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Accelerate your Numpy Scientific Workflows on Apple Silicon with MLX In this video, I compare the execution speed of numpy and numba with MLX, a python library that executes code on Mac GPUs and provides a numpy-compatible API. For my tests, I simulate a large number time series that follow an AR(3)-GARCH(1,1) process and compute the t-stat for the H0 that mean returns are 0 for each sample path. Find out how MLX performs in comparison to numpy and numba! Note: MLX requires Apple Silicon (M1/M2/M3). 👍 Please like if you found this video helpful, and subscribe to stay updated with my latest tutorials. 🔔 The sample code used in this video is available on GitHub: https://github.com/Vincent-Codes-Finance/mlx-vs-numba The written version of this tutorial is available at: https://vincent.codes.finance/posts/apple-mlx/ 🔖 Chapters: 00:00 Intro 00:37 What is MLX? 04:00 Sample numpy workflow 06:18 MLX code 08:32 Numba code 10:52 Benchmarking 13:57 Benchmark results 17:19 Outro Video links: MLX: https://ml-explore.github.io/mlx/build/html/index.html numpy: https://numpy.org/ numba: https://numba.pydata.org/ 🐍 More Vincent Codes Finance: - ✍🏻 Blog: https://vincent.codes.finance - 🐦 X: https://twitter.com/CodesFinance - 🧵 Threads: https://www.threads.net/@codesfinance - 😺 GitHub: https://github.com/Vincent-Codes-Finance - 📘 Facebook: https://www.facebook.com/people/Vincent-Codes-Finance/61559283113665/ - 👨‍💼 LinkedIn: https://www.linkedin.com/company/vincent-codes-finance/ - 🎓 Academic website: https://www.vincentgregoire.com/ #numba #mlx #numba #pandas #python #bigdata #research #researchtips #jupyternotebook #vscode #professor #pandas #finance #datascience #dataanalytics #dataanalysis Key Highlights & Overview: Understanding <strong>Python Numpy For Gpu</strong> is essential for modern technical workflows and software skills. Follow along with top-rated video tutorials from industry experts today on <strong>TutorTube</strong>.