To do general purpose GPU calculations on AMD hardware you need a GPU that is supported by ROCm (AMD's equivalent to CUDA). Most of the gaming GPUs are not.

There is a list here but be aware that that is for the latest rocm version, some tools might still use older versions with different supported devices.

https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html#supported-gpus

Latest ollama has support for AMD GPUs. I had to compile from source to make it pick up the GPU on my system.

Look into llamafile. It makes things so easy.

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.

llama.cpp (and ollama) has AMD support through ROCm and also now Vulkan.