On my machine I’m running opensuse tumbleweed and has the amdgpu driver installed. I use it for gaming and recently I’ve become interested in running LLMs. So I would like to keep a balance of both without compromising too much on performance.
I know that there are proprietary drivers for AMD cards but I’m hesitant to install it as I’ve heard that it performs less efficiently in games when compared to the open source driver.
I’m mainly confused about this ROCM thing. Is it not included with the opensource amdgpu drivers ? Or is it available as a separate package?
So what driver to use ?
Or perhaps, is it possible to run oogabooga or stable diffusion within a distrobox container (with the proprietary drivers) and still keep using the open source gpu drivers for the Host operating system.
Basically, RoCM and CUDA allows one to do math on the GPU. Most Linear Algebra operations (i.e. LLM or NNs and ML generally) can be parallelized over a GPU which is much more performant than CPU.
To perform calculations on GPU, one needs some sort of interface to to their programming language of choice, NVIDIA has CUDA which is in CPP with bindings to python: (pytorch, Tensorflow etc. ), Julia: Flux etc.
RoCM is AMDs solution, there bindings are young and not widely implemented.
My advice, play around with Flux RoCM and PyTorch RoCM just to get an idea. Suffice it to say, when I started doing RL and LLMs more seriously I gave up my colab and sold my AMDs to fund a 3060.