PyTorch 2.15 MPS adds FP8 and faster LLM decoding on Macs
Hugging Face's PyTorch team published a blog post on October 20, 2026 describing MPS improvements in PyTorch 2.15, including FP8 support with float8_e4m3fn storage and scaled matrix multiplication on the Mac backend. The team reports self-measured gains: MoE decoding 5–6.5× faster than 2.14, with Qwen3.6-35B-A3B rising from 9.4 to 61.3 tokens/s on an M3 Ultra, and Wan2.2 video VAE decode peak memory falling from 270 GiB to about 8 GiB. The post also says MPS now requires macOS 15 or later, PyTorch 2.15 requires Python 3.11 or later, and 2.16 will run attention entirely on Metal.
The Hugging Face PyTorch team says its goal is to make the Mac a first-class place to run and train PyTorch models, and these MPS kernels land in the PyTorch 2.15 release candidate.
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