Edge AI

Edge AI refers to running AI inference on devices at the 'edge' of the network — on smartphones, IoT devices, cameras, industrial equipment, vehicles, or local edge servers — rather than sending data to cloud servers for processing. The AI model is deployed directly on the device where the data is generated. Edge AI requires models that are small and efficient enough to run on limited hardware. Techniques like quantisation, pruning, and distillation compress models for edge deployment. Specialised hardware (NPUs in smartphones, edge AI accelerators, compact GPU modules) provides the compute needed for on-device inference. The edge AI ecosystem spans the full range of AI tasks: computer vision on security cameras, natural language processing on smart speakers, predictive maintenance on industrial sensors, and autonomous navigation in vehicles. Each application requires balancing model capability with device constraints.

Edge AI offers three primary advantages: latency (responses in milliseconds rather than the hundreds of milliseconds required for cloud round-trips), privacy (sensitive data never leaves the device), and reliability (operation continues without internet connectivity). Manufacturing and industrial applications benefit from edge AI for real-time quality inspection, predictive maintenance, and safety monitoring. Healthcare applications process patient data on-device for privacy compliance. Retail applications run computer vision for inventory management and checkout automation. The trade-off is model capability. Edge devices cannot run the largest, most capable AI models. Organisations must decide which tasks require the full power of cloud AI and which can be handled effectively by smaller, edge-deployed models. Hybrid architectures — using edge AI for initial processing and cloud AI for complex analysis — are increasingly common.