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Any of you have a self-hosted AI "hub"? (e.g. for LLM, stable-diffusion, ...) (lemmy.ml)

submitted 7 months ago by [email protected] to c/selfhosted

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I've been looking into self-hosting LLMs or stable diffusion models using something like LocalAI and / or Ollama and LibreChat.

Some questions to get a nice discussion going:

Any of you have experience with this?
What are your motivations?
What are you using in terms of hardware?
Considerations regarding energy efficiency and associated costs?
What about renting a GPU? Privacy implications?

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[–] Audalin 10 points 7 months ago (1 children)

Have been using llama.cpp, whisper.cpp, Stable Diffusion for a long while (most often the first one). My "hub" is a collection of bash scripts and a ssh server running.

I typically use LLMs for translation, interactive technical troubleshooting, advice on obscure topics, sometimes coding, sometimes mathematics (though local models are mostly terrible for this), sometimes just talking. Also music generation with ChatMusician.

I use the hardware I already have - a 16GB AMD card (using ROCm) and some DDR5 RAM. ROCm might be tricky to set up for various libraries and inference engines, but then it just works. I don't rent hardware - don't want any data to leave my machine.

My use isn't intensive enough to warrant measuring energy costs.

[–] [email protected] 4 points 7 months ago (1 children)

So you access the models directly via terminal? Is that convenient? Also, do you get satisfying inference speed and quality with a 16GB card?

[–] Audalin 7 points 7 months ago* (last edited 7 months ago) (1 children)

Mostly via terminal, yeah. It's convenient when you're used to it - I am.

Let's see, my inference speed now is:

~60-65 tok/s for a 8B model in Q_5_K/Q6_K (entirely in VRAM);
~36 tok/s for a 14B model in Q6_K (entirely in VRAM);
~4.5 tok/s for a 35B model in Q5_K_M (16/41 layers in VRAM);
~12.5 tok/s for a 8x7B model in Q4_K_M (18/33 layers in VRAM);
~4.5 tok/s for a 70B model in Q2_K (44/81 layers in VRAM);
~2.5 tok/s for a 70B model in Q3_K_L (28/81 layers in VRAM).

As of quality, I try to avoid quantisation below Q5 or at least Q4. I also don't see any point in using Q8/f16/f32 - the difference with Q6 is minimal. Other than that, it really depends on the model - for instance, llama-3 8B is smarter than many older 30B+ models.

[–] [email protected] 3 points 7 months ago (1 children)

Thanks! Glad to see the 8x7B performing not too bad - I assume that's a Mistral model? Also, does the CPU significantly affect inference speed in such a setup, do you know?

[–] Audalin 5 points 7 months ago

If your CPU isn't ancient, it's mostly about memory speed. VRAM is very fast, DDR5 RAM is reasonably fast, swap is slow even on a modern SSD.

8x7B is mixtral, yeah.