![m1 max for mac mini m1 max for mac mini](https://techsviewer.com/wp-content/uploads/2021/01/LG-32UL950-W-Monitor.jpg)
Naturally, more memory always helps when working with large datasets, so if you already have a Mac with the original M1 that supports 8–16GB of RAM, it might be worth an upgrade to the M1 Pro Max.Ĭurrently, I have a Mac mini (M1, 2020) and it works fine for me. RAM: The MacBook Pro with M1 Pro comes with 16GB of RAM, while the MacBook Pro with M1 Pro Max comes with 32GB of RAM.(keep in mind that people see a 20–30% performance penalty when running x86–64 programs with Rosetta2 compared to native ARM64) Who knows how much time it will take the PyData ecosystem to catch up with the Apple Silicon. However, that option doesn't help run Python natively on the M1 Macs. ARM64 package availability: Currently, the best option to run Python on the new Macs is through Rosetta 2.Data science libraries such as TensorFlow and PyTorch benefit from more CPU cores, so the upgrade from 4 high-performance CPU cores in the original M1 to 8 in the new M1 Pro/Max will be definitely good for doing data science tasks. High-performance cores: The new M1 Pro and M1 Max support eight high-performance and two low-power cores.The M1 Pro and Max are very efficient with their resources, but is it a good fit for data science activities? Stanley Seibert (Anaconda) Pros and Cons of The M1 for Data Science Apple has an alpha port of TensorFlow that uses ML Compute, and maybe other projects will be able to take advantage of Apple hardware acceleration in the coming years. Apple offers APIs like Metal and ML Compute which could accelerate machine learning tasks, but they are not widely used in the Python ecosystem. The most exciting development will be when machine learning libraries can start to take advantage of the new GPU and Apple Neural Engine cores on Apple Silicon. Truth is, the original M1 Macs did not offer anything that especially benefits data scientists and things haven’t changed much with the new M1 Pro and M1 Max chips (we’ll check the technical specifications in the next section).įortunately, according to the Anaconda website, there may be some coming benefits in the future. That said, those aren’t exclusive benefits for data science. Also, MacBooks with M1 Pro and Max have excellent battery life. Scores for the M1 Max show a single-threaded score in the 1,700 to 1,800 range and a multi-threaded score of 11,000 to 12,000 (this is 15% higher than the fastest Intel laptops).
![m1 max for mac mini m1 max for mac mini](https://cdn.mos.cms.futurecdn.net/GkRo7cZPYvVsSJLSj6rCZW-1200-80.jpg)
Some of the benefits that the M1 Pro and Max chips have are the high single-thread performance. Current and (Possible) Future Benefits for Data Science Let’s find out whether it’s worth acquiring the new MacBook. That said, if you frequently work with tools used in data science, you should think twice about buying the latest computer, migrating, or even updating your operating system (I learned this the hard way after updating to macOS Catalina and suddenly losing Anaconda) I’m a happy owner of a Mac with the original M1 and I have to say that it’s impressive what it’s capable of - I can’t even imagine how great it would be to have the new MacBook Pro with M1 Pro or M1 Max. They are supposed to be faster and more powerful than the original M1, but what does that mean for data scientists? As you might know, Apple released its new MacBook Pro with M1 Pro and M1 Max.