Performance Notes Of PyTorch Support for M1 and M2 GPUs

Apple’s ARM-based M1 and M2 chips offer strong performance and energy efficiency, and PyTorch has announced plans to support their GPUs for deep learning workloads that previously ran mainly on CPUs. Early testing noted that convolutional neural networks may require substantially more memory on M1 GPUs and CPUs than on CUDA GPUs, creating potential swapping concerns, although the reported MacBook Pro runs remained below 80% memory utilization. To compare hardware fairly, the training tests used a batch size of 32 because Nvidia RTX 2080 Ti and GTX 1080 Ti GPUs were limited by 11 GB of VRAM, while a later M1 Pro test using batch size 64 was about 20% faster. Sebastian Raschka provides additional benchmarks and discussion in a linked article on running PyTorch on the M1 GPU.

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