Private LLM: beyond a locally hosted model
Deploy a private LLM with controlled data, runtime, observability, authority and explicitly bounded external dependencies.
An LLM is private only when the complete chain—telemetry, models, retrieval, logs and backups—respects the declared boundary.
Downloading weights locally does not remove auxiliary calls, logging leakage or runtime dependencies.
Privacy must be demonstrated at realization and operational levels, not inferred from where weights reside.
What the architecture must make explicit
Privacy and sovereignty are properties of the executed system: network flows, data, telemetry, dependencies, updates, backups and authority must share one controlled perimeter.
Move from a keyword to an inspectable engineering decision
Bound
Define network, organizational and legal boundaries for the realization.
Inventory
Identify models, APIs, telemetry, registries, caches and bootstrap dependencies.
Isolate
Replace or encapsulate dependencies that violate the selected perimeter.
Challenge
Run no-network, service-loss and complete-restoration tests.
A claim is only as strong as its binding
The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.
Questions that change the design
What does private llm: beyond a locally hosted model require?
An LLM is private only when the complete chain—telemetry, models, retrieval, logs and backups—respects the declared boundary. Privacy and sovereignty are properties of the executed system: network flows, data, telemetry, dependencies, updates, backups and authority must share one controlled perimeter.
Which evidence should be retained?
At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. Privacy must be demonstrated at realization and operational levels, not inferred from where weights reside.
Does AMF prescribe a particular model?
No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.