Integraltech Laptops & Desktops Driver Download

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For many companies, the cost savings alone can be a driver towards deploying an edge-computing architecture. Companies that embraced the cloud for many of their applications may have discovered that the costs in bandwidth were higher than they expected.


Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies. Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself, given the increasing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities and even building-automation systems require fast processing and response.

Integraltech Laptops & Desktops Driver Download


“Edge computing has evolved significantly from the days of isolated IT at ROBO [Remote Office Branch Office] locations,” says Kuba Stolarski, a research director at IDC, in the “Worldwide Edge Infrastructure (Compute and Storage) Forecast, 2019-2023” report. “With enhanced interconnectivity enabling improved edge access to more core applications, and with new IoT and industry-specific business use cases, edge infrastructure is poised to be one of the main growth engines in the server and storage market for the next decade and beyond.”

Integraltech Laptops & Desktops Driver Downloads


Integraltech Laptops & Desktops Driver Download

Integraltech laptops & desktops driver downloads

Companies such as NVIDIA have recognized the need for more processing at the edge, which is why we’re seeing new system modules that include artificial intelligence functionality built into them. The company’s latest Jetson Xavier NX module, for example, is smaller than a credit-card, and can be built into smaller devices such as drones, robots and medical devices. AI algorithms require large amounts of processing power, which is why most of them run via cloud services. The growth of AI chipsets that can handle processing at the edge will allow for better real-time responses within applications that need instant computing.