Modular edge compute · distributed inference
Rover Edge AI
A modular compute node that puts inference where the data is made. Edge AI nodes distribute through the network rather than concentrating in one place, so the field tasks that depend on them keep running when the path back to anywhere else does not.
Why distributed rather than central
A model in a datacentre is a dependency on the link
Centralised inference works until the uplink degrades, and field operations are exactly where uplinks degrade. Moving the model to the node changes what a lost link costs: a degraded uplink becomes a reporting delay rather than a capability outage.
Inference stays local
Workloads run on the node next to the sensors, cameras, or radios feeding them. Results are available where the decision is made rather than after a round trip.
Survivable by construction
No node depends on a central service to do its job. Losing the link, or losing a neighbouring node, removes capacity rather than removing the capability.
Modular
Nodes are specified per workload and added where the work is, so capacity follows the operation instead of being sized once for the whole estate up front.
Inside the same network
Nodes sit behind the same Zero Trust policy plane as Rover routers, switches, and gateways, and report telemetry to the same place. An inference node is not a policy exception.
Deployment
Where it runs
- Fixed site
- Alongside a campus build or a Private Cellular deployment
- Mobile
- In a RapidSTACK kit, vehicle or portable
- Distributed
- Multiple nodes across a site or across sites, working as one pool
- Disconnected
- Continues operating through a degraded or absent uplink
Specification
- Form
- Modular node, configured per workload
- Sizing
- Scoped against the models and throughput a programme actually needs
- Policy
- Same AAA, segmentation, and telemetry as the rest of the estate
- Build options
- COTS, modular variants, and custom builds
- Detail
- Hardware configurations and benchmark figures provided on request