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

submitted 5 months ago by [email protected] to c/[email protected]

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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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[–] [email protected] 4 points 5 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?

[–] [email protected] 7 points 5 months ago* (last edited 5 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 5 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?

[–] [email protected] 5 points 5 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.