LocalLLaMA
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Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
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Then don't offload! Since its 3000 series, you can run an exl3 with a really tight quant.
For instance, Mistral 24B will fit in 12GB with no offloading at 3bpw, somewhere in the quality ballpark of an Q4 GGUF: https://cdn-uploads.huggingface.co/production/uploads/6383dc174c48969dcf1b4fce/tfIK6GfNdH1830vwfX6o7.png
It's especially good for long context, since exllama's KV cache quantization is so good.
You can still use kobold.cpp, but you'll have to host it via an external endpoint like TabbyAPI. Or you can use croco.cpp (a fork of kobold.cpp) with your own ik_llama.cpp trellis-quantized GGUF (though you'll have to make that yourself since they aren't common... it's complicated, heh).
Point being that simply having an ampere (3000 series RTX) card can increase efficiency massively over a baseline GGUF.
I'll have to check exllama once i build the system, if it can fit a 24B model in 12 gb. It should give me some leeway for 13B ones. Though i feel like i'll need to quantize to exl3 myself for the models i use. Worth a try on a Container though.
Thanks for the tip.
You can definitely quantize exl3s yourself; the process is vram light (albeit time intense).
What 13B are you using? FYI the old Llama2 13B models don’t use GQA, so even their relatively short 4096 context takes up a lot of vram. Newer 12Bs and 14Bs are much more efficient (and much smarter TBH).
right now i'm hopping between nemo finetunes to see how they fare. i think i only ever used one 8B model from Llama2, the rest is been all Llama 3 and maybe some solar based ones. unfortunately i have yet to properly dig into the more technical side of llms due to time contraints.
so long as it's not interactive i can always run it at night and make it shut off the rig when it's done. power here is cheaper at night anyways :-)
thanks for the info (and sorry for the late response, work + cramming for exams turned out to be more brutal than expected)
Yeah it’s basically impossible to keep up with new releases, heh.
Anyway, Gemma 12B is really popular now, and TBH much smarter than Nemo. You can grab a special “QAT” Q4_0 from Google (that works in kobold.cpp, but fits much more context with base llama.cpp) with basically the same performance as unquantized, would highly recommend that.
I'd also highly recommend trying 24B when you get the rig! It’s so much better than Nemo, even more than the size would suggest, so it should still win out even if you have to go down to 2.9 bpw, I’d wager.
Qwen3 30B A3B is also popular now, and would work on your 3770 and kobold.cpp with no changes (though there are speed gains to be had with the right framework, namely ik_llama.cpp)
One other random thing, some of kobold.cpps sampling presets are very funky with new models. I’d recommend resetting everything to off, then start with like 0.4 temp, 0.04 MinP, 0.02/1024 rep penalty and 0.4 DRY, not the crazy high temp sampling they normally use, with newer models than llama2.
I can host specific model/quantization on the kobold.cpp API to try if you want, to save tweaking time. Just ask (or PM me, as replies sometimes don’t send notifications).
Good luck with exams! No worries about response times, /c/localllama is a slow, relaxed community.