# RAPIDS NVIDIA's open-source GPU-accelerated data science software suite, including libraries for DataFrames, machine learning, and graph analytics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### 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... ## Make your ZeroGPU Spaces go brrr with ahead-of-time compilation DevFeed: [Make your ZeroGPU Spaces go brrr with ahead-of-time compilation]() Original publisher: [Read original article]() Author: Charles Bensimon; Sayak Paul; Linoy Tsaban; Apolinário from multimodal AI art Published: 2025-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [RAPIDS](), [GPU](), [fly](), [PyTorch](), [quantization](), [Open Source]() Tags: [compilation](), [gpu](), [guide](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [latency](), [models](), [optimization](), [python](), [pytorch](), [quantization](), [spaces](), [text-to-image](), [zerogpu]() ### AI overview This tutorial explains how to use PyTorch ahead-of-time compilation in ZeroGPU Spaces. It covers faster model startup and inference, FP8 quantization, dynamic shapes, and the process-based GPU allocation model used by ZeroGPU, with reported speedups of 1.3x-1.8x on Flux, Wan, and LTX models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Leveraging Spark 3 and NVIDIA's GPUs to Reduce Cloud Cost by up to 70% for Big Data Pipelines DevFeed: [Leveraging Spark 3 and NVIDIA's GPUs to Reduce Cloud Cost by up to 70% for Big Data Pipelines]() Original publisher: [Read original article]() Author: Ilay Chen Published: 2024-02-21T16:42:14Z Content type: tutorial Language: en Sources: [PayPal Technology]() Topics: [Apache Spark](), [GPU](), [RAPIDS](), [Cloud](), [Data pipelines](), [migration](), [upgrade](), [Open Source]() Tags: [apache-spark](), [big-data](), [cloud](), [cloud-computing](), [data-pipelines](), [data-processing](), [gpu](), [machine-learning](), [migration](), [rapids](), [upgrade]() ### AI overview A PayPal engineering blog explains how upgrading from Apache Spark 2 to Spark 3 and migrating workloads to GPU clusters with NVIDIA Spark RAPIDS can accelerate selected big-data processing tasks and potentially reduce cloud costs by up to 70%. It covers the migration, parameter tuning, challenges, and reported benefits. ### Source excerpt By Ilay Chen and Tomer Akirav At PayPal, hundreds of thousands of Apache Spark jobs run on an hourly basis, processing petabytes of data and requiring a high volume of resources. To handle the growth of machine learning solutions, PayPal requires scalable environments, cost awareness and constant innovation. This blog explains how Apache Spark 3 and GPUs can help enterprises potentially reduce Apache Spark's jobs cloud costs by up to 70% for big data processing and AI applications. Our journey will begin with a brief introduction of Spark RAPIDS -- Apache Spark's accelerator that leverages GPUs to accelerate processing via the RAPIDS libraries. We will then review PayPal's CPU-based Spark 2 application, our upgrade to Spark 3 and its new capabilities, explore the migration of our Apache Spark application to a GPU cluster, and how we tuned Spark RAPIDS parameters. We will then discuss some challenges we encountered and the benefits of the updates. Libra scales in the cloud, generated by AIBackground GPUs are everywhere, and their parallelism characteristics are perfect for processing AI and graphics applications, among other things. For those unfamiliar: what makes GPUs different from CPUs, computation-wise, is that CPUs have a limited amount of very strong cores, whereas GPUs have thousands, or even tens of thousands or more, relatively weak cores that work together very well. PayPal has been leveraging GPUs to train models for some time now, and so we decided to evaluate if the parallelism of the GPU can be helpful with processing big data applications based on Apache Spark. In our research, we encountered NVIDIA's Spark RAPIDS open-source project. It has many purposes, however we focused on Spark RAPIDS's cost reduction potential, because enterprises like PayPal spend lots of money on running Spark jobs in the cloud. Using Spark with GPUs isn't common in the industry yet, but according to our findings as described in this blog, the potential benefits could be enorm