Billions of IoT sensors—in retail stores, on city streets, on warehouse floors, and in hospitals—are generating massive amounts of data. Tapping into faster insights from that data can mean improved services, streamlined operations, and even saved lives. But to do this, enterprises need to make decisions in real time, and that means taking their AI compute to where the data is, the network’s edge.
At the edge, IoT and mobile devices use embedded processors to collect data. Edge computing takes the power of AI directly to those devices and processes the captured data at its source—instead of in the cloud or data center. This accelerates the AI pipeline to power real-time decision-making and software-defined autonomous machines.
Processing data at the point of action means data travel is reduced or eliminated, accelerating AI.
When sensitive data is processed locally, it doesn’t need to be sent to the cloud so it’s better protected.
Sending data to the cloud demands bandwidth and storage. Local processing lowers those costs.
Edge computing occurs locally without the need for internet access. That expands the places AI can go.
Edge computing is made for real-time, always-on solutions. By processing data as close to its source as possible, latency is minimized and organizations gain actionable insights in real time. Businesses can respond to customers instantly, deliver critical information to surgeons as they operate, run warehouses with maximum efficiency and safety, drive innovation in autonomous vehicles, and much more.
Smart stores are the future of retail. Learn how leading retailers like Walmart are leaning into AI at the edge to optimize everything from in-store analytics to warehouse operations to last-mile delivery.
Edge AI is helping manufacturers realize the factory of the future. See how BMW Group is using it to get a 360° view of their assembly line and power a safer, more efficient, automated operation.
Liverpool, Australia, is expecting a boom in daily commuters—and that means new infrastructure challenges. Learn how the city is using real-time insights from video streams to predict traffic flows and make better decisions.
AI is helping make our hospitals and healthcare options smarter and safer to deliver better patient care. With edge computing, AI can be brought directly to the examination room, the operating room table, or a patient’s bedside.
The nexus of 5G, the internet of things (IoT), and edge computing is turbocharging network performance and moving telco services out to the edge in connected factories, retail stores, hospitals, and even city streets.
With edge computing, utilities are dynamically forecasting energy demand and managing supply, integrating renewable and distributed energy resources, and enhancing grid resiliency through a software-defined smart grid.
Learn more about real-time performance at the edge.
AI and cloud-native applications, IoT and its billions of sensors, and 5G networking make large-scale AI at the edge possible. Explore the NVIDIA solutions in enterprise edge, embedded edge, and industrial edge, all of which transform that possibility into real-world results, automating intelligence at the point of action and driving decisions in real time.
So what is edge AI? It’s the powerful compute that can bring people, businesses, and accelerated services together, making the world a smaller, more connected place. Subscribe to edge AI news and keep up to date with the latest developments.
Edge computing is computing done at or near the source of data, allowing for the real-time processing of data that’s preferred for intelligent infrastructure. Cloud computing is done within the cloud. This type of computing is highly flexible and scalable, making it ideal for customers who want to get started quickly or those that have varying usage. Both computing models have distinct advantages, which is why many organizations will look to a hybrid approach to computing.
Edge computing offers benefits such as lower latency, higher bandwidth, and data sovereignty compared to traditional cloud or data center computing. Many organizations are looking for real-time intelligence from AI applications. For example, self-driving cars, autonomous machines in factories, and industrial inspection all present a serious safety concern if they can’t act quickly enough—in real time—on the data they ingest.
Edge computing isn't limited to any industry or application. Organizations across every industry are using these solutions to accelerate their applications and take advantage of the benefits of AI at the edge. Examples include smart shopping experiences in retail, intelligent infrastructure in smart cities, and automation of industrial manufacturing.