English

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

Machine Learning 2026-03-23 v3

Abstract

mlx-vis implements eight dimensionality reduction methods -- UMAP, t-SNE, PaCMAP, LocalMAP, TriMap, DREAMS, CNE, MMAE -- and NNDescent k-NN graph construction entirely in MLX for Apple Silicon Metal GPU. A built-in GPU renderer produces scatter plots and smooth animations via hardware H.264 encoding. On Fashion-MNIST (70K points, M3 Ultra), seven of eight methods embed in 2.0-4.7s and render 800-frame animations in 1.4s. The library depends only on MLX and NumPy and is available at https://github.com/hanxiao/mlx-vis.

Cite

@article{arxiv.2603.04035,
  title  = {mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon},
  author = {Han Xiao},
  journal= {arXiv preprint arXiv:2603.04035},
  year   = {2026}
}

Comments

8 pages, 8 figures. Software: https://github.com/hanxiao/mlx-vis. v3: VRAM optimization, updated benchmarks, added LocalMAP and MMAE methods

R2 v1 2026-07-01T11:02:58.604Z