Guide
Edge Hardware Capacity Guide
- Author
- DHI Safety OperationsEdge AI ArchitectureReviewed by DHI Engineering
- Published
- 2026-07-02
- Read time
- 8 min read
- Updated
- 2026-07-02
Who this is for
For IT and engineering teams planning edge hardware for live video inference.
The buyer question
What factors determine edge hardware capacity for safety AI?
Camera streams drive load
Capacity depends on stream resolution, frame rate, codec, camera count, and whether the model uses the main stream or substream.
Model mix matters
A forklift trajectory model, smoke detection model, and fall detection model may create different compute patterns. Size for the actual use case mix.
Plan for operations
Hardware planning should include power, network placement, physical access, logging, update windows, and failover behavior.
How to use this with DHI
Use this page as a pre-pilot checklist. Pick one zone, one event type, one alert owner, and one review cadence. If the current cameras cannot support the workflow, fix the camera plan before expanding the deployment.
Validate "edge hardware capacity guide" on your live feeds.
Coordinate a 30-day architecture review and live camera validation based on the protocol described in "edge hardware capacity guide" for your facility.
Best follow-up: bring the current workflow that "Edge Hardware Capacity Guide" is supposed to improve.
- Book a demoSee the flow on a real operating scenario and scope a pilot around one facility or corridor.
- See deployment architectureReview camera ingest, edge inference, alert routing, and what stays on-premises.
- Get the implementation checklistDownload the deployment checklist buyers use before green-lighting an industrial AI pilot.
- Talk to an engineerBring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.