GPU ACCELERATION OF YOUR DATA ANALYSIS WORKFLOWS

Data science workflows have been slow and cumbersome. Yet they depend on CPUs to load, filter, and manipulate data, as well as train and deploy models. GPUs significantly reduce infrastructure costs and provide superior performance for end-to-end data science workflows using NVIDIA RAPIDS™ libraries. GPU-accelerated data science is available everywhere - on the desktop, in the data center, in the peripherals, and in the cloud.

Maximize productivity

Reduce wait time to get the most valuable insights and accelerate ROI.

REACH MORE

Accelerate machine learning training by up to 215x and perform more iterations, more experiments, and deeper explorations.

COST EFFICIENCY

Reduce data science infrastructure costs and increase data center efficiency.

APACHE SPARK 3.0 accelerates wITH RAPIDS GRAPHIC PROCESSORS

Version 3.0 is the first release of Spark to offer fully integrated and seamless GPU acceleration for analytics and AI workloads. Take advantage of Spark 3.0 with GPUs either on-premises or in the cloud - without having to change code each time. The breakthrough performance of GPUs enables organizations and researchers to train larger models more frequently - unlocking the value of Big Data with the power of AI.

XGBOOST TRAINING FOR NVIDIA GPUS

NVIDIA's GPU-accelerated XGBoost enables the world's leading machine learning algorithm to deliver performance gains in games. With significantly faster training performance over CPUs, data science teams can tackle larger data sets, iterate faster, and optimize models for maximum predictive accuracy and business value.
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CPU: Core i9 | End-to-end time = Data Prep + Conversion + Training + Validation
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DATA SCIENCE SOLUTIONS

Titan RTX GPU

PC

Familiarize with machine learning.

RTX Workstations Laptop Monitor

Quadro

Professional workstations for machine learning.

DGX Server

Cloud & Data Center

NVIDIA-certified enterprise systems for running advanced AI workloads

RAPIDS: NEW SOFTWARE LIBRARIES FOR DATA SCIENCE

RAPIDS is built on more than 15 years of NVIDIA® CUDA® development and expertise in machine learning. It is powerful new software for fully executing end-to-end data science learning pipelines in the GPU, shrinking learning time from days to minutes.

MACHINE LEARNING TO DEEP LEARNING
:EVERYTHING ON THE GPU

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FASTER END-TO-END SPEEDS IN RAPIDS

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USE RAPIDS TODAY

RAPIDS libraries are open source, written in Python and built on Apache Arrow. The software is developed together with open source communities worldwide. Download RAPIDS, the acceleration of your machine learning as well as your data science workflows.
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem based on Apache Arrow. The collaboration between NVIDIA and Ursa Labs will accelerate the pace of innovation in Arrow core libraries and contribute to significant performance gains in analytics and feature engineering workloads.
Wes McKinney, head of Ursa Labs and creator of Apache Arrow and Pandas
I have achieved a 24x speedup with RAPIDS XGBOOST and can now replace hundreds of CPU nodes by running my largest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!?
Streaming Media Company
My previous bottleneck was I/O. ...10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. This scale would have easily taken us 4 days with the old infrastructure ... simply fantastic.
A medium-sized specialty store with 6000 branches