Local AI Runtime — Self-Hosted Generative AI Infrastructure

Running a language model locally solves the inference problem, but it does not automatically make that model safe or practical for other applications to use. External systems still need authentication, controlled model access, request boundaries, usage tracking, and a reliable production interface.

I built Local AI Runtime to provide that missing infrastructure layer. Instead of allowing applications to connect directly to Ollama, the runtime provides a controlled API boundary through which trusted systems can access locally hosted generative AI while administration, governance, security, and usage visibility remain centralized.

The runtime separates consuming applications from the underlying inference engine. External systems communicate with the Local AI Runtime through authenticated HTTPS APIs, while the runtime validates requests, verifies machine credentials, enforces model assignments, accounts for usage, and then communicates privately with Ollama.

Ollama remains loopback-only and is never exposed as the application-facing AI endpoint. This creates a reusable infrastructure boundary where applications consume generative AI without needing direct access to the model runtime or responsibility for managing its operational controls.

The Local AI Runtime includes a dedicated administrative control plane so routine AI infrastructure operations do not require direct server or inference-engine access. Administrators can generate credentials for consuming applications, assign approved models, inspect existing keys, enable or revoke access, review runtime and model information, and monitor consumer activity through a dedicated web interface.

This turns infrastructure operations that would otherwise require manual developer intervention into controlled administrative workflows while keeping the underlying inference engine isolated from users and external applications.

Each consuming application receives its own machine identity rather than sharing a common credential. API keys are associated with individual consumers and persistently assigned to an approved model, allowing access and usage to be managed independently for each connected system.

Model installation and model authorization are intentionally separated. A model being installed on the server does not automatically make it available to applications. Consumers can use their assigned model but cannot arbitrarily switch to another installed model, keeping model policy under administrative control.

The runtime records operational metadata for each inference request so administrators can understand how different applications are consuming local AI. Usage reporting includes request counts, success and failure status, prompt and output tokens, execution duration, model attribution, last-used information, and consumer-level activity.

This visibility was designed with data minimization in mind. Prompt content, generated responses, structured schemas, authorization credentials, and raw provider responses are not persisted for usage accounting. The system therefore provides operational and token visibility without turning the infrastructure layer into a repository of application conversations.

The production deployment keeps both the backend service and Ollama behind controlled network boundaries. NGINX provides HTTPS termination, the Local AI Runtime backend operates on a loopback interface, Ollama remains loopback-only, and systemd manages the production service. Rate limits, bounded request sizes, separate administrative and machine identities, and model-governance controls add further protection around inference access.

The runtime was then validated against a separate production application through its intended machine-to-machine API boundary. A real generation request travelled from the external application through its provider layer into the authenticated Local AI Runtime, executed through Ollama using the assigned Llama 3.2:3b model, and returned successfully with matching model and token accounting across both systems.