Purpose-sized compute for real-time video analytics

RIFE EDGE AI hardware is selected around the analytics workload, not just the number of cameras. A four-camera deployment running several demanding models can require a different configuration from a sixteen-camera deployment using lighter analytics. The objective is to provide enough compute, memory, storage and network capacity for reliable operation with room for the agreed growth plan.

Deployment classes

Compact edge node

Suitable for limited camera groups, distributed sites or focused pilots where a small local processing footprint is preferred.

Standard edge server

Designed for broader site deployments with multiple camera streams, multiple analytics and local management requirements.

Enterprise GPU server

Used when higher stream counts, more demanding models, centralized site processing, redundancy or future expansion require greater compute capacity.

These are sizing categories rather than fixed universal specifications. Final hardware is confirmed in the project bill of materials.

What RIFE evaluates when sizing hardware

• Number of simultaneous video streams
• Resolution, codec and frame rate
• Number and type of AI models per stream
• Inference frequency and required latency
• Dashboard and concurrent-user load
• Event-image and clip retention
• Network interface requirements
• Local storage needs
• Rack, desktop or industrial mounting environment
• Power, cooling and ambient conditions
• Redundancy and future expansion

Why camera capacity is not a single number

A published statement such as “one server supports X cameras” can be misleading unless the resolution, model and workload are specified. RIFE therefore provides capacity against an agreed test configuration. If the project changes from one AI model to several models per stream, or from a lower-resolution sub-stream to a higher-resolution stream, compute requirements may change.

Typical hardware specification schedule

For procurement, RIFE can provide a configuration schedule covering the relevant items for the selected appliance, such as processor, GPU/accelerator, RAM, storage, network interfaces, operating system, power supply, form factor, dimensions and environmental requirements.

Only verified specifications should be published against a named RIFE appliance model.

Local storage and evidence

Storage requirements depend on whether the system retains metadata only, event snapshots, short clips or continuous video. RIFE calculates storage separately from AI compute so that retention policy and performance can be designed intentionally.

Health monitoring and lifecycle

Enterprise deployments should include a clear method for checking service availability, storage health, camera connectivity, compute utilization and software version. Remote monitoring or support functions depend on the selected architecture and customer security policy.

Related RIFE EDGE AI resources

Trust & Technology Center · Architecture · Cameras & Integrations · Pilot Program

Get a hardware sizing recommendation

Provide the camera count, resolution, proposed AI applications, retention requirement and site architecture. RIFE can prepare an indicative hardware class and validate it during the pilot or technical design stage.

Request edge AI sizing

Continue through the RIFE EDGE AI technical ecosystem

RIFE EDGE AI Deployment, Support & Technical FAQRIFE EDGE AI Pilot ProgramCameras, VMS & Enterprise Integrations — RIFE EDGE AI