MPS in PyTorch 2.15

If you run models on your Mac or use MPS as your primary acceleration tool for computing, there's plenty to look forward to in PyTorch 2.15! FP8 arrives on MPS, language-model decoding gets faster paths through linear layers and softmax and new Metal kernels tackle some surprisingly expensive parts of audio, video and training workloads.

A quick word about who we are: we're the PyTorch team at Hugging Face. We contribute across PyTorch, and one of our focus areas is the MPS backend, where the goal is to make your Mac a first-class place to run and train PyTorch models. At the start of 2026, running even small models on MPS often meant working around unsupported operations, performance cliffs and unexpected memory growth. So we run real models from the Hugging Face Hub across language, image and video, find where MPS falls short and fix it upstream in PyTorch, more and more often by replacing MPSGraph with dedicated Metal kernels. Much of this post is the result of that work, done together with the PyTorch maintainers and community contributors.

TL;DR

What this means for you

If you run models on a Mac with transformers, diffusers or tools like ComfyUI, here's what 2.15 changes in practice:

Contents

• Getting started
• FP8 comes to MPS
• Faster language-model decoding
• Linear algebra gets a boost
• Convolutions, padding, and training
• Softmax and reductions
• A much smaller memory footprint for video decoding
• More control with MPS streams
• More operations work on MPS
• A sneak peek at PyTorch 2.16
• Wrapping up

Getting started

You'll need an Apple silicon Mac with macOS 15 or later and Python 3.11 or later (see the 2.15 compatibility matrix). If your OS is too old or no Metal GPU is available, the MPS availability check now tells you why.

To try the release candidate in a separate environment:

uv pip install torch==2.15.0 --index-url https://download.pytorch.org/whl/test/cpu