If that's the case, then there's a (slim) chance we might see Apple Silicon GPUs in the future, which would then potentially make it possible to build an Apple Silicon Machine that could compete with an Nvidia workstation.Īt the end of the day, Nvidia is winning the software war with CUDA. The upcoming Mac Pro is rumored to be preserving the configurability of a workstation. Even if we could fully leverage the onboard hardware, even the M1 Ultra isn't going to outpace top-of-the-line consumer-grade Nvidia GPUs (3090Ti/4090) because it doesn't have the compute. Apple doesn't provide public interfaces to the ANE or the AMX modules, making it difficult to fully leverage the hardware, even from PyTorch's MPS runtime or Apple's 'Tensorflow for macOS' packageĢ.
Having large amounts of unified memory is useful for training large models, but there are two problems with using Apple Silicon for model training:ġ.
