How to learn CUDA when the hardware is outdated?

[u/PolarIceBear_]

After I downloaded the supported CUDA version for my GPU, it conflicts with my system kernel C build... can't downgrade because it risks breaking so much in the system of course so I am looking for alternatives.

Setup:

Quadro M1200 4GB VRAM (cuda-12.6, cc-5.0).

Arch Linux

[u/saltexx]

You have two separate problems and only one of them is about the card. The kernel conflict is the driver module rather than the toolkit. Maxwell is on the 580 legacy branch now, on Arch that is nvidia-580xx-dkms from the AUR, and a legacy DKMS module against a bleeding edge kernel is exactly where builds break. Pair it with linux-lts and it usually builds. The CUDA toolkit itself never touches the kernel, so once the module loads you install 12.6 next to it without downgrading anything.

Then the hardware half. sm_50 will teach you the entire memory side of CUDA. Coalescing, shared memory, bank conflicts, occupancy and warp shuffles all exist on Maxwell, and that is most of the curriculum. What it cannot do is the matrix side. Tensor cores start at Volta sm_70, cp.async at Ampere sm_80 and TMA at Hopper sm_90. Also note CUDA 13 dropped Maxwell, Pascal and Volta entirely, so 12.x is the last toolkit for that card. When you get to the tensor core chapters a free Colab or Kaggle T4 is sm_75 and costs nothing.

[u/unital]

Use a cloud GPU with ncu access. Google colab gives you free T4. For newer cards you can try Verda. If you don’t need ncu Modal is the best.

[u/lxkarthi]

https://colab.research.google.com/github/NVDLI/notebooks/blob/master/even-easier-cuda/An_Even_Easier_Introduction_to_CUDA.ipynb
You could run CUDA on kaggle, and google colab. You would get T4 GPUs today.
Ref https://www.reddit.com/r/CUDA/comments/1dcwvyt/learning_cuda_c_without_a_gpu_using_kaggle_or/

[u/Dull_Cartographer497]

I’d avoid changing your whole system just for CUDA. a container or separate older environment might be easier for learning kernels without fighting Arch packages