Data analytics workflows have traditionally been slow and cumbersome, relying on CPU computations for data preparation, training, and deployment. Accelerated data science can boost the performance of end-to-end analytics workflows, accelerating value creation while reducing costs.

Transformative technologies for immediate results

Challenges of the industry

  • Data preparation is a complex, time-consuming process that data scientists spend much of their time on.

  • Iteration takes considerable time, resulting in less robust analyses.

  • Downscaling the data sets leads to suboptimal results.

Organizations use analytics to interpret their data and make business decisions. While data analytics unlocks enormous potential, traditional CPU-based processing and analysis of data increases the overall effort and complexity of business operations, reducing profitability. Thanks to accelerated data science, a new era of data analytics is now beginning, enabling businesses and professionals to make the most of their data and infrastructure.

Accelerated data science improves the entire workflow of end-to-end data analytics, whether transforming data for enterprise use or visualizing data at terabyte scale to understand a specific problem domain. Data scientists can easily take full advantage of NVIDIA GPUs with their favorite toolsets, giving your organization the power of high-performance computing with minimal learning curve.

With the combined power of powerful data analytics, companies can better serve their customers, develop products faster, and enable more innovation throughout their operations.

Lightning-fast performance
with Big Data

The results show that using GPUs for both small and large data analysis problems leads to huge time and cost savings. Using familiar APIs such as Pandas and Dask, RAPIDS runs up to 20 times faster on GPUs than on leading CPUs at 10 terabytes in size. With just 16 NVIDIA DGX A100 systems, the power of 350 servers is available, making NVIDIA's solution for HPC performance 7x more cost-effective.

The advantages of accelerated analyses

Reduced waiting times

Spend less time waiting on processes, allowing more time to iterate and test solutions to solve pressing business problems.

Better results

Analyze multi-terabyte datasets with powerful processing for more accurate results and faster reporting.

No refactoring

Accelerate and scale your existing data science toolchain with minimal code changes without having to learn how to use new tools.

Faster processing

Accelerate large-scale data transformations and deliver high-quality data sets faster to support professionals and departments across your organization.

Enormous interoperability

Easily share instrument memory with a large number of frequently used analysis libraries to avoid costly and time-consuming copying of data.

No refactoring

Don't spend hours converting from one data format to another - use the data formats that work best for your business.

Lower expenses

Make the most of your budget with GPU acceleration instead of piling on costs by buying, deploying, and managing more and more CPUs.

Better decisions

Use all your data to make better business decisions, improve organizational performance, and better meet customer needs.

Seamless scaling

Easily scale from a single desktop to clusters with multiple nodes and multiple GPUs thanks to consistent, intuitive architecture.

End-to-end accelerated analytics with NVIDIA

NVIDIA offers solutions to accelerate the entire end-to-end analytics workflow, whether it's reducing the turnaround time of your ETL pipelines or accelerating large-scale machine learning workflows. NVIDIA and its partners offer solutions for running data science workflows on your notebook, in the cloud, to your own premises with NVIDIA-certified systems. These solutions combine the optimized hardware and software for high-performance data analytics to make it easy for organizations to get the most out of their data. With RAPIDS open source software suites and NVIDIA CUDA, data professionals can accelerate analytics pipelines on NVIDIA GPUs and reduce the time spent on data analytics operations such as data loading, processing, and training from days to minutes. The power of CUDA can be harnessed using the popular Python Java-based programming language, making it easy to enter the world of accelerated analytics.

From machine learning to deep learning - all on GPU

Data flow diagram

Data preparation and ETL

Process terabyte-sized ETL pipelines at lightning speed on NVIDIA GPUs with RAPIDS and Spark 3.0 or Dask - put high-quality data sets at the fingertips of your professionals.


Develop, iterate, and refine business-enabling models to support your operations with RAPIDS cuML and Dask.


Gain a deeper understanding of your data through massive visualizations with RAPIDS and Plotly Dash.


Quickly gain business insights with RAPIDS FIL to optimize workflows and decision-making.

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