I have a Strix Halo based HP ZBook G1A and it's been pretty easy getting local models to run on it. Training small LLMs on it has been a bit harder but doable as well. Mind you, I 'only' have 64 GB with mine.
Under Linux, getting LM Studio to work using the Vulkan backend was trivial. Llama.cpp was a bit more involved. ROCm worked surprisingly well with Arch — I would credit the package maintainers. The only hard part was sorting out Python packaging for PyTorch (use local packages with system's ROCm).
I wouldn't say it's perfect but it's definitely not as bad as it used to be. I think the biggest downside is the difference in environment when you use this as a dev machine and then run the models on NVIDIA hardware for prod.
Can you share a bit more on the small LLMs you've trained? I'm interested in the applicability of current consumer hardware for local training and finetuning.
I'm not the AI expert in the company but one of my colleagues creates image segmentation models for our specific use case. I've been able to run the PyTorch training code on my computer without any issues. These are smaller models that are destined to run on Jetson boards so they're limited compared to larger LLMs.
edit: just to be clear, I can't train anything competitive with even the smallest LLMs.
Under Linux, getting LM Studio to work using the Vulkan backend was trivial. Llama.cpp was a bit more involved. ROCm worked surprisingly well with Arch — I would credit the package maintainers. The only hard part was sorting out Python packaging for PyTorch (use local packages with system's ROCm).
I wouldn't say it's perfect but it's definitely not as bad as it used to be. I think the biggest downside is the difference in environment when you use this as a dev machine and then run the models on NVIDIA hardware for prod.