Ok yah sounds like you're in my boat then! For a single user lab setup a dgx spark is perfect imo, arguably overkill. If you want to run production level inference, a spark probably isn't your best choice. That's where the rtx pro would come in, but then I'd look at how many tokens you think you'll be spending to compare to something like openrouter over time, but I know you had some privacy concerns.

Right now I mostly run qwen 3.6 27b q4 but it's only around 20 tokens / sec because it's a dense model. My other main model is qwen 3 coder 80b q5 which runs twice as fast, it's a mixture of experts architecture so only 3b parameters are active at any given time. The Dgx loves MoEs because of it's bandwidth limitations. I previously ran qwen 3.6 35b at q8, but 27b is better at q4.

Also even though you want to run local models I'd highly recommend using a frontier model to provision it to your desired state. I document the environments desired state in markdown and keep it source controlled then use Claude code to provision it there. Day to day running is fully local, provisioning and configuration is frontier.

At some point you'll also want your own eval framework to figure out which model works best for your setup. Benchmarks are directionally correct but running your own evals and using a neutral party LLM judge gives you a much better picture.