Distributed AI inference spreads model-serving work across multiple machines. A coordinator or routing layer sends each request to an eligible node, which loads or already hosts the required model and returns generated output. Capacity can grow as more providers join the network.
The design can use geographically and administratively separate hardware, but it must handle routing, model availability, latency, privacy, failures, and inconsistent node performance. Distribution does not automatically make inference faster or safer. Those outcomes depend on scheduling, verification, and the trust model between users and providers.
ELI5
Distributed AI inference serves model requests using several networked computers instead of one machine. A coordinator sends each request to a suitable node and returns the generated result.
For example, independent accelerator owners can provide capacity to one inference network. The system must protect user data, verify results and handle slow or unavailable nodes because distribution alone does not guarantee faster or safer service.
