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Install on Windows

Install the Roboflow Inference Server on Windows with the native installer or with Docker Desktop, on CPU, GPU, or TensorRT.

Windows installer (x86)

You can run the Roboflow Inference Server on your Windows machine with the native desktop app. Download the latest Windows installer from the latest GitHub release: View the latest release and download installers on GitHub.

  1. Download the latest installer and run it to install Roboflow Inference.
  2. When the install finishes, it offers to launch the Inference Server.
  3. To stop the server, close the terminal window it opens.
  4. To start it again later, find Roboflow Inference in your Start Menu.

inference-models backend. When used with the inference-models backend, the Inference Server must run with elevated admin rights because of cache management with symlinks. The alternative is to enable Developer Mode.

The inference-models backend is opt-in via an environment flag: $env:USE_INFERENCE_MODELS = "True".

Using Docker

First, install Docker Desktop. Then use the CLI to start the container.

pip install inference-cli
inference server start

If the pip install command fails, you may need to install Python first. Once you have Python 3.12, 3.11, or 3.10 on your machine, retry the command.

Manually starting the container

If you want more control over the container settings, start it yourself.

The core CPU Docker image includes support for OpenVINO acceleration on x64 CPUs via onnxruntime. Heavy models like SAM2 may run too slowly (dozens of seconds per image) to be practical; if you need them, use a CUDA-capable GPU.

The primary use cases for CPU inference are processing still images (for example NSFW classification of uploads or document verification) or infrequent sampling of frames from a video (for example occupancy tracking of a parking lot).

To get started with CPU inference, use the roboflow/roboflow-inference-server-cpu:latest container.

docker run -d ^
    --name inference-server ^
    --read-only ^
    -p 9001:9001 ^
    --volume "%USERPROFILE%\.inference\cache:/tmp:rw" ^
    --security-opt="no-new-privileges" ^
    --cap-drop="ALL" ^
    --cap-add="NET_BIND_SERVICE" ^
    roboflow/roboflow-inference-server-cpu:latest

Docker Compose

If you use Docker Compose for your application, the equivalent YAML is:

version: "3.9"

services:
  inference-server:
    container_name: inference-server
    image: roboflow/roboflow-inference-server-cpu:latest

    read_only: true
    ports:
      - "9001:9001"

    volumes:
      - "${USERPROFILE}/.inference/cache:/tmp:rw"

    security_opt:
      - no-new-privileges
    cap_drop:
      - ALL
    cap_add:
      - NET_BIND_SERVICE

Roboflow Enterprise plans add a Helm chart for Kubernetes deployments, networking solutions for OT networks, and customized support and installation packages. Contact the sales team to learn more.

Next steps