Does anyone run their own AI service?
If so,
Which one?
What hardware?
Do you have a way to access it from outside or from a phone?
And, if you’re open to sharing, what do you do with it?
I know AI-enabling hardware is insanely expensive right now, but some of the smaller (<32GB) more efficient models (think deepseek- or qwen-based) don’t absolutely need specialized hardware to run and perform well enough for many purposes.
I am so aware of the absolutely abysmal privacy practices of basically all AI companies that I’m not comfortable using them to help me with web searches and the such, because there’s no way that all this information won’t be going into a profile. This makes me want to have my own “helper” service.



I run lemonade on my strix halo box, I change the models I use, but the qwens are pretty good.
I access it via Tailscale on my phone, then I have a page I can use. The Lemonade interface forces you into the app, so you’ll want some other interface for your LLMs, as the app is junk.
I used to run llama.cpp and llama swap, but lemonade combines them and makes managing different ai backends easier.
I’ve been eyeing lemonade as I am on llama and swap my self. How much of a pita was the migration. I’m a lazy bastard.
Lemonade is pretty good, especially for AMD GPUs and the lazy, but it’s pretty opinionated (like ollama but not as toxic) and it’s not easy to directly control llama-server parameters. It feels a bit commercial / we want to IPO.
Seeing as you already have your parameters, llama-swap might be a better fit, lets you configure llama-server directly in a yaml, and switch between them. You can even have multiple versions of the same model (e.g. thinking or not, try out different parameters) and change without reloading.
Both work fine with e.g. Open WebUI and Pi (lemonade its own integration and llama-server needs an extension).
I pointed lemonade at my llama.cpp model folder but it uses json files to configure them. I think it was fine for importing them or you could have an agent write them for you. After that you can manage models inside the app
Overall it wasn’t a huge deal, it’s easier than managing llama.cpp directly with rocm updates.