Create a Faster, Smarter, More Connected World
Billions of IoT sensors across retail stores, city streets, warehouses, and hospitals are producing vast amounts of data. Tapping into this data faster and more efficiently can enhance services, streamline operations, and save lives. To achieve this, enterprises must make real-time decisions by deploying AI computing at the network edge where data is generated.
Benefits of Edge Computing
At the edge, IoT and mobile devices employ embedded processors to gather data. Edge computing brings AI directly to these devices, processing data where it's captured—instead of in the cloud or data center. This speeds up the AI pipeline for real-time decision-making and autonomous machines.
Lower Latency
Processing data at the point of action means data travel is reduced or eliminated, accelerating AI.
Improved Security
When sensitive data is processed locally, it doesn’t need to be sent to the cloud, so it’s better protected.
Reduced Cost
Sending data to the cloud demands bandwidth and storage. Local processing lowers those costs.
Wider Reach
Edge computing occurs locally without the need for internet access. That expands the places AI can go.
DGX Spark brings the power of NVIDIA Grace Blackwell™ to developer desktops. The GB10 Superchip, combined with 128 GB of unified system memory, lets AI researchers, data scientists, and students work with AI models locally with up to 200 billion parameters.
Innovate at the Edge With NVIDIA
AI, cloud-native applications, IoT with billions of sensors, and 5G networking enable widespread AI at the edge. Explore NVIDIA solutions in enterprise edge, embedded edge, and industrial edge, all of which deliver real-world results by automating intelligence at the point of action and driving decisions in real time.
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FAQ
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.