On-Premise Computer Vision: Run Vision AI on Your Own Servers

On-premise computer vision runs production inference on hardware controlled by a factory or organization, keeping camera data and real-time decisions local while allowing training, monitoring, and model management to remain local, cloud-connected, or fully offline depending on requirements. It is presented as a way to address sensitive data handling, IT/OT network separation, low-latency machine control, resilience to internet outages, and potentially more predictable costs for continuous multi-camera workloads. The deployment approaches described include fully air-gapped environments, local inference with selectively cloud-based retraining, dedicated or customer-controlled cloud infrastructure, and line-side edge appliances such as NVIDIA Jetson systems or Roboflow AI1. Roboflow Inference, RF-DETR models, and Workflows can operate on CPU servers, NVIDIA GPU systems, Jetson hardware, and edge devices, with GPU sizing determined by complete workload testing rather than model latency alone. The implementation process involves deploying an Inference Server, loading models locally, connecting RTSP or other camera feeds, running workflows, and sending production decisions through industrial integrations such as PLC, OPC UA, or Modbus. Maintaining accuracy requires collecting meaningful examples of failures, new conditions, and data drift for retraining, while production approval depends on documented data flows, storage, connectivity, access controls, update processes, failure behavior, and logging.

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