 🚀 Learn why ML loves GPUs!