Inference placement, model delivery, and request routing are related decisions with different responsibilities. These guides help you decide where a model should run, how the release reaches that environment, and which eligible service should handle each task.
Measure the path from input preparation to a usable output, including transfer and initialization. A fast execution benchmark is only part of that experience. Establish device support, data permissions, model compatibility, and a fallback that the application can explain before optimizing an individual stage.
For the broader architecture, explore Edge AI. Follow the articles below as a reading path, or use the guide directory to connect this topic with the rest of the stack.
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.