Unfortunately that’s not easily doable, as it depends on the CUDA version, the number of native PyTorch kernels, the number of used compute capabilities, the number of 3rd party libs (such as cudnn, NCCL) etc.
The best way would be to perform a CUDA operation on your system and check the memory usage via nvidia-smi.