---
title: "Running PyTorch Models on Apple Silicon GPUs with the ExecuTorch MLX Delegate – PyTorch"
url: https://daily.dev/posts/running-pytorch-models-on-apple-silicon-gpus-with-the-executorch-mlx-delegate-pytorch-thejyz1bz
source_url: https://pytorch.org/blog/running-pytorch-models-on-apple-silicon-gpus-with-the-executorch-mlx-delegate
type: article
source: "PyTorch"
published: 2026-05-18T15:32:06.093Z
updated: 2026-05-18T15:32:34.414Z
tags: ["pytorch"]
reading_time: 5
upvotes: 0
comments: 0
language: en
---

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# Running PyTorch Models on Apple Silicon GPUs with the ExecuTorch MLX Delegate – PyTorch

**[PyTorch](https://daily.dev/sources/pytorch)** · 5 min read · 0 upvotes · 0 comments

## Summary

PyTorch has released the ExecuTorch MLX Delegate, an experimental backend that enables GPU-accelerated inference for PyTorch models on Apple Silicon Macs using Apple's MLX framework. It integrates with the PyTorch 2 export stack via torch.export and supports quantization options including BF16, FP16, FP32, and 2/4/8-bit affine quantization via TorchAO. The delegate delivers 3-6x higher throughput compared to existing ExecuTorch backends on macOS. Validated models include dense transformers (Llama 3.2, Qwen 3, Gemma 3, Phi-4 mini), sparse Mixture-of-Experts (Qwen 3.5 35B-A3B), and speech-to-text models (Whisper, Parakeet, Voxtral) for both offline and real-time transcription. The workflow follows the standard ExecuTorch pipeline: export with torch.export, lower with MLXPartitioner, and run the resulting .pte file.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://pytorch.org/blog/running-pytorch-models-on-apple-silicon-gpus-with-the-executorch-mlx-delegate>

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