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3 Things NVIDIA Is Doing Right -- The Motley Fool

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Most of the company's GPU revenue comes from its gaming chip business, which saw revenues grow 17% year-over-year in Q1. TrendForce expects the U.S. virtual reality market to reach 70 billion market by 2020, giving NVIDIA plenty of room for more growth. And the company recently debuted its DGX-1 supercomputer, which can be be paired with Drive PX 2 to process real-time autonomous driving information from the cloud. Facebook uses NVIDIA's Tesla M40 GPU accelerators to help power its Big Sur machine learning computer servers.


IBM Extends GPU Cloud Capabilities, Targets Machine Learning

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As GPU maker Nvidia's CEO stressed at this year's GPU Technology Conference, deep learning is a target market, fed in part by a new range of their GPUs for training and executing deep neural networks, including the Tesla M40, M4, the existing supercomputing-focused K80, and now, the P100 (Nvidia's latest Pascal processor, which is at the heart of a new appliance specifically designed for deep learning workloads). While cloud rival Amazon Web Services, among others, are sporting GPU cards for high performance computing (HPC) and deep learning users, the partnership between Nvidia and IBM is giving Big Blue a leg up in terms of making a wider array of GPUs available to suit different workloads. Today that suite of GPU options was enriched with the addition of the virtualization-ready Nvidia M60 cards, which can support a wider range of workloads--from HPC applications, to machine learning workloads, to virtual services and gaming platforms. As our own Timothy Prickett Morgan noted earlier this year, at the moment, Nvidia identifies six cloud providers that provide cloud-based GPU capacity or hosted GPU capacity.


IBM Steps Up GPU Power for the Cloud

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By adopting Nvidia's Tesla solution, IBM is making inroads in supporting AI and cognitive across a variety of enterprises. HPC (high performance computing) need powerful GPUs to do data analytics, AI, and graphical computations. To accelerate adoption for IBM's Watson, the company offers 30 API's (Watson services). This gives developers using Google's cloud infrastructure AI machine learning functionality for their apps.


Google's Making Its Own Chips Now. Time for Intel to Freak Out

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Over the past decade, the company has designed all sorts of new hardware for the massive data centers that underpin its myriad online services, including computer servers, networking gear, and more. At the same time, as more and more businesses adopt the cloud computing services offered by Google, they'll be buying fewer and fewer servers (and thus chips) of their own, eating even further into the chip market. That's because it helps run TensorFlow, the software engine that drives the Google's deep neural networks, networks of hardware and software that can learn particular tasks by analyzing vast amounts of data. Other tech giants typically run their deep neural nets with graphics processing units, or GPUs--chips that were originally designed to render images for games and other graphics-heavy applications.


Google's Making Its Own Chips Now. Time for Intel to Freak Out

WIRED

Over the past decade, the company has designed all sorts of new hardware for the massive data centers that underpin its myriad online services, including computer servers, networking gear, and more. At the same time, as more and more businesses adopt the cloud computing services offered Google, they'll be buying fewer and fewer servers (and thus chips) of their own, eating even further into the chip market. That's because it helps run TensorFlow, the software engine that drives the Google's deep neural networks, networks of hardware and software that can learn particular tasks by analyzing vast amounts of data. Other tech giants typically run their deep neural nets with graphics processing units, or GPUs--chips that were originally designed to render images for games and other graphics-heavy applications.


How NVIDIA Could Dominate Machine Learning -- The Motley Fool

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Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google, Amazon, and Facebook (NASDAQ:FB) are just a few. Meanwhile, its data center revenue, which includes its GPU sales for cloud-based and machine learning services brought in just 143 million in the quarter. I don't expect NVIDIA's revenue to spike from GPU sales for cloud-based machine learning, but rather steadily increase as the company builds out its data center segment. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon.com, The Motley Fool recommends Intel.


Deep Learning in the Cloud with NVIDIA DIGITS and Titan-X GPUs starting at 0.49 per hour - Bitfusion.io

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Deep learning users can now access pre-configured NVIDIA DIGITS Titan-X GPU instance starting at 49 cents per hour on Nimbix! Data Scientists and Deep learning users can now try the single-click solution on Cloud Service Provider Nimbix to launch an instance configured to run NVIDIA DIGITS on Titan-X GPUs at as low as 0.49/hour, the most affordable high performance GPUs compared to anywhere in the cloud, powered by Bitfusion Boost's Software Defined Supercompute technology. The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. The single-click solution lowers the barrier for deep learning developers and data scientists to spin up affordable Titan-X GPU instances powered by Bitfusion Boost's GPU virtualization technology.


IBM Research Lead Charts Scope of Watson AI Effort

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Watson represents a larger focus at IBM that integrates machine learning and data analytics technologies to bring cognitive computing capabilities to its customers. He is an IBM Fellow, Vice President Europe and Director IBM Research – Zurich Research Laboratory, Switzerland. In January 2014, IBM launched the IBM Watson business unit, investing 1B dedicated to developing and commercializing cloud-delivered cognitive computing technologies. In addition to the Watson business unit, in September 2015 we announced Watson Health, dedicated to improving the ability of doctors, researchers and insurers to surface new insights from the massive amount of personal health data being created and provide turnkey solutions to deliver personalized healthcare.


Nvidia CEO bets on artificial intelligence as the future of computing

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Huang said deep learning will be the basis for the entire computer industry, including data centers and the cloud, for years to come. Huang also said he believes AI and deep learning will transform data centers and cloud services. Rajat Monga, a Google technical lead and manager of TensorFlow, an open source software library for machine learning that was developed at Google, said the company thinks deep learning will infuse every Google service, including new areas such as robotics. It's what he called the world's first car computing platform powered by deep learning.