Faster insights can save time, money and even lives. That's why companies in every industry want to harness the data generated by billions of IoT sensors to transform themselves. To do this, they need powerful, distributed computing power and secure, easy management. NVIDIA Edge Computing solutions bring together NVIDIA Certified Systems™, embedded platforms, AI software, and turnkey management services designed for AI at the network edge.
So rely on edge computing solutions that deliver high-performance, distributed computing power, secure and easy remote management, and compatibility with industry-leading technologies.
Advantages of Edge Computing
The use of AI at the edge offers companies many advantages. By processing data in real time, companies can respond quickly to changes in their operations or markets. Another benefit of using AI at the edge is that companies are able to process their data securely. Because edge computing takes place locally, data is not as vulnerable to outside attacks as it would be if data were transferred to a central data center. In addition, using AI at the edge can help increase business efficiency. For example, manufacturers can use edge computing and AI to monitor the health of their production equipment and plan ahead for maintenance needs. This can minimize downtime and increase production efficiency.
Overall, the use of AI at the edge offers many benefits, including improved efficiency, quality control and safety. By processing data in real time, companies can respond quickly to changes in their operations and minimize downtime
Overall, the use of AI at the edge offers many benefits, including improved efficiency, quality control and safety. By processing data in real time, companies can respond quickly to changes in their operations and minimize downtime
Lower latency
Moving AI computing to the place where data is generated, rather than collecting it and uploading it to a central data center or cloud for processing, reduces latency and enables real-time insights.
Improved security and data protection
Because edge computing allows data to be processed on-site, there is less need to send sensitive data to the public cloud. Furthermore, the data is collected and processed.
Lower costs
Generating more and more data increases bandwidth requirements and data storage costs. By using edge computing and local data processing, less data needs to be transferred to and from the cloud.
Greater range
Traditional cloud computing requires Internet access. With edge computing, however, data is processed without Internet access, extending its reach to previously inaccessible, remote locations.
Solutions for Edge Computing
NVIDIA offers a wide range of solutions for enterprises looking to deploy edge computing to build intelligent infrastructures
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NVIDIA-certified systems at the limit
NVIDIA-Certified Systems™ are the essential platform for edge computing, providing retailers, manufacturers, hospitals, and other edge locations with an optimized solution for deploying AI applications. These systems provide the performance and security required for scale-out deployments and enable organizations to simplify with tested configurations designed for edge computing.
NVIDIA Jetson for embedded systems
NVIDIA ® Jetson™ is used by developers to create breakthrough AI products across industries, and by students and enthusiasts for hands-on AI learning and amazing projects. The Jetson platform includes small, energy-efficient developer kits and production modules that provide powerful acceleration of the NVIDIA CUDA-X™ software stack.
NVIDIA Fleet Command
Designed specifically for AI, NVIDIA Fleet Command™ is a turnkey solution for AI lifecycle management. It removes the complexity of building and maintaining an edge software platform by providing streamlined deployments, over-the-air updates, and detailed monitoring capabilities. Multi-layered security protocols protect intellectual property and application knowledge from the cloud to the edge. By constantly monitoring for performance, irregularities and security incidents, organizations are relieved of the task of developing and operating these capabilities. With Fleet Command, enterprises can go from zero to AI in minutes.
NVIDIA Operators
NVIDIA GPU Operator enables infrastructure teams to manage the lifecycle of GPUs when used with Kubernetes at the cluster level, eliminating the need to manage each node individually. The GPU Operator is packaged and installed as a helmet diagram and manages the lifecycle of software components to run GPU-accelerated applications on Kubernetes. The components included are NVIDIA GPU Feature Discovery, the NVIDIA driver, the Kubernetes device plug-in, the NVIDIA Container Toolkit and NVIDIA Data Center GPU Manager (DCGM) for monitoring. NVIDIA Network Operator is responsible for automating the provisioning and management of host network components in a Kubernetes cluster. It includes the Kubernetes device plug-in, the NVIDIA driver, the NVIDIA peer memory driver, and the Multus and Macvlan container network interfaces (CNIs). These components were previously installed manually, but are now automated by Network Operator, streamlining the deployment process and enabling accelerated data processing with improved customer experience.
NVIDIA EGX Plattform
The NVIDIA EGX™ platform enables enterprise IT to deliver diverse applications on a powerful and cost-effective infrastructure. The platform is a combination of high-performance GPU computing and fast, secure networking in NVIDIACertified Systems™ built and sold by our partners. The EGX platform enables enterprises to prepare for the future while reducing costs by standardizing on a single, unified architecture for easy management, deployment, operations and monitoring. The EGX platform supports a wide range of accelerated edge AI applications that deliver faster insights where they matter most.
NVIDIA Metropolis
NVIDIA Metropolis™ is an application framework with a rich set of developer tools and a partner ecosystem that brings together visual data and AI to improve operational efficiency and safety across a wide range of industries. It helps make sense of the flood of data generated by trillions of sensors in infrastructure to solve problems such as frictionless retail, optimized inventory management, traffic planning in smart cities, visual inspection on factory floors, patient care in healthcare facilities, and more. With cutting-edge technology and an extensive developer ecosystem, companies can quickly build, deploy, and scale AI and IoT applications - from the edge to the cloud.
NVIDIA platform for accelerated computing
NVIDIA offers a portfolio of certified systems with Ampere Tensor Core GPUs for inference engines. The A2 is an entry-level, low-power solution ideal for far-edge servers. The A100 provides the highest inference performance and the A30 provides optimal inference performance for mainstream servers. These certified systems provide leading inference performance in cloud, data center, and edge domains, enabling AI-enabled applications with fewer servers and lower power consumption. NVIDIA also offers a comprehensive portfolio of GPUs, systems, and networking for optimal data center performance, scalability, and security.
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Advantages for different applications
The use of AI at the edge is particularly relevant for companies in the industrial and engineering sectors, as these industries typically work with large amounts of data and operate complex operations and machinery. Here are some specific applications of AI at the edge for these industries:
Predictive maintenance
With the help of edge computing and AI, manufacturers can monitor the condition of their machines in real time and plan maintenance needs in advance. This can minimize downtime and increase production efficiency.
Quality control
By using AI at the edge, manufacturers can collect and analyze data from sensors and cameras in real time to monitor the quality of their products. Deviations can thus be quickly detected and production can be adjusted accordingly.
Process optimization
Edge computing and AI can help optimize processes by collecting and analyzing data from multiple sources in real time. For example, manufacturers can use AI and edge computing to monitor and optimize the energy consumption of their machines to reduce costs.
Security
By using AI at the edge, manufacturers can improve the safety of their assets and employees. Sensors can collect data in real time, and AI can analyze that data to detect and prevent potential threats.
Converged NVIDIA accelerators
This is where strong performance, advanced networking features and robust security come together in one package.
In a unique, efficient architecture, NVIDIA converged accelerators combine the powerful performance of NVIDIA GPUs with the enhanced networking and security of NVIDIA Smart Network Interface Cards (SmartNICs) and Data Processing Units (DPUs). Maximum performance and enhanced security for I/O-intensive, GPU-accelerated workloads from the data center to the edge.Read
NVIDIA Converged Accelerators Datasheet (PDF)
NVIDIA Converged Accelerators Datasheet (PDF)
NVIDIA A30X
NVIDIA A30X
The NVIDIA A30X combines the NVIDIA A30 Tensor Core GPU with the BlueField-2 DPU. MIG allows the GPU to be partitioned into up to four GPU instances, each running a separate service.
The design of this board provides a balance of compute and input/output (IO) performance for use cases such as 5G vRAN and AI-based cybersecurity. Multiple services can run on the GPU, with the integrated PCIe switch providing low latency and predictable performance.
NVIDIA A100X
NVIDIA A100X
The NVIDIA A100X combines the power of the NVIDIA A100 Tensor Core GPU with the BlueField-2 DPU. With MIG, each A100 can be partitioned into up to seven GPU instances, allowing even more services to run simultaneously.
The A100X is ideal for use cases where the compute requirements are more intensive. Examples include 5G with massive multiple-input and multiple-output (MIMO) capabilities, AI-on-5G deployments, and specialized workloads such as signal processing and multi-node training.
The A100X is ideal for use cases where the compute requirements are more intensive. Examples include 5G with massive multiple-input and multiple-output (MIMO) capabilities, AI-on-5G deployments, and specialized workloads such as signal processing and multi-node training.
Register for the NVIDIA Converged Accelerator Developer Kit
Are you interested in developing the next generation of AI and cybersecurity applications? Want to be among the first to get hands-on experience with the new Converged Accelerators? Sign up to receive information about the Converged Accelerator Developer Kit and get early access to the hardware and software components.