Today NVIDIA announced that Microsoft would be offering its GPU-enabled professional graphics applications and accelerated computing capabilities to enterprise customers through Microsoft Azure, Microsoft’s cloud platform. Azure is the first cloud-computing platform to provide NVIDIA GRID 2.0 virtualized graphics for its customers. The partnership between the companies aims to deliver accelerated graphics and high performance computing to any connected device, regardless of location.
Today NVIDIA announced that Microsoft would be offering its GPU-enabled professional graphics applications and accelerated computing capabilities to enterprise customers through Microsoft Azure, Microsoft’s cloud platform. Azure is the first cloud-computing platform to provide NVIDIA GRID 2.0 virtualized graphics for its customers. The partnership between the companies aims to deliver accelerated graphics and high performance computing to any connected device, regardless of location.
With NVIDIA GPU capabilities in Azure, businesses will be able to deploy NVIDIA Quadro-grade professional graphics applications and accelerated computing on-premises, in the cloud, or through a hybrid combination of the two. Businesses will be able to use either Linux or Windows VMs. Organizations will now be able to deliver graphic-intense applications from companies such as Autodesk and Ersi straight to their users from the cloud.
Not only will users receive the benefits of running graphic-intense applications with GRID 2.0, Azure also offers supercomputing-class performance. This performance is achieved by adding NVIDIA Tesla Accelerated Computing Platform's flagship Tesla K80 GPU accelerators. Designed from the ground up, the Tesla Accelerated Computing Platform is made for HPC, computational science, supercomputing, data analytics and deep learning applications and delivers higher performance and better energy efficiency than CPU only approaches.
Azure deploys the Tesla K80 GPU accelerator in its N-Series VMs. Which means that companies can use Azure to dramatically accelerate most workloads, even some of their most demanding, without the need to invest in dedicated computing resources.
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