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If you have a good enough NVIDIA card, probably a 1080ti or better, download KoboldCPP and a .gguf model from huggingface and run it locally.
The quality is directly tied to your GPU's vram size and how big of a model you can load into it, so don't expect the same results as an LLM running on a data center. For example, I can load a 20gb gguf model into a 3090 with 24gb of vram.
Actually not 100% true, you can offload a portion of the model into ram to save VRAM to save money on a crazy gpu and still run a decent model, it just takes a bit longer. I personally can wait a minute for a detailed answer instead of needing it in 5 seconds but of course YMMV
Is there a general term for the setting that offloads the model into RAM? I'd love to be able to load larger models.
I thought CUDA was supposed to just supposed to treat VRAM and regular RAM as one resource, but that doesn't seem to be correct.
Ollama does that by default, but prioritizes gpu above regular ram and cpu. In fact, it's other feature that often doesn't work, cause they can't fix the damn bug that we reported a year ago -
mmap. That feature allows you to load and use model directly from disk (alto, incredibly slow, but allows to run something like deepseek that weight ~700gb with at least 1-3 token\s).num_gpu allows you to specify how much to load into GPU vram, the rest will be swapped to regular RAM.