Selfhosted

37923 readers

467 users here now

A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.

Rules:

Be civil: we're here to support and learn from one another. Insults won't be tolerated. Flame wars are frowned upon.
No spam posting.
Posts have to be centered around self-hosting. There are other communities for discussing hardware or home computing. If it's not obvious why your post topic revolves around selfhosting, please include details to make it clear.
Don't duplicate the full text of your blog or github here. Just post the link for folks to click.
Submission headline should match the article title (don’t cherry-pick information from the title to fit your agenda).
No trolling.

Resources:

Any issues on the community? Report it using the report flag.

Questions? DM the mods!

founded 1 year ago

MODERATORS

HybridSarcasm@lemmy.world

HybridSarcasm@lemmy.hybridsarcasm.xyz

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

submitted 1 month ago by robber@lemmy.ml to c/selfhosted@lemmy.world

22 comments fedilink hide all child comments

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?

you are viewing a single comment's thread
view the rest of the comments

[–] Audalin@lemmy.world 10 points 1 month 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.

[–] robber@lemmy.ml 4 points 1 month 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@lemmy.world 7 points 1 month ago* (last edited 1 month 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.

[–] robber@lemmy.ml 3 points 1 month 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@lemmy.world 5 points 1 month 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.