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FauxLiving@lemmy.world 3 weeks agoYeah, you do want more contextual intelligence than an 8B for this.
Oh yeah, I’m sure. I may peek at it this weekend. I’m trying to decide if Santa is going to bring me a new graphics card, so I need to see what the price:performance curve looks like.
Massive understatement!
I think I stopped actively using image generation a little bit after LoRAs and IP Adapters were invented. I was trying to edit a video (random meme gif) to change the people in the meme to have the faces of my family, but it was very hard to have consistency between frames. Since there is generated video, it seems like someone solved this problem.
brucethemoose@lemmy.world 3 weeks ago
Oh yes, it has come a LOONG way. Some projects to look at are:
github.com/ModelTC/LightX2V
github.com/deepbeepmeep/Wan2GP
And for images: github.com/nunchaku-tech/nunchaku
I dunno what card you have now, but hybrid CPU+GPU inference is the trend days.
As an example, I can run GLM 4.6, a 350B LLM, with measurably low quantization distortion on a 3090 + 128GB CPU RAM, at like 7 tokens/s.
You can easily run GLM Air on like a 3080 + system RAM, or even a lesser GPU. You just need the right software and quant.
FauxLiving@lemmy.world 3 weeks ago
Thanks a ton, saves me having to navigate the slopped up search results (‘AI’ as a search term is SEOd to death and back a few times)
That system has the 3080 12GB and 64GB RAM but I have another 2 slots so I could go up to 128GB. I don’t doubt that there’s a GLM quant model that’ll work.
Is ollama for hosting the models and LM Studio for chatbot work still the way to go? Doesn’t seem like there’s much to improve in that area once there’s software that does the thing.
brucethemoose@lemmy.world 3 weeks ago
And IMO… your 3080 is good. It’s very well supported. It’s kinda hard to upgrade, in fact, as realistically you’re either looking at a 4090 or a used 3090.
brucethemoose@lemmy.world 3 weeks ago
Oh no, the software is everything, and ollama is awful. It’s enshittifying: don’t touch it with a 10 foot pole.
Speeds are basically limited by CPU RAM bandwidth. Hence you want to be careful doubling up RAM, and doubling it up can the max speed (and hence cut your inference speed).
Anyway, start with this. Pick your size, based on how much free CPU RAM you want to spare:
huggingface.co/ubergarm/GLM-4.5-Air-GGUF
The “dense” parts will live on your 3080 while the “sparse” parts will run on your CPU. The backend you want is this, specifically the built-in llama-server:
github.com/ikawrakow/ik_llama.cpp/
Regular llama.cpp is fine too, but it’s quants just aren’t quite as optimal or fast.
It has two really good built-in web UIs: the “new” llama.cpp chat UI, and mikupad, which is like a “raw” notebook mode more aimed at creative writing. But you can use LM Studio if you want, or anything else; there are like a bazillion fronted out there.