---
title: "FP8"
description: "1 posts about FP8, summarised, each linking to the original."
---

# FP8
> 1 posts about FP8, summarised, each linking to the original.

## Articles

### [MPS in PyTorch 2.15](https://yomu.fyi/post/mps-in-pytorch-2-15.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Irakli Salia, Khushi Agrawal
- Published: Oct 20, 2026
- Type: Announcement

PyTorch 2.15 brings FP8 support to MPS, storing float8\_e4m3fn values in half the space of BF16 and enabling scaled matrix multiplication directly on the GPU with tensorwise or rowwise scaling. Language-model decoding accelerates significantly: mixture-of-experts models decode 5–6.5× faster thanks to a native grouped matmul with a dedicated decoding path—on an M3 Ultra with torch.compile, Qwen3.6-35B-A3B reaches 61.3 tokens/s, up from 9.4. Dense models benefit from specialized matrix-vector kernels for single-row linear layers, and softmax over the vocabulary is about 8× faster. Video generation memory usage drops dramatically: Wan2.2’s VAE decode now peaks at 8 GiB instead of 270 GiB. The Hugging Face team runs real models to find and fix MPS shortcomings. The release also adds complex LU and triangular solves, faster small-matrix inverses and QR decomposition, public MPS streams, and requires macOS 15 or later, with many operations moved from MPSGraph to dedicated Metal kernels.