Hugging Face's work on the PyTorch 2.15 MPS backend brings FP8 support, native grouped matmuls, and dedicated Metal kernels to Apple Silicon Macs running macOS 15. Mixture-of-experts models see decoding speedups up to 6.5 times faster on hardware like the M3 Ultra, while memory peaks for video VAE decoding drop from gigabytes of overhead to manageable footprints. The release replaces various MPSGraph bottlenecks with direct Metal execution for single-row linear layers, softmax, and common linear algebra operations.
It is worth adopting if you are actively running local inference or fine-tuning models on a modern Mac and hitting previous memory or fallback walls. However, the catch is the strict hardware floor requiring macOS 15 and Python 3.11, meaning older setups get left behind without an upgrade path. Skip this if you are strictly bound to CUDA infrastructure, as parity with NVIDIA hardware is still incomplete.
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