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

NVIDIA Developer Blog · published 18 August 2026

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Even Faster and More Scalable UMAP on the GPU with NVIDIA cuML Read at the source ↗
developer.nvidia.com
https://developer.nvidia.com/topics/ai/data-science/cuda-x-data-science-libraries/cuml
developer.nvidia.com
https://github.com/NVIDIA/cuvs/releases
github.com
https://openreview.net/forum?id=CR35IJQD2J
openreview.net
https://docs.rapids.ai/api/cuvs/stable/cpp_api/neighbors_all_neighbors/
docs.rapids.ai
https://github.com/lmcinnes/umap
github.com
https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438
direct.mit.edu
https://docs.rapids.ai/api/cuvs/stable/cuvs_bench/wiki_all_dataset/
docs.rapids.ai
https://www.nvidia.com/en-us/data-center/dgx-platform/
nvidia.com
https://www.nvidia.com/en-us/data-center/h100/
nvidia.com
https://scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html
scikit-learn.org
https://github.com/NVIDIA/cuvs
github.com
https://github.com/rapidsai/cuml
github.com
https://medium.com/rapids-ai/faster-topic-modeling-with-bertopic-and-rapids-cuml-5c7559aba898
medium.com
https://build.nvidia.com/nvidia/single-cell-analysis
build.nvidia.com
https://docs.rapids.ai/install/
docs.rapids.ai
https://docs.nvidia.com/cuvs/user-guide/api-guides/indexing-guide/all-neighbors
docs.nvidia.com