I'm choosing between an AMD 9070 XT and an NVIDIA 5070 Ti for a Linux build. The AMD card is about $250 cheaper, but I want to train neural networks and run local language models as well as do CPU-based tabular work.
Is ROCm a realistic alternative to CUDA for this, or is the NVIDIA premium worth it for compatibility?
For a machine learning build, I'd lean NVIDIA if the extra $250 fits the budget. CUDA has broader support, so you're less likely to find that a library or a training setup assumes a different GPU stack.
That doesn't mean AMD can't train models. It means the compatibility risk is higher, particularly once you move beyond well-supported projects. Check the exact frameworks and versions you need before deciding.
AMD is a reasonable option if your main GPU task is running local text models. ROCm support in a large project like llama.cpp makes that a more approachable use case than a niche training pipeline.
Training and fine-tuning are less straightforward to recommend without knowing the tools involved. Before buying, look up support for the exact card and the versions of the libraries you plan to use. The price saving may be worthwhile if your workload is covered.
I'd separate the Linux desktop question from the compute question. NVIDIA drivers can be installed through a distribution's package manager, though window manager annoyances are still possible. A smoother desktop experience with AMD doesn't automatically mean every ML package will work with ROCm.
12th-floor-interval:
That distinction helps. The desktop issues have been minor, so the compute software is probably the deciding factor.
Neither card choice needs to decide your CPU-based tabular workflow. Focus the comparison on the neural network training and local model tools where you actually want GPU acceleration, then weigh any compatibility gaps against the $250 saving.
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civic-duty-doodler:
Check the specific GPU and Linux distribution too, not just whether a framework has a ROCm option.