# Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy

DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy](<https://devfeed.tech/articles/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy-6933.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy/>)

Author: Tanya Lenz

Published: 2026-08-18T16:48:08Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [RAPIDS](<https://devfeed.tech/topics/rapids.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [feature](<https://devfeed.tech/tags/feature.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [post](<https://devfeed.tech/tags/post.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training](<https://devfeed.tech/tags/training.md>), [vector](<https://devfeed.tech/tags/vector.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

## AI overview

This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy.

## Source excerpt

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...