{"id":733,"date":"2020-03-24T00:00:00","date_gmt":"2020-03-24T00:00:00","guid":{"rendered":""},"modified":"2025-06-29T13:47:23","modified_gmt":"2025-06-29T20:47:23","slug":"microsoft-powers-transformation-at-nvidia-s-gtc-digital-conference","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/microsoft-powers-transformation-at-nvidia-s-gtc-digital-conference\/","title":{"rendered":"Microsoft powers transformation at NVIDIA\u2019s GTC Digital Conference"},"content":{"rendered":"\n

The world of supercomputing is evolving. Work once limited to high-performance computing (HPC) on-premises clusters and traditional HPC scenarios, is now being performed at the edge, on-premises, in the cloud, and everywhere in between. Whether it\u2019s a manufacturer running advanced simulations, an energy company optimizing drilling through real-time well monitoring, an architecture firm providing professional virtual graphics workstations to employees who need to work remotely, or a financial services company using AI to navigate market risk, Microsoft\u2019s collaboration with NVIDIA makes access to NVIDIA graphics processing units (GPU) platforms easier than ever.<\/p>\n\n\n\n

These modern needs require advanced solutions that were traditionally limited to a few organizations because they were hard to scale and took a long time to deliver. Today, Microsoft Azure delivers HPC capabilities, a comprehensive AI platform, and the Azure Stack family of hybrid and edge offerings that directly address these challenges.<\/p>\n\n\n\n

This year during GTC Digital<\/a>, we\u2019re spotlighting some of the most transformational applications powered by NVIDIA GPU acceleration that highlight our commitment to edge, on-prem, and cloud computing. Registration is free, so sign up to learn how Microsoft is powering transformation.<\/p>\n\n\n\n

Visualization and GPU workstations<\/h2>\n\n\n\n

Azure enables a wide range of visualization workloads, which are critical for desktop virtualization as well as professional graphics such as computer-aided design, content creation, and interactive rendering. Visualization workloads on Azure are powered by NVIDIA\u2019s world-class GPUs and Quadro technology, the world\u2019s preeminent visual computing platform. With access to graphics workstations on Azure cloud, artists, designers, and technical professionals can work remotely, from anywhere, and from any connected device. See our NV-Series virtual machines (VMs) for Windows<\/a> and Linux<\/a>.<\/p>\n\n\n\n

Artificial intelligence<\/h2>\n\n\n\n

We\u2019re sharing the release of the updated execution provider in ONNX Runtime<\/a> with integration for NVIDIA TensorRT 7<\/a>. With this update, ONNX Runtime can execute open Open Neural Network Exchange (ONNX) models on NVIDIA GPUs on Azure cloud and at the edge using the Azure Stack Edge, taking advantage of the new features in TensorRT 7 like dynamic shape, mixed precision optimizations, and INT8 execution.<\/p>\n\n\n\n

Dynamic shape support enables users to run variable batch size, which is used by ONNX Runtime to process recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) models. Mixed precision and INT8 execution are used to speed up execution on the GPU, which enables ONNX Runtime to better balance the performance across CPU and GPU. Originally released in March 2019, TensorRT with ONNX Runtime delivers better inferencing performance on the same hardware when compared to generic GPU acceleration.<\/p>\n\n\n\n

Additionally, the Azure Machine Learning service now supports RAPIDS<\/a>, a high-performance GPU execution accelerator for data science framework using the NVIDIA CUDA platform<\/a>. Azure developers can use RAPIDS in the same way they currently use other machine learning frameworks, and in conjunction with Pandas, Scikit-learn, PyTorch, and TensorFlow. These two developments represent major milestones towards a truly open and interoperable ecosystem for AI. We\u2019re working to ensure these platform additions will simplify and enrich those developer experiences.<\/p>\n\n\n\n

Edge<\/h2>\n\n\n\n

Microsoft provides various solutions in the Intelligent Edge portfolio to empower customers to make sure that machine learning not only happens in the cloud but also at the edge. The solutions include Azure Stack Hub, Azure Stack Edge, and IoT Edge.<\/p>\n\n\n\n

Whether you are capturing sensor data and inferencing at the Edge or performing end-to-end processing with model training in Azure and leveraging the trained models at the edge for enhanced inferencing operations Microsoft can support your needs however and wherever you need to.<\/p>\n\n\n\n

Supercomputing scale<\/h2>\n\n\n\n

Time-to-decision is incredibly important with a global economy that is constantly on the move. With the accelerated pace of change, companies are looking for new ways to gather vast amounts of data, train models, and perform real-time inferencing in the cloud and at the edge. The Azure HPC portfolio consists of purpose-built computing, networking, storage, and application services to help you seamlessly connect your data and processing needs with infrastructure options optimized for various workload characteristics.<\/p>\n\n\n\n

Azure Stack Hub announced preview<\/h2>\n\n\n\n

Microsoft, in collaboration with NVIDIA, is announcing that Azure Stack Hub with Azure NC-Series Virtual Machine (VM) support is now in preview. Azure NC-Series VMs are GPU-enabled Azure Virtual Machines available on the edge. GPU support in Azure Stack Hub unlocks a variety of new solution opportunities. With our Azure Stack Hub hardware partners, customers can choose the appropriate GPU for their workloads to enable Artificial Intelligence, training, inference, and visualization scenarios.<\/p>\n\n\n\n

Azure Stack Hub brings together the full capabilities of the cloud to effectively deploy and manage workloads that otherwise are not possible to bring into a single solution. We are offering two NVIDIA enabled GPU models during the preview period. They are available in both NVIDIA V100 Tensor Core and NVIDIA T4 Tensor Core GPUs. These physical GPUs align with the following Azure N-Series VM types as follows:<\/p>\n\n\n\n