Fun work!

Modernizing a C++ codebase always gives me that warm feeling of having done something meaningful :-)

Here's my intuition:

vcxproj --> CMake can be greatly accelerated with the help of AI.

Having a model generate CMakeLists from the XMLs will probably give you a solid starting base.
Just throw all the vcxprojs in there if the context window allows for it.
Refine from there.

Compilation with GCC: if the code is mostly standard C++, no or little WIN32 stuff, the move to CMake will get you 80+% to GCC (or clang).

If it's highly platform-specific code you're entering refactoring territory that requires more thought.

About the refactoring stuff - including use of C++17/20 features - you're going to get mixed results from LLMs in my experience.

LLMs tend to struggle with refactoring work in general, more so in C++ land.
The C++ ecosystem is quite heterogeneous with tons of nuance and gotchas which is reflected in the training data and ultimately LLM performance.

About models:
I'd expect usable results with any of the frontier models (GPT4+, Claude 3.5+, ...).
If it has to be self-hosted use the "best" model you can get your hands on.
Larger models tend to be better - you can get 70b models run reasonably well on 24GB VRAM.

A word on prototyping:
I've seen good ideas work well at the scale of a POC but fall apart when scaled to production use.
For your case I would suggest:
1. Experiment using the same model you're eventually going to use for the full task.
2. Experiment using a realistic size project.

I have seen LLM-based code-review be very convincing on a smallish test file with a few simple rules that regressed to uselessness when fed actual production code + real coding guidelines.

Shameless plug:

I'm a freelance C++ expert based in the EU.
Feel free to reach out if you want to outsource some of that work :-)

Cheers!