I’d like to self host a large language model, LLM.
I don’t mind if I need a GPU and all that, at least it will be running on my own hardware, and probably even cheaper than the $20 everyone is charging per month.
What LLMs are you self hosting? And what are you using to do it?
I got a home server with a Nvidia Tesla P4, not the most power or the most vram (8gb), but can be gotten for ~$100usd (it is a headless GPU so no video outputs)
I’m using ollama with dolphin-mistral and recently deepseek coder
LLMs use a ton of VRAM, the more VRAM you have the better.
If you just need an API, then TabbyAPI is pretty great.
If you need a full UI, then Oogabooga’s TextGenration WebUI is a good place to start
TinyLLM on a separate computer with 64GB RAM and a 12-core AMD Ryzen 5 5500GT, using the rocket-3b.Q5_K_M.gguf model, runs very quickly. Most of the RAM is used up by other programs I run on it, the LLM doesn’t take the lion’s share. I used to self host on just my laptop (5+ year old Thinkpad with upgraded RAM) and it ran OK with a few models but after a few months saved up for building a rig just for that kind of stuff to improve performance. All CPU, not using GPU, even if it would be faster, since I was curious if CPU-only would be usable, which it is. I also use the LLama-2 7b model or the 13b version, the 7b model ran slow on my laptop but runs at a decent speed on a larger rig. The less billions of parameters, the more goofy they get. Rocket-3b is great for quickly getting an idea of things, not great for copy-pasters. LLama 7b or 13b is a little better for handing you almost-exactly-correct answers for things. I think those models are meant for programming, but sometimes I ask them general life questions or vent to them and they receive it well and offer OK advice. I hope this info is helpful :)
GPT4All is a nice and easy start.
Using Ollama to try a couple of models right now for an idea. I’ve tried to run Llama 3.2 and Qwen 2.5 3b, both of which fits my 3050 6G’s VRAM. I’ve also tried for fun to use Qwen 2.5 32b, which fits in my RAM (I’ve got 128G) but it was only able to reply a couple of tokens per second, thereby making it very much a non-interactive experience. Will need to explore the response time piece a bit further to see if there are ways I can lean on larger models with longer delays still.
