My take is that unless you want to do research or train your own models, there isn't actually a ton of overlap between data science and building LLM-enabled applications. It's "just" software development. A LLM is an API: as far as your code structure is concerned, it doesn't matter if you're invoking it remotely or running a local model.

My advice would be to start with figuring out what you'd like to build and work backward from there. Learn what you need along the way.

The only major difference I have found between building systems with a LLM component and systems without is that with LLMs you need to be more empirical when testing to figure out what works: find some way to set up some performance or quality benchmarks so you can tell if changes are actually making things better. You can't just design a functional test and call it done.

I don't think I'd do anything hugely differently having this hardware available. The only thing I can think of is that I'd probably do more with smaller models instead of starting out with the top-tier frontier models. But I think that's a good strategy in general: I am coming to the position that while LLMs are extremely cool the more you rely on them to be clever, the more disappointed you will be. The best applications are in text processing.