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

- FP8 comes to the Mac: store float8_e4m3fn values in half the space of BF16 and use scaled matrix multiplication and all other ops which are supported on CUDA/CPU!

- MoE models decode 5–6.5× faster than in 2.14: Qwen3.6-35B-A3B goes from 9.4 to 61.3 tokens/s on an M3 Ultra, thanks to a native grouped matmul with a dedicated decoding path, fewer GPU-CPU syncs and faster top-k routing.

- Less time per token for every LLM: single-row linear layers use specialized matrix-vector kernels, and softmax over the vocabulary is about 8× faster.

- More linear algebra on the GPU: complex LU and triangular solves, faster small-matrix inverses, and faster QR decomposition.

- Audio, video, and training get attention too: faster paths for convolutions, padding, dropout, interpolation backward and reductions. Wan2.2's video VAE decode now peaks at 8 GiB of memory instead of 270 GiB.

- More control from Python: public MPS streams and more reliable event timing.

- Updated requirements: MPS now requires macOS 15 or later; PyTorch 2.15 requires Python 3.11 or later.

- A peek at 2.16: attention fully on Metal, more MPSGraph-to-Metal migrations and faster LLM decoding.

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:

- Much faster local LLMs. Mixture-of-experts models decode 5–6.5× faster than in 2.14: on an M3 Ultra with torch.compile, Qwen3.6-35B-A3B goes from 9.4 to 61.3 tokens/s and LFM2.5-8B-A1B from 23.6 to 123.6 tokens/s. Dense models benefit too, from faster linear layers and an 8× faster softmax for sampling. ( More )

- Video generation that fits in memory. Decoding a video with Wan2.2's tiled VAE used to push driver memory to 270 GiB; it now peaks at about 8 GiB. ( More )

- Correct image editing with Qwen-Image-2.1. On 2.14, the Qwen-Image-2.1 VAE encoder silently corrupted images on MPS, breaking image-edit workflows ( reported in ComfyUI ). The same padding change fixes it: an encode-decode round trip now matches CPU at 29.95 dB PSNR, up from 10.06 dB.

- FP8 weights on your Mac. FP8 tensors take half the memory of BF16, and F.scaled_mm multiplies them directly on the GPU. ( More )

Contents

- Getting started

- FP8 comes to MPS

- Faster language-model decoding

- Linear algebra gets a boost

- Convolutions, padding, and training

- Softmax and reductions