Air-Gapped Computer Vision Deployment: How to Run Offline

Roboflow Enterprise Offline Mode enables computer vision systems to run models and Workflows entirely on local hardware for facilities where camera data, inference outputs, and production decisions must remain within isolated networks. The approach supports fully disconnected, internally isolated, and restricted-egress environments, with local Inference Servers processing camera or RTSP streams and delivering results to plant systems through integrations such as OPC UA, PLC, Modbus, MQTT, databases, and local files. Before disconnecting, teams must cache model weights and Workflow definitions using a persistent Docker cache, then enable offline loading; cached models have 30-day leases that require planned renewal, while network isolation itself must be enforced separately at the operating-system or network level. Local Workflows can include detection, segmentation, OCR, tracking, filtering, quality decisions, and supported local vision-language models, though cloud-dependent blocks such as OpenAI, Gemini, and Slack cannot operate in a strict air gap. The deployment example outlines a PCB inspection pipeline in which RF-DETR analyzes an RTSP stream on a local GPU system and writes pass/fail results to an OPC UA server. Model improvement remains possible through a manual feedback loop that collects selected production cases locally, transfers approved samples to a connected environment for labeling and retraining, and caches updated models and Workflows before returning them to production.

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