NVIDIA has teamed with the world’s leading OEMs and system builders to deliver powerful new workstations designed to help millions of data scientists, analysts and engineers make better business predictions faster and become more productive.
Purpose-built for data analytics, machine learning, and deep learning, the systems provide the extreme computational power and tools required to prepare, process and analyze the massive amounts of data used in fields such as finance, insurance, retail and professional services.
NVIDIA-powered workstations for data science are based on a powerful reference architecture made up of dual, high-end NVIDIA Quadro RTX GPUs and NVIDIA CUDA-X AI accelerated data science software, such as RAPIDS, TensorFlow, PyTorch, and Caffe. CUDA-X AI is a collection of libraries that enable modern computing applications to benefit from NVIDIA’s GPU-accelerated computing platform.
“Data science is one of the fastest growing fields of computer science and impacts every industry. Enterprises are eager to unlock the value of their business data using machine learning and are hiring at an unprecedented rate data scientists who require powerful workstations architected specifically for their needs,” said Jensen Huang, founder and CEO of NVIDIA. “With our partners, we are introducing NVIDIA-powered data science workstations — made possible by our new Turing Tensor Core GPUs and CUDA-X AI acceleration libraries — that allow data scientists to develop predictive models that can revolutionize their business.”
NVIDIA GPU-Accelerated Data Science Workstation
Data science problems involve data on a massive scale and require large-scale processing capabilities. NVIDIA-powered data science workstations make it easy for scientists to wrangle, prep, train and deploy models quickly and accurately. Features and benefits include:
- Dual, high-end Quadro RTX GPUs — Powered by the latest NVIDIA Turing GPU architecture and designed for enterprise deployment, dual Quadro RTX 8000 and 6000 GPUs deliver up to 260 teraflops of compute performance and 96GB of memory using NVIDIA NVLink interconnect technology. Quadro RTX-powered data science workstations provide the capacity and bandwidth to handle the largest datasets and compute-intensive workloads as well as the graphics power required for 3D visualization of massive datasets, including VR.
- Data science software stack — built on the Linux operating system and Docker containers:
- NVIDIA CUDA-X AI — A collection of NVIDIA's GPU acceleration libraries to accelerate deep learning, machine learning, and data analysis. CUDA-X AI includes cuDNN for accelerating deep learning primitives, cuML for accelerating machine learning algorithms, TensorRT for optimizing trained models for inference and over 15 other libraries. Together they work seamlessly with NVIDIA Tensor Core GPUs to accelerate the end-to-end workflows for developing and deploying AI-based applications. CUDA-X AI can be integrated into deep learning frameworks, including TensorFlow, PyTorch and MXNet, and leading cloud platforms, including AWS, Microsoft Azure and Google Cloud.
- NVIDIA RAPIDS — A set of GPU-accelerated libraries analytics for data preparation, traditional machine learning and graph analytics.
- Anaconda Distribution — With Anaconda, Inc., NVIDIA is providing Anaconda Distribution, an innovative way for data scientists to perform Python/R, data science, AI and machine learning.
- Enterprise-ready — Tested and optimized in conjunction with workstation manufacturers to meet the needs of mission-critical enterprise deployments.
- Optional software support — Offers peace of mind with NVIDIA-developed software and containers, including deep learning and machine learning frameworks.
By freeing data scientists to work locally, NVIDIA-powered data science workstations are the ideal complement to NVIDIA’s data science portfolio.
“The NVIDIA-powered data science workstation enables our data scientists to run end-to-end data processing pipelines on large datasets faster than ever,” said Mike Koelemay, chief data scientist at Lockheed Martin Rotary & Mission Systems. “Leveraging RAPIDS to push more of the data processing pipeline to the GPU reduces model development time, which leads to faster deployment and business insights.”
Broad Ecosystem Support and Adoption
NVIDIA-powered Data Science Workstations help OEMs and leading data science software providers meet the growing demand for GPU-accelerated data science capabilities and offer powerful new options to customers conducting AI-based exploration.
NVIDIA-powered systems for data scientists are available immediately from global workstation providers such as Dell, HP and Lenovo and regional system builders, including AMAX, APY, Azken Muga, BOXX, CADNetwork, Carri, Colfax, Delta, EXXACT, Microway, Scan, Sysgen, and Thinkmate.