> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deploy/enterprise-deployment/offline-mode.md). # Offline Mode {% hint style="info" %} Offline Mode for Roboflow Enterprise customers requires that you use our Docker container. {% endhint %} Roboflow Enterprise customers can configure Roboflow Inference, our on-device inference server, to cache weights for up to 30 days. This allows your model to run completely air-gapped or in locations where an Internet connection is not readily available. To run your model offline, you need to: 1. Create and attach a Docker volume to `/tmp/cache` on the your Inference Server. 2. Start a Roboflow Inference server with Docker. 3. Make a request to your model through the server, which will initiate the model weight download and cache process. You will need an internet connection for this step. Once your weights have been cached, you can use them locally. Below, we provide instructions for how to run your model offline on various device types, from CPU to GPU. ## CPU Image: [roboflow / roboflow-inference-server-cpu](https://hub.docker.com/r/roboflow/roboflow-inference-server-cpu) ```bash sudo docker volume create roboflow sudo docker run --net=host --env LICENSE_SERVER=10.0.1.1 --mount source=roboflow,target=/tmp/cache roboflow/roboflow-inference-server-cpu ``` ## GPU To use the GPU container, you must first install [nvidia-container-runtime](https://github.com/NVIDIA/nvidia-container-runtime). Image:[ roboflow / roboflow-inference-server-gpu](https://hub.docker.com/r/roboflow/roboflow-inference-server-gpu) ```bash sudo docker volume create roboflow docker run -it --rm -p 9001:9001 --gpus all --mount source=roboflow,target=/tmp/cache roboflow/roboflow-inference-server-gpu ``` ## Jetson 4.5 Your Jetson Jetpack 4.5 will already have installed. Image:[ roboflow/roboflow-inference-server-jetson-4.5.0](https://hub.docker.com/r/roboflow/roboflow-inference-server-jetson-4.5.0) 
sudo docker volume create roboflow
docker run -it --rm -p 9001:9001 --runtime=nvidia --mount source=roboflow,target=/tmp/cache roboflow/roboflow-inference-server-jetson-4.5.0
## Jetson 4.6 Your Jetson Jetpack 4.6 will already have installed. Image:[ roboflow/roboflow-inference-server-jetson-4.6.1](https://hub.docker.com/r/roboflow/roboflow-inference-server-jetson-4.6.1/tags) 
sudo docker volume create roboflow
docker run -it --rm -p 9001:9001 --runtime=nvidia --mount source=roboflow,target=/tmp/cache roboflow/roboflow-inference-server-jetson-4.6.1
## Jetson 5.1 Your Jetson Jetpack 5.1 will already have installed. Image: [roboflow/roboflow-inference-server-jetson-5.1.1](https://hub.docker.com/r/roboflow/roboflow-inference-server-jetson-5.1.1) ```bash sudo docker volume create roboflow docker run -it --rm -p 9001:9001 --runtime=nvidia --mount source=roboflow,target=/tmp/cache roboflow/roboflow-inference-server-jetson-5.1.1 ``` ## Running Inference With your Inference server set up with local caching, you can run your model on images and video frames without an internet connection. Refer to the "[Predict on an Image Over HTTP](https://inference.roboflow.com/quickstart/run_model_on_image/)" Inference documentation for guidance on how to run your model. ## Inference Results The weights will be loaded from the your Roboflow account over the Internet (via the License Server if you have configured it) with SSL encryption and stored safely in the Docker volume for up to 30 days. Your inference results will contain a new `expiration` key you can use to determine how long the Inference Server can continue to provide predictions before renewing its lease on the weights via an Internet or License Server connection. Once the weight expiration date drops below 7 days, the Inference Server will begin trying to renew the weights' lease once per hour until a connection to the Roboflow API is successfully made. Once the lease has been renewed, the counter will reset to 30 days. ```json { "predictions": [ { "x": 340.9, "y": 263.6, "width": 284, "height": 360, "class": "example", "confidence": 0.867 } ], "expiration": { "value": 29.91251408564815, "unit": "days" } } ``` {% hint style="info" %} If you have questions about deploying your model offline, contact your Roboflow representative for guidance. {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.roboflow.com/deploy/enterprise-deployment/offline-mode.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.