Hello friends,
I’m pretty deep into self-hosting - especially on the home automation side. I’ve got a couple of options for self-hosted AI, but I don’t think they’ll meet my long term goals:
-
Coral TPUs: I have 2x processing my Frigate data. These seem fine for that purpose, but not useful for generative AIs?
-
Jetson Nano: Near as I can tell nothing supports these things except DeepStack, which appears to be abandoned. Bummed these haven’t gotten broader support in the community.
I’ve got plenty of rack space and my day job is managing thousands of machines, so not afraid of a more technical setup.
The used NVIDIA rack mounted Tesla GPU servers look interesting. What are y’all using?
Requirements:
- Rack mounted
- Supports local LLM and GenAI
- Linux-based
- Works with Docker
My friend did this with a RTX 3060 12GB, and documented the process in this Octopusx blog post
If you have any questions we’d be happy to help
Don’t have direct experience with either, but:
It’s my understanding that a corral tpu is exclusively an inference accelerator, no training or more generative applications. Also, corral TPUs are a little bit unobtainium, with the only options I’ve seen behind scalped about as much as a pi, to basically the same result.
I think you’re overthinking the nano a bit. I’m not sure that you’d need explicit support for the nano, because it’s just a cuda gpu and so it should^TM just run anything cuda, as long as the arm cpu doesn’t trip the software up . For example, I’ve seen people running blender renders across a cluster of jetsons, just because, and I doubt that blender has any explicit support for jetsons.
If you’re coming at it from the sense that you have rack space to spare, a used Tesla / Quadro gpu would probably be better value than a jetson nano OG, because those were I think 2GB/4GB and 256 Kepler era cuda cores. You’d almost have to go out of your way to find a worse PCIe card, plus a normal PCIe card in a normal x86 server wouldn’t have arm software restrictions. Although as the other commenter mentioned, cooling/power draw is a more serious consideration for a PCIe card, plus the risks of buying used.
I totally agree on the Coral TPUs. Great for Frigate, but not much else. I’ve got 2x of the USB ones cranking on half a dozen 4K stream - works wonderfully.
And I agree in theory these Nanos should be great for all sorts of stuff, but nothing supports them. Everything I’ve seen is custom one offs outside of DeepStake (though CodeProject.AI purports there’s someone now working on a Nano port).
Sounding like a decent gaming GPU and a 2-3U box is the ticket here.
The best consumer NVIDIA card is the 3090ti because of its 24GB memory, so you can run bigger LLM models. I have a 3060ti 12GB which works pretty well with 7B and 13B LLM models.
A 4090 is good enough for running many models. You probably want an A6000 for larger ones. But many models that don’t fit in your VRAM can be scaled down without much loss of effectiveness.
Nvidia is the only game in town right now. I decided on a 3090 for the time being, with the option of adding another one later. I think in two years we will have 100x better options specifically tailored for AI.