this post was submitted on 24 Jun 2026
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Do you host your own ML / AI / LLM? What do you use, and what do you use it for?

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[–] brucethemoose@lemmy.world 70 points 2 weeks ago* (last edited 2 weeks ago) (7 children)

An aside for anyone reading this:

https://sleepingrobots.com/dreams/stop-using-ollama/

And that barely scratches the surface. Please.

Use anything but Ollama. Even APIs.

[–] SuspiciousCarrot78@aussie.zone 13 points 2 weeks ago (2 children)
[–] tristynalxander@mander.xyz 4 points 2 weeks ago (1 children)

It's not that hard to use llama.cpp directly anyway. Why would I use a wrapper when I can just run a python script?

[–] BlackLaZoR@lemmy.world 2 points 1 week ago* (last edited 1 week ago)

I use LMStudio, because it has quality of life improvements like nice GUI and huggingface search engine. Also they have Vulkan backend that at least on 7900XTX is ~10% faster than rocm (on LLama 3 8b Q4_0 it gets 115Tokens/s vs 105 on rocm)

[–] brucethemoose@lemmy.world 3 points 2 weeks ago* (last edited 2 weeks ago)

Or exllama! Vllm, sglang, Lorax. Koboldcpp, Aphrodite, text-generation-webui, LM Studio, powerinfer, ktransformers, mlc-LLM, really whatever floats your boat. Just not ollama, specifically.

[–] plasma8726@lemmy.today 10 points 2 weeks ago (1 children)

Thanks for this link. Because of this article, I had claude stand up a llama.cpp container next to my already running ollama container. It ran side by side tests with the same model and parameters, and the results blew ollama out of the water. I'm in the process of moving hermes and openwebgui over to the llama.cpp instance to see how it goes day to day.

[–] brucethemoose@lemmy.world 1 points 2 weeks ago* (last edited 2 weeks ago) (1 children)

If you’re using docker anyway, and “fast” pure GPU models, you might try a vllm container while you’re at it.

It should be much faster than even llama.cpp, albeit at the cost of context length, and it supports some exotic 4-bit quantization like SPQA.

Same with TabbyAPI. It’s quantization is SOTA, though it does not support CPU offloading, and it’s speed is somewhere between vllm and llama.cpp.

[–] plasma8726@lemmy.today 1 points 2 weeks ago (1 children)

Thanks! I'll look into this. I'm a bit limited at 12GB of VRAM right now.

[–] brucethemoose@lemmy.world 1 points 2 weeks ago* (last edited 2 weeks ago)

A 3060?

Exllama/TabbyAPI is still worth looking at if you are trying to run a model purely in GPU RAM. It’s easily the most VRAM efficient backend, it just doesn’t support CPU offloading (which is useful for MoEs if you have considerable spare CPU RAM) and more optimized for 4xxx and up Nvidia cards.

And TabbyAPI has a docker container you can use. Look for “exl3” models on huggingface.

[–] pinball_wizard@lemmy.zip 9 points 2 weeks ago

I agree that the concerns listed there are smells, and I wasn't aware of some of the options listed there.

Thank you for sharing this!

[–] vagabond@lemmy.dbzer0.com 5 points 2 weeks ago

Didn't know this. Going to switch this weekend, thanks for sharing this!

[–] Kroko@feddit.online 1 points 1 week ago

Thanks. Good to know