Is there a way to find the max GPU memory watermark? How to run locally with minimal setup

According to the README.md, the memory goes as follows:

Maximum length limits depends on free GPU provided by Google-Colab fingers-crossed

For GPU: Tesla T4 or Tesla P100 with ~16G the max length is ~1400
For GPU: Tesla K80 with ~12G the max length is ~1000
To check what GPU you got, open a new code cell and type !nvidia-smi

I am interested in structures of around either (a) one single chain of 240-280aa or around (b) 2 different chains of ~120 + ~140aa. What would be the minimal GPU that would allow us to run this locally?

I am thinking that given our own custom MSAs, it wouldn't need to connect to MMSeqs2 or download the 2Tb of sequence data, thus going straight into running the prediction based on the MSA of internal data on the docker container?

Or am I missing something obvious that would still require Colab or something else remote?

Start with README.md and inspect the Docker-based execution path referenced in the issue, including how custom MSAs are supplied and where GPU requirements are determined. Document the minimum local GPU and setup for the stated chain lengths, and clarify whether MMseqs2, Colab, or remote services remain necessary.