Hybrid inference splits deployed AI work between user-controlled hardware and remote services. Routine or sensitive tasks can run locally, while requests that need greater capability, larger context, persistent infrastructure, or heavy parallel compute can use the cloud.
The design needs a routing policy and clear data boundaries. Applications should disclose when information leaves the device, handle cloud unavailability, compare results across models, and avoid assuming that one location is always cheaper or better.
ELI5
Hybrid inference combines AI running on a local device with models running in the cloud. Each request goes to the environment that best fits its privacy, capability, context, cost and availability needs.
For example, a laptop can handle private routine summaries locally and send a difficult, approved task to a stronger cloud model. The application should disclose when data leaves the device and keep working safely when the network is unavailable.
Why combine local and cloud AI inference?
The combination can balance privacy, responsiveness, cost control, model capability, context capacity, and access to large infrastructure.
What is the main challenge in hybrid inference?
The system must choose the right execution location while preserving user expectations about data, latency, cost, and reliability.
