I wanted to run LLMs locally on my server (for better privacy), and was wondering if:
I could use Intel ARC/AMD GPUs - these are often less expensive and AMD has open source drivers, which is something I like.
If a PCIe x4 Gen 3 slot would be enough (it’s an x16 slot with x4 speeds) - this is an important consideration.
Would 8GB of RAM (in the GPU, I believe it’s called VRAM?) be enough?
I’m looking at language models to train on my Reddit and Lemmy content, in an aim to make it write like me (and maybe even better than me? Who knows). I don’t quite know which models I will train, or how I will do so (I certainly won’t be writing anything from scratch), but I was wondering; with the explosion of FOSS AI models, maybe something like this would be possible with the hardware constraints I mentioned above?
Does the speed of the connection between the GPU and the CPU really matter in such applications?
Another thing, llama.cpp support offloading layers to gpu, you could try opencl backend for that for non-nvidia gpu’s. But llama.cpp can also run on cpu-only, with usable speed. On my system, it does about 150ms per token on a 13b model.
koboldcpp is probably the most straight forward to get running, since you don’t have to compile, it has a simple UI to set launch parameters, and it also have a web ui to chat with the bot in. And since it use llama.cpp it support everything that does, including opencl (clblast in launcher)
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Another thing, llama.cpp support offloading layers to gpu, you could try opencl backend for that for non-nvidia gpu’s. But llama.cpp can also run on cpu-only, with usable speed. On my system, it does about 150ms per token on a 13b model.
koboldcpp is probably the most straight forward to get running, since you don’t have to compile, it has a simple UI to set launch parameters, and it also have a web ui to chat with the bot in. And since it use llama.cpp it support everything that does, including opencl (clblast in launcher)
Thanks, I’ll take a look