Inference is the execution of a model on an input. Delivering its weights, selecting its endpoint, and validating its output each need their own design. Explore how those decisions connect across device, edge, and cloud environments.
Use a representative input set and a pinned model release. Check runtime compatibility, first-use loading, warm execution, and output quality. Decide whether a remote fallback is permitted before the local path fails. An endpoint returning a response is not enough to establish application success.
Connect these ideas with the Edge AI overview. If the question spans several parts of the system, the guide directory offers a sequence through the related decisions.
Reliable model delivery requires more than uploading an ONNX file. Build a release process that binds models to runtime assets, verifies downloaded bytes, handles browser compatibility, and activates updates only when a complete working package is ready.
A useful LLM router chooses among eligible models, budgets the entire request, and measures successful outcomes. Build a practical policy for deadlines, retries, streaming, evaluation, and gradual rollout without hiding quality failures behind lower request prices.
Inference placement starts with the job a model must do. Learn how to compare local devices, nearby servers, and cloud services using complete latency budgets, realistic memory measurements, privacy boundaries, and explicit fallback rules.