I purchased my budget gaming PC in an emergency. It was a patch job of a machine, and like so many patch jobs, it stuck around far longer than any temporary fix ought to. But, a few weeks ago, I caught a Mac Rumor that the new M2 MacBook Pros would be available shortly.
So, I retired the patch job and upgraded to a M2 Max - largely as a standard of living upgrade. The PC could run Docker or Anaconda - but not both. And it took literally minutes to start a project running Django and React.
Because I’m a nerd, I wanted to do the digital equivalent of a drag race , and ran the same model on various platforms to see how much throwing money improves small things.
So, I ran this model 5 times:
- Cheap desktop CPU-only 
- Using a Nvidia 1060 3gb graphics card 
- Google Colab with an A100-SXM4-40gb ML-focused card 
- Apple M2 Max, CPU-only 
- Apple M2 Max 30-core GPU
There were a couple of interesting learnings here.
First - when dealing with a small dataset, even a cheap graphics card is a dramatic improvement.
Second, considering that A100 card  is 24x the price of the 1060 , I’d expect more than 75% improvement.
Finally, for processing in a laptop, the M2 Max did impressively well. I expect this kind of output matches something like a desktop 3070, albeit in a much smaller package.
Bonus - if you’re currently trying to install the Metal plugin to use Tensorflow   with your brand new M2 machine, the plugin is currently messed up.
Use the following command to get an earlier, working version of Metal.
pip install tensorflow-macos==2.9 tensorflow-metal==0.5.0