# NVIDIA-Accelerated Data Science

The only hardware-to-software stack optimized for data science.

## GPU-Accelerate Your Data Science Workflows

Data science workflows have traditionally been slow and cumbersome, relying on CPUs to load, filter, and manipulate data, and train and deploy models. With NVIDIA AI software, including RAPIDS™ open-source software libraries, GPUs substantially reduce infrastructure costs and provide superior performance for end-to-end data science workflows. GPU-accelerated data science is available everywhere—on the laptop, in the data center, at the edge, and in the cloud.

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## Features and Benefits

### Maximize Productivity

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

### Accomplish More

Accelerate machine learning training up to 215X faster and perform more iterations, increase experimentation and carry out deeper exploration.

### Cost-Efficiency

Reduce data science infrastructure costs and increase data center efficiency.

## Zero-Code Changes With RAPIDS

Available for Spark, pandas, and networkX.

[Learn More](https://developer.nvidia.com/rapids)

### 150X

### Faster Pandas with cuDF

\* Benchmark on Groupy advanced operation (5GB) DuckDB Data Benchmark

HW: Intel Xeon Platinum 8480CL CPU and NVIDIA Grace Hopper™ GPU

SW: pandas v1.5 and cudf.pandas v23.10

### 5X

### Faster Spark with the RAPIDS Accelerator for Spark

\* NDS 2.0 benchmarks were run with parquet decimal data @ SF3K with UCX off

CPU-only: 8x n1-standard-32

GPU: 8x g2-standard-16, 8x L4 24GB

SW: Spark RAPIDS 24.02

### 48X

### Faster NetworkX with cuGraph

\* Benchmark on PageRank with synthetic dataset having ~16,384 vertices and ~524,288 edges

HW: Intel Xeon Platinum 8480CL CPU and NVIDIA H100 80GB (1x GPU)

SW: NetworkX v3.2 and cuGraph v23.10

## XGBoost Training on NVIDIA GPUs

GPU-accelerated XGBoost brings game-changing performance to the world’s leading machine learning algorithm in both single node and distributed deployments. With significantly faster training speed over CPUs, data science teams can tackle larger data sets, iterate faster, and tune models to maximize prediction accuracy and business value.

### Data Prep

### XGBoost

### End-to-end

CPU: Core i9 | End-to-end time = Data Prep + Conversion + Training + Validation

Learn how to get started today with GPU-accelerated XGBoost

[Get Started](http://resources.nvidia.com/en-us-xgboost)

## NVIDIA GPU Solutions for Data Science

Explore unparalleled acceleration across a variety of different NVIDIA GPU solutions.

### PC

Get started in machine learning.

[Learn More](https://www.nvidia.com/en-us/titan/titan-rtx.md)

### Workstations

A new breed of workstations for data science.

[Learn More](https://www.nvidia.com/en-us/ai-data-science/workstations.md)

### Data Center

NVIDIA-Certified Systems for Enterprises to run Modern AI Workloads.

[Learn More](https://www.nvidia.com/en-us/data-center.md)

### Cloud

Versatile accelerated machine learning.

[Learn More](https://www.nvidia.com/en-us/data-center/gpu-cloud-computing.md)

## GPU-Accelerated Business in Action

Maximize performance, productivity and ROI for machine learning workflows.

Interactive Infographic

Solution Brief

## RAPIDS: Suite of Data Science Libraries

RAPIDS, built on NVIDIA CUDA-X AI, leverages more than 15 years of [NVIDIA® CUDA®](https://developer.nvidia.com/cuda-toolkit) development and machine learning expertise. It’s powerful software for executing end-to-end data science training pipelines completely in NVIDIA GPUs, reducing training time from days to minutes.

[Download Source Code](https://rapids.ai/)

[Download NGC Container](https://ngc.nvidia.com/registry/nvidia-rapidsai-rapidsai)

> RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads.

- Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas

> I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest 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. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome.

- A mid-market specialty retailer with 6000 stores

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## Partner Ecosystem

RAPIDS is open to all and being adopted globally in data science and analytics. Our partners together are transforming the traditional big data analytics ecosystem with [GPU-accelerated analytics](https://www.nvidia.com/en-us/ai-accelerated-analytics/partners.md), machine learning, and deep learning advancements.

## Webinars

### Transforming AI Development on NVIDIA-Powered Data Science Workstations

[Register Now](https://info.nvidia.com/transforming-ai-development-data-science-workstation-reg-page.html)

### Improving Machine Learning Performance and Productivity with XGBoost

[Register Now](https://info.nvidia.com/gpu-accelerated-machine-learning-xgboost-reg-page.html)

### RAPIDS for GPU-Accelerated Data Science in Healthcare

[Register Now](https://info.nvidia.com/rapids-for-gpu-accelerated-data-science-reg-page-emea)

### End-to-End Data Science Acceleration with RAPIDS and DGX-2

[Register Now](https://info.nvidia.com/emea-end-to-end-data-science-acceleration-with-rapids-dgx2.html?linkId=100000004627913)

[View More Webinars](https://gateway.on24.com/wcc/gateway/elitenvidiabrill/1407606/category/15562/data-science?partnerref=datascience)

Explore GPU-accelerated hardware solutions

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