Otherwise, get a pre-owned GTX950 (one that doesn't require external power supply) and a TB3 to PCI-E x16 adapter. Not enclousure, adapter. Should cost you around $200 all in IIRC. And it allows you to upgrade the card furthur down the line since most of the cost is the adapter.
https://developer.nvidia.com/embedded/jetson-nano-developer-...
Here[4] is a notebook that shows how to install CUDA into an environment using the GPU accelerated runtime.
Only major downside is that resources aren't guaranteed (see first section under "Resource Limits" here[5]), so you sporadically may not be able to start a GPU-accelerated runtime session. But that shouldn't be much of a blocker for tinkering purposes.
[1] https://colab.research.google.com/notebooks/intro.ipynb
[2] https://colab.research.google.com/notebooks/gpu.ipynb
[3] https://colab.research.google.com/notebooks/tpu.ipynb
[4] https://colab.research.google.com/github/ShimaaElabd/CUDA-GP...
Its quite easy to set up as well, basically a workstation that you can just connect to with remote desktop, but migrate the hardware it runs on.