What would be the cheapest and most cost-effeciant way of self hosting LLMs
What would be the cheapest and most cost-effeciant way of self hosting LLMs
I've a minipc running an AMD 5700U where I host some services, including ollama and openwebui.
Unfortunately the support of rocm isn't quite there yet and not to mention that of mobile GPUs.
Surprisingly the prompts work when configured to use the CPU, but the speed is just... well, not good.
So, what'd be a cheap and energy efficient setup to run sone kind of LLM for personal use, but still get decent speed?
I was thinking about getting an e-gpu case, but I'm not sure about how solid this would end up.
You're viewing a single thread.
I have my gaming pc running as ollama host when i need it (RX 6700XT with rocm doing the heavy lifting). PC idles at ~50W and draws up to 200W when generating an answer. It is plenty fast though.
My mini pc home server is running openwebui with access to this "ollama instance" but also OpenAIs api when i just need a quick answer and therefor don't turn on my pc.
I have the exact same gpu and tried that. But couldn't get ollama docker version (rocm) to work with the gpu. even changing the env variable to 10.30.1. (rocminfo reports gfx1031)
would you mind giving some instructions or a link?
If you're lucky you just set it to the wrong version, mine uses 10.3.0 (see below).
I tried running the docker container first as well but gave up since there are seperate versions for cuda and rocm which comes packaged with this as well and therefor gets unnecessary big.
I am running it on Fedora natively. I installed it with the setup script from the top of the docs:
curl -fsSL https://ollama.com/install.sh | shAfter that i created a service file (also stated in the linked docs) so that it starts at boot time (so i can just boot my pc and forget it without needing to login).
The crucial part for the GPU in question (RX 6700XT) was this line under the [service] section:
Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"As you stated, this sets the environment variable for rocm. Also to be able to reach it from outside of localhost (for my server):
Environment="OLLAMA_HOST=0.0.0.0"oh man... I'm such a dumb dumb .. didn't even try 10.3.0, now I did and the docker version works and is extremely fast, compared to a CPU... Thank you so much.
Glad i could help ;)
For me it was 0.0.0.0:11434
Just a noob question: any advantage of doing this (except privacy) of using thar setup instead of using chatgpt4 from openai website?
You can get different results, sometimes better sometimes worse, most of the time differently phrased (e.g. the gemma models by google like to do bulletlists and sometimes tell me where they got that information from). There are models specifically trained / finetuned for different tasks (mostly coding, but also writing stories, answering medical questions, telling me what is on a picture, speaking different languages, running on smaller / bigger hardware, etc.). Have a look at ollamas library of models which is outright tiny compared to e.g. huggingface.
Also, i don't trust OpenAI and others to be confidential with company data or explicit code snippets from work i feed them.