Definition
Edge node
Local compute hardware that runs model inference beside the camera network.
What it means
An edge node is the local server or hardware appliance that ingests camera streams, runs inference, and emits safety events. It is the physical layer that keeps the first alert local.
Why it matters
Node sizing affects camera count, model count, latency, failover, and deployment cost.
Evaluation questions
How many streams and models will run on each node?
Where will the node sit on the network?
What happens if the node or stream restarts?
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NVIDIA Jetson edge AI
Review local edge hardware fit.
Platform architecture
Review how DHI runs inference, event routing, and camera ingest.
Edge AI safety evaluation guide
Use a structured checklist to evaluate platform fit before a pilot.
Pricing and pilot scope
Understand what changes the final pilot and rollout scope.
Validate edge node in a real pilot.
Use your current cameras, VMS, and response workflow to test whether the concept works in one defined zone.
The checklist is built for operators evaluating a live pilot in the next 30 days.
Request a demo
See the flow on a real operating scenario and scope a pilot around one facility or corridor.
See deployment architecture
Review camera ingest, edge inference, alert routing, and what stays on-premises.
Get the implementation checklist
Download the deployment checklist buyers use before green-lighting an industrial AI pilot.
Talk to an engineer
Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.