We have had good success with our GPUs in high-performance computing, deep learning in hpc, deep learning, data analytics, remote work stations, said McHugh, a former Cisco executive who joined Nvidia six months ago.The Tesla M10 GPU has high user density when it comes to delivering apps such as Outlook, Office 2016, web browsers, Adobe Photoshop, and the Windows 10 operating system.Delivering business applications in a virtualized way is becoming more challenging because more businesses are using demanding graphics apps these days.The percentage of GPU-accelerated apps has more than doubled in the past five years, with half that growth coming in the first months of 2016 alone, according to a study by Lakeside Software.To provide the best user experience, these applications increasingly use OpenGL and DirectX APIs, as well as graphics technology from the data center.While the need for advanced GPU technology has commonly been associated with the usage of 3D applications, as enterprises make the move to software like Windows 10, Office 365, and other SaaS and web apps, IT departments will increasingly seek the benefits of GPU acceleration to provide everyday business tools to all of their users, said Robert Young, analyst for IT Service Management and Client Virtualization Software at IDC, in a statement.Nvidia is teaming  up with virtualization software companies, such as Citrix and VMware, to deliver a high-end virtualized app that runs as if it were being processed on a user s personal machine.The cost of running such virtual apps or remote desktop sessions is now down to less than $2 a month per user and, for virtual PCs, is less than $6 a month per user.The new Nvidia Grid software is available worldwide today, and the Tesla M10 will be generally available in the fall.Virtualized apps can now be delivered at a subscription price of about $10 per concurrent user, McHugh said, on the Nvidia Grid service.
IBM is offering Nvidia Grid Tesla M60 GPU in cloud to make compute intensive tasks easier in virtual desktops.It will allow customers to deploy more powerful cloud servers for doing complex computing tasks including data analytics, graphics, energy exploration and deep learning/artificial intelligence.Tesla M60 with NVIDIA GRID virtualisation technology can speed up virtualised desktop applications like CAD/CAM computer-aided design and computer-aided manufacturing including AutoCAD.With the availability of the technology in cloud, companies can used the GPU resources on IBM Cloud on an on-demand basis.It will help reduce the processing time to hours in comparison to the use of CPU-only based servers.Nvidia vice president and general manager Jim McHugh said: "For the first time, businesses can deliver workstation-class graphics-intensive applications from the cloud along with high performance computing."
Microsoft today announced that its N-Series of virtual machine VM instances backed by graphics processing units GPUs are now available in preview for developers to use in the Azure public cloud.Microsoft first announced the N-Series in September.There are two categories of these new instances: the NC series and the NV series.The former, which uses Nvidia Tesla K80 GPUs, is meant for compute-intensive processes, while the latter, relying on Nvidia Tesla M60 and Nvidia Grid GPUs, is oriented around visual workloads.They all work in association with Intel E5-2690v3 chips.The new instances are initially only available in Azure s South Central US Texas region, Microsoft Azure director of program management Corey Sanders wrote in a blog post.Sanders is especially bullish about the NC instances.This SKU … offers the fastest computational GPU available in the public cloud, he wrote.
Public cloud market leader Amazon Web Services AWS is close to releasing new instances — physical slices of virtual machines — backed by Nvidia s graphics processing units GPUs , VentureBeat has learned.The new instances will launch in the next few weeks, one source familiar with the matter told VentureBeat.Another source said the new instances are in an early-access phase for certain customers ahead of the official announcement.The testing phase will end sometime this month, the source said.The new P2 instances will allow for up to 16 Nvidia Tesla K80 GPUs, supporting up to 64 vCPUs and up to 758 GiB of RAM, the source added.AWS launched its first GPU-backed instance, the CG1 that employed Nvidia Tesla Fermi M2050 GPUs, in 2010.In 2013 AWS came out with the G2 series of instances, with the first using Nvidia GK104 Kepler GPUs.In April 2015 AWS introduced a more powerful G2 instance that relies on Nvidia Grid GPUs.
Amazon has rolled out its latest GPU computing box instance line, G3.It comes in three flavours: g3.4xlarge (1 GPU), g3.8xlarge (2 GPUs), and g3.16xlarge (4 GPUs).The line is meant for 3D modelling, visualisation, video encoding and other graphics-intensive apps.Amazon's G2 line first came out in 2013.The high-horse, introduced later on in 2015, was the g2.8xlarge, which came with four Nvidia Grid GPUs with 4GB of video memory and 1,536 CUDA Cores each.You could encode four 1080p video streams or eight real-time 720p video streams, Amazon said in a blog post.
Already, you can play Doom – a graphics-intensive computer game requiring split-second decision-making – over the internet without downloading it.The gamer’s commands are sent from a personal computer to the server and back.“We’re trying to move everything to the cloud,” Egor Gurjev, co-founder and CEO of PlayKey tells Tech in Asia.There’s no certainty this form of “cloud gaming” will become mainstream.To serve its users, PlayKey leases over a hundred Nvidia Grid servers – which are ideal for graphics-intensive applications – in four spots around the world.The blockchain could eliminate the need for centralized servers entirely.
These days, no cloud platform is complete without support for GPUs.There’s no other way to support modern high-performance and machine learning workloads without them, after all.Often, the focus of these offerings is on building machine learning models, but today, Google is launching support for the Nvidia P4 accelerator, which focuses specifically on inferencing to help developers run their existing models faster.In addition to these machine learning workloads, Google Cloud users can also use the GPUs for running remote display applications that need a fast graphics card.To do this, the GPUs support Nvidia Grid, the company’s system for making server-side graphics more responsive for users who log in to remote desktops.Since the P4s come with 8GB of DDR5 memory and can handle up to 22 tera-operations per second for integer operations, these cards can handle pretty much anything you throw at them.
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