Explore a conference talk from JuliaCon 2024 that delves into the GPU acceleration capabilities of Julia's Scientific Machine Learning (SciML) ecosystem. Learn how SciML provides a comprehensive suite of numerical solvers comparable to SciPy and MATLAB, while offering unique advantages through GPU integration and machine learning compatibility. Discover the automated process of converting CPU-based models to GPU-based implementations, understanding why this approach achieves significant performance improvements of 20x-100x over PyTorch and Jax for ODE solvers. Gain insights into ongoing developments for accelerating small optimization problems that traditionally lack parallelization options. The 29-minute presentation demonstrates how Julia's ecosystem stands out by combining standard numerical solving capabilities with advanced GPU toolsets under a unified interface.