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offline llm

Offline LLM for air-gapped and sovereign environments

Keep inference available when the network is unavailable, restricted, or inappropriate for the data being handled.

OpenJet helps you run an offline LLM with a terminal-native interface, guarded tools, and hardware-aware inference. It is built for environments where prompts, files, logs, and system metadata should not leave the device.

Why local wins

  • Runs without network access. Once the runtime and models are installed, OpenJet can operate fully offline on the device.
  • Better fit for data sovereignty. Prompts, logs, and system metadata stay local instead of being exported to a hosted AI service, which is important for sovereignty-sensitive environments.
  • Practical for air-gapped ops. OpenJet still gives operators a usable interface for local logs, commands, and procedures even when the network is unavailable by policy or by design.
  • Embeddable, not just interactive. The same offline runtime is exposed as a Python SDK, so an on-device model can sit inside your own tooling instead of only behind a terminal a human has to drive.

Common deployment paths

  • Run an offline LLM in air-gapped environments with no outbound access.
  • Keep operational context local for regulated, sovereign, or privacy-sensitive systems.
  • Use local AI in environments where cloud log export would be a policy or compliance problem.
  • Embed an offline model directly in an internal application with the Python SDK.

Frequently asked questions

Can OpenJet run as a fully offline LLM?
Yes. Once the runtime and model assets are installed, OpenJet can operate without a network connection.
Why use an offline LLM instead of a hosted API?
An offline LLM is a better fit when network access is unavailable, restricted, or inappropriate for the data being handled.
Does offline just mean private?
No. Offline also matters for availability, reliability, sovereignty, and keeping operations working when cloud access is impossible.

Explore OpenJet

Embed a local model with the Python SDK, run Qwen3.8-27B as a local coding agent, compare local coding-agent alternatives, read the docs, or go back to the homepage.