

I use a 128GB Framework Desktop. Back when I got it, it was $2,500 with 8TB of SSD storage, but the RAM shortage has driven prices up to substantially more. That system’s interesting in that you can tell Linux to use essentially all of the memory as video memory; it has an APU with unified memory, so the GPU can access all that memory.
That’ll get you 70B models like llama 3-based stuff at Q6_K with 128K of context window, which is the model max. That’s okay for chatbot-like operation, but you won’t want to run code generation with that.
For some tasks, you may be better-off using a higher-bandwidth-but-less-memory video card and an MoE model; this doesn’t keep all of the model active and in video memory, only loading relevant expert models. I can’t suggest much there, as I’ve spent less time with that.
If you don’t care about speed — you probably do — you can run just about anything with llama.cpp using the CPU and main memory, as long as you have enough memory. That might be useful if you just want to evaluate the quality of a given model’s output, if you want to get a feel for what you can get out of a given model before buying hardware.
You might want to ask on !localllama@sh.itjust.works, as there’ll be more people familiar there (though I’m not on there myself).
EDIT: I also have a 24GB Radeon 7900 XTX, but for LLM stuff like llama.cpp, I found the lack of memory to be too constraining. It does have higher memory bandwidth, so for models that fit, it’s faster than the Framework Desktop. In my experience, GPUs were more interesting for image diffusion models like Stable Diffusion — most open-weight image diffusion models are less-memory hungry – than LLM stuff. Though if you want to do Flux v2, I wasn’t able to fit it on that card. I could run it on the Framework Desktop, but at the resolutions I wanted to run it at, the poor ol’ Framework took about 6 or 7 minutes to generate an image.
EDIT2: I use all AMD hardware, though I agree with @anamethatisnt@sopuli.xyz that Nvidia hardware is going to be easier to get working; a lot of the AMD software is much more bleeding edge, as Nvidia got on all this earlier. That being said, Nvidia also charges a premium because of that. I understand that a DGX Spark is something of an Nvidia analog to the Framework Desktop and similar AI Max-based systems, has unified memory, but you’ll pay for it, something like $4k.

















!locallama@sh.itjust.works.
@localllama@sh.itjust.works would be a user named “localllama” rather than a community named “localllama”.