GPU Technology Conference -- 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
WorkstationData 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
AdoptionNVIDIA-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.
Read what partners and customers, such as BlazingDB, BOXX,
Charter Communications, Datalogue, Dell, Graphistry, H2O.ai, HP,
Kinetica, Lenovo, MapR, MIT and OmniSci, among others, are
saying.
AvailabilityNVIDIA-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.
About NVIDIA NVIDIA’s (NASDAQ: NVDA) invention
of the GPU in 1999 sparked the growth of the PC gaming market,
redefined modern computer graphics and revolutionized parallel
computing. More recently, GPU deep learning ignited modern AI — the
next era of computing — with the GPU acting as the brain of
computers, robots and self-driving cars that can perceive and
understand the world. More information at
http://nvidianews.nvidia.com/.
For further information, contact:Gail LagunaSr.
PR Manager, Professional VisualizationNVIDIA
Corporation+1-408-386-2435glaguna@nvidia.com
Certain statements in this press release including, but not
limited to, statements as to: global systems builders and OEMs
teaming with NVIDIA and integrating NVIDIA Quadro RTX GPUs and
NVIDIA CUDA-X AI; the benefits, impact, performance, abilities and
features of NVIDIA GPU-accelerated data science workstations,
NVIDIA Quadro RTX GPUs and NVIDIA CUDA-X AI; CUDA-X AI enabling
modern computing applications to benefit from NVIDIA’s
GPU-accelerated computing platform; enterprises being eager to
unlock the benefits of machine learning and analytics and hiring
data scientists at an unprecedented rate; NVIDIA GPU-accelerated
data science workstations providing enterprises with the processing
power to build their own machine learning models and delivering a
major boost in AI acceleration enabling data scientists to
interpret and manage data, solve complex problems and provide
valuable, actionable insights; NVIDIA-powered data science
workstations making it easy for scientists to use models quickly
and accurately; NVIDIA-powered data science workstations being the
ideal complement to NVIDIA’s data science portfolio; NVIDIA-powered
data science workstation enabling data scientists to run end-to-end
data processing pipelines on large datasets faster than ever and
leveraging RAPIDS, leading to faster deployment and business
insights; NVIDIA-powered data science workstations helping OEMs and
data science software providers bring powerful new options to
customers; and the availability of NVIDIA-powered systems for data
scientists are forward-looking statements that are subject to risks
and uncertainties that could cause results to be materially
different than expectations. Important factors that could cause
actual results to differ materially include: global economic
conditions; our reliance on third parties to manufacture, assemble,
package and test our products; the impact of technological
development and competition; development of new products and
technologies or enhancements to our existing product and
technologies; market acceptance of our products or our partners'
products; design, manufacturing or software defects; changes in
consumer preferences or demands; changes in industry standards and
interfaces; unexpected loss of performance of our products or
technologies when integrated into systems; as well as other factors
detailed from time to time in the most recent reports NVIDIA files
with the Securities and Exchange Commission, or SEC, including, but
not limited to, its annual report on Form 10-K and quarterly
reports on Form 10-Q. Copies of reports filed with the SEC are
posted on the company's website and are available from NVIDIA
without charge. These forward-looking statements are not guarantees
of future performance and speak only as of the date hereof, and,
except as required by law, NVIDIA disclaims any obligation to
update these forward-looking statements to reflect future events or
circumstances.
© 2019 NVIDIA Corporation. All rights reserved. NVIDIA, the
NVIDIA logo, CUDA-X, NVIDIA Turing, NVLink, Quadro RTX, RAPIDS and
TensorRT are trademarks and/or registered trademarks of NVIDIA
Corporation in the U.S. and other countries. Other company and
product names may be trademarks of the respective companies with
which they are associated. Features, pricing, availability and
specifications are subject to change without notice.
A photo accompanying this announcement is available at
http://www.globenewswire.com/NewsRoom/AttachmentNg/e7a6d74d-3e76-4d3e-bc86-441816f1e4ae
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