mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon

• URL: http://arxiv.org/abs/2603.04035v1
• Date: Wed, 04 Mar 2026 13:16:48 GMT
• Title: mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon
• Abstract summary: mlx-vis is a Python library that implements six dimensionality reduction methods and a k-nearest neighbor graph entirely in MLX.<n>The library provides UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, and NNDescent, all executing on Metal GPU through a unified fit_transform interface.<n>On Fashion-MNIST with 70,000 points, all methods complete embedding in 2.1-3.8 seconds and render 800-frame animations in 1.4 seconds on an M3 Ultra.
• Score: 5.943245848892104
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: mlx-vis is a Python library that implements six dimensionality reduction methods and a k-nearest neighbor graph algorithm entirely in MLX, Apple's array framework for Apple Silicon. The library provides UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, and NNDescent, all executing on Metal GPU through a unified fit_transform interface. Beyond embedding computation, mlx-vis includes a GPU-accelerated circle-splatting renderer that produces scatter plots and smooth animations without matplotlib, composing frames via scatter-add alpha blending on GPU and piping them to hardware H.264 encoding. On Fashion-MNIST with 70,000 points, all methods complete embedding in 2.1-3.8 seconds and render 800-frame animations in 1.4 seconds on an M3 Ultra, with the full pipeline from raw data to rendered video finishing in 3.6-5.2 seconds. The library depends only on MLX and NumPy, is released under the Apache 2.0 license, and is available at https://github.com/hanxiao/mlx-vis.

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