Edge AI & local intelligence
Place AI workloads near devices and users when latency, connectivity, privacy or operating constraints make local execution valuable.
What must continue to work when the cloud is unavailable?
Describe the workload and its operating environment before choosing hardware. Identify the required response time, memory, power and connectivity. Decide which capabilities must remain local and which can tolerate delayed synchronisation.
Compare model size and runtime options against representative workloads. Account for sustained performance, thermal behaviour and shared resources. Design local data handling and version management alongside the inference path.
Plan how the system updates, observes failures and recovers. Test loss of connectivity, interrupted updates and resource pressure. Keep remote management and local autonomy boundaries clear for the people responsible for operation.
What to take forward
- Workload and hardware constraints
- Local/cloud partition and inference approach
- Offline, update and recovery test plan
What should I bring to a conversation?
Bring the decision you need to make, the constraints you already know and a small, non-confidential example. We can then agree whether a resource, a focused review or a more detailed discussion is the right next step.