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Modern businesses are trying to harness data from billions of sensors in the Internet of Things (IoT) found in retail stores, urban streets and hospitals. The ability to gain insights faster with edge computing solutions can not only save time and money, but also save lives. To do this, edge computing solutions must offer powerful, distributed computing power, secure and easy remote management, and compatibility with industry-leading technologies.
THE BENEFITS OF ACCELERATED EDGE COMPUTING
LOWER LATENCY, FASTER INSIGHTS
Because edge computing processes data locally rather than in the cloud or in a data centre, latency and bandwidth requirements are reduced. This enables real-time feedback and decision-making. Smart sensors embedded in many IoT devices, for example, can process data from a factory's autonomous machines and cameras and instantly notify employees of irregularities, malfunctions and more. Beyond embedded devices, companies can place edge servers in close proximity to sensors to further reduce latency, typically in a server room or cabinet in a shop, hospital or warehouse.
LARGE AND SCALABLE COMPUTING POWER
The physical distance between data and data centres leads to latency. By introducing accelerated computing in the periphery - and thus closer to the data source - edge computing reduces the main cause of this latency. But to process the vast amounts of data that are constantly being generated locally, embedded devices and edge servers still require the power of a data centre. With NVIDIA GPUs and software, organisations no longer have to sacrifice computing power for latency or scalability in the periphery.
DISTRIBUTED, SIMPLIFIED IT
When systems are distributed across multiple locations, organisations face new challenges in supply, deployment, management and maintenance. To keep operations running smoothly, it's important to be able to install software remotely on devices. The NVIDIA EGX™ AI platform - with helmet charts, containers, continuous integration and continuous deployment (CI/CD) - streamlines and accelerates this process. Using NVIDIA EGX, IT professionals can easily deploy updated AI containers in minutes.
NVIDIA EGX's GPU operator automates the installation and configuration of all components required for GPU computing in the Kubernetes cluster. Components such as NVIDIA drivers, container runtimes, device plug-ins and monitoring run in containers on Kubernetes as services, so only a single image needs to be managed for CPU and GPU nodes.
CROSS-SECTOR ACCELERATED EDGE COMPUTING
The NVIDIA NVIDIA EGX AI platform delivers the power of accelerated AI computing with an easy-to-deploy, cloud-native software stack, a wide range of validated servers and devices, and a vast ecosystem of partners offering products and services with and for EGX.
The NVIDIA EGX platform delivers the power of accelerated AI computing with an easy-to-deploy, cloud-native software stack, a wide range of validated servers and devices, and a vast ecosystem of partners offering products and services with and for EGX.
Quickly and easily provision GPU servers with NVIDIA EGX, a cloud-native, scalable edge-first software stack. One of the key components of the stack is the NVIDIA GPU Operator, which standardises and automates the provisioning of all required components for GPU-enabled Kubernetes clusters.
The NVIDIA NGC™ catalogue has an extensive range of GPU-accelerated software for edge computing, including Helm charts for Kubernetes deployment. The NGC catalogue gives you easy access to the most relevant AI and data science software containers, pre-trained models and SDKs, all tuned, tested and optimised by NVIDIA. With the-NGC catalogue, you can create and deploy customised solutions that are powerful, secure and scalable.
Harness the power and versatility of NVIDIA's GPUs and networking technology in the peripherals with the NVIDIA EGX platform. The EGX hardware portfolio starts with the energy-efficient NVIDIA® Jetson™ product family for tasks such as image recognition and sensor fusion. It extends to a full rack of NVIDIA T4 Tensor Core GPU servers capable of more than 10,000 tera operations per second (TOPS) and serving hundreds of users with real-time speech recognition and other complex AI experiences.
NVIDIA Fleet Command is a hybrid cloud platform for managing and scaling AI in the periphery. With NVIDIA Fleet Command, you can easily manage and scale your peripheral deployments. Whether it's tens or millions of peripherals, you can securely and remotely deploy AI across the enterprise in minutes.