Privacy & offline
TurboLLM is offline-first. Core local use needs no account, no backend, and no internet. Nothing about how you use it is measured, and nothing you feed it is uploaded. Your prompts, chats, files, and keys stay on your machine.
No account, no backend, no internet, no telemetry. Your prompts, chats, files, and keys never leave your machine.
No account, no cloud, no login
There is no TurboLLM cloud account to create and no login screen to get past. You run npx turbollm and the app is yours. There is no backend service that your session phones home to, and nothing is uploaded by default. TurboLLM works the same on a fully air-gapped machine as it does on a connected one.
No telemetry, no analytics
TurboLLM collects no analytics and no telemetry. It does not count your requests, track which models you load, time your sessions, or report crashes to anyone. There is no usage stream to opt out of, because there is no usage stream. Honesty is the brand promise here: the same reason TurboLLM never prints a faked tokens/sec number is the reason it never quietly measures you.
Everything is stored locally
Your data lives on your disk, under a single directory in your home folder:
~/.turbollm/Chats, settings, and keys are kept there on your own machine. Nothing is synced to a server, because there is no server to sync to. If you want a clean slate, that directory is the whole footprint.
When TurboLLM touches the network
TurboLLM reaches the network only when you ask it to. There is no background traffic. The exhaustive list of moments it goes online:
Downloading an engine build
When you choose to install an inference engine, TurboLLM fetches that build. No engine is downloaded until you pick it.
Downloading a model
When you pull a model from Hugging Face, TurboLLM downloads the weights you selected. Nothing is fetched unless you ask for it.
An in-chat tool call you approve
A web_search or fetch_url tool call reaches the internet only after you approve it. Every tool call passes through an approval gate first.
Auto-memory stays on-device
Auto-memory is experimental and off by default. When you turn it on, it uses your own loaded local model to extract facts from your conversation. That work happens entirely on your machine using the model you already have running — nothing leaves the device to make it happen.