What is Edge Computing? - Explanation & Meaning

Definition

Edge computing is a distributed computing paradigm that places data processing and storage closer to the source of the data — at the "edge" of the network — rather than sending everything to a central data center or the cloud.

Technical explanation

Edge computing addresses the limitations of pure cloud architectures: latency, bandwidth, and privacy. By processing data on or near the device that generates it — via edge servers, gateways, or the device itself — response times are reduced from hundreds of milliseconds to single-digit milliseconds. This is crucial for real-time applications such as autonomous vehicles, industrial robotics, and augmented reality. In 2026, 5G connectivity enhances the edge by providing high bandwidth and low latency to mobile and IoT devices. Edge AI combines edge computing with AI inference: optimized models (via TensorRT, ONNX Runtime, or TensorFlow Lite) run locally on edge hardware such as NVIDIA Jetson, Intel NUC, or specialized AI chips. Multi-access edge computing (MEC) brings compute power to the mobile network itself. Fog computing acts as an intermediate layer between edge and cloud. Container orchestration via K3s (lightweight Kubernetes) enables distributed management of edge workloads. Challenges include securing distributed nodes, over-the-air updates, and monitoring thousands of edge devices.

How MG Software applies this

At MG Software, we advise on and implement edge computing architectures for clients who need low latency, offline functionality, or local data processing. We deploy containerized applications on edge hardware and seamlessly integrate edge nodes with cloud backends for data aggregation and monitoring.

Practical examples

• A manufacturing facility deploying edge computing for real-time quality control via computer vision, where AI models run directly on camera gateways and detect defects in under 10 milliseconds — without dependency on cloud connectivity.
• A retail chain placing edge servers in each store to process point-of-sale transactions locally, ensuring the system continues functioning during internet outages and processing customer data locally in compliance with GDPR.
• A smart city project using edge computing to analyze traffic data from thousands of sensors locally and sending only aggregated insights to the cloud, reducing bandwidth costs by 80%.

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