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?
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
If you don’t need to host but can run locally, GPT4ALL is nice, has several models to download and plug and play with different purposes and descriptions, and doesn’t require a GPU.
I second that. Even my lower-midrange laptop from 3 years ago (8GB RAM, Integrated AMD GPU) can run a few of the smaller LLMs, and it’s true that you don’t even need a GPU as they can run in RAM. And depending on how much RAM you have and what GPU, you might find models performing better in RAM instead of on the GPU. Just keep in mind that when a model says, for example, 8GB Memory required, if you have 8GB RAM, you can’t run it cuz you also have your operating system and other applications running. If you have 8GB video memory on your GPU though, you should be golden (I think).
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.
My (docker based) configuration:
Linux > Docker Container > Nvidia Runtime > Open WebUI > Ollama > Llama 3.1
Docker: https://docs.docker.com/engine/install/
Nvidia Runtime for docker: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
Open WebUI: https://docs.openwebui.com/
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 :)
I am not self hosting an LLM, but running on my laptop with Alpaca. Google’s Gemma 2B. On my hardware its pretty slow, but kind of gets the work done. My hardware is getting old, need to upgrade soon.
I run locally mistral-nemo in my 1070-ti
