GPU ACCELERATION OF YOUR
DATA ANALYSIS WORKFLOWS
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.
CPU: Core i9 | End-to-end time = Data Prep + Conversion + Training + Validation
DATA SCIENCE SOLUTIONS
PC
Familiarize with machine learning.
Quadro
Professional workstations for machine learning.
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 machine learning expertise. 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
EVERYTHING ON THE GPU
FASTER END-TO-END SPEEDS IN RAPIDS
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.