Local AI deployment: architecture considerations
A technical note on data flow, evaluation, and operational ownership in local inference systems.
Local deployment raises questions about where data goes, how the model is evaluated, and who operates the system. This note outlines those considerations; examples of my project work are on the selected work page.
Start with the boundary
Some organizations need model capability without sending their documents, prompts, or logs to an external API. Local inference can support that requirement, but the model endpoint is only one part of the data flow.
Storage, retrieval, access control, logs, telemetry, and updates need the same scrutiny. The operating requirements determine whether an air gap, an on-premises deployment, or another arrangement makes sense.
Make the constraints explicit
Hardware capacity, latency, model quality, and operating costs affect each other. Evaluation should use the intended workload, including the cases where the system needs to decline a request or fall back to a different path.