Run Ultralytics YOLO on Raspberry Pi with OpenVINO 15th Jul 2026 Naush Patuck 0 comments Friends of ours from Intel and Ultralytics – Alexander Nesterov, Dmitriy Pastushenkov, Francesco Mattioli, and Nuvola Ladi – are here to teach you how to run YOLO on Raspberry Pi using OpenVINO. This guide focuses on deploying Ultralytics YOLO computer vision models on Raspberry Pi with OpenVINO. We’ll cover how the runtime is installed, how models become deployment artefacts, how compilation and caching affect startup, and how builds become repeatable. The standard OpenVINO deployment practices still apply. Convert the model when load latency matters, compile for the target device, cache compiled artefacts, and package the runtime explicitly. This gives Raspberry Pi a clear role.
It is not an exception to OpenVINO, but rather a small Linux Arm64 target to which the standard OpenVINO deployment model can be applied directly. System setup at a glance The recommended first path is as follows: Use Raspberry Pi OS (64-bit) on Raspberry Pi 4 or Raspberry Pi 5 Create a clean Python virtual environment Install OpenVINO from PyPI Verify that OpenVINO sees the CPU device Install the Ultralytics Python package Prefer OpenVINO IR for deployment when startup time matters Enable model caching for services that restart often Move to source builds or cross-builds when you need control over OpenVINO runtime packaging, C++ integration, or repeatable release artefacts Raspberry Pi is the target, OpenVINO is the deployment layer Raspberry Pi is a common target for practical computer vision. It can sit next to a camera, inside a prototype, near a machine, on a lab bench, or in a small service that needs to run without a workstation nearby. OpenVINO gives Raspberry Pi boards a standard deployment shape.
Source: https://www.raspberrypi.com/news/run-ultralytics-yolo-on-raspberry-pi-with-openvino/
It is not an exception to OpenVINO, but rather a small Linux Arm64 target to which the standard OpenVINO deployment model can be applied directly. System setup at a glance The recommended first path is as follows: Use Raspberry Pi OS (64-bit) on Raspberry Pi 4 or Raspberry Pi 5 Create a clean Python virtual environment Install OpenVINO from PyPI Verify that OpenVINO sees the CPU device Install the Ultralytics Python package Prefer OpenVINO IR for deployment when startup time matters Enable model caching for services that restart often Move to source builds or cross-builds when you need control over OpenVINO runtime packaging, C++ integration, or repeatable release artefacts Raspberry Pi is the target, OpenVINO is the deployment layer Raspberry Pi is a common target for practical computer vision. It can sit next to a camera, inside a prototype, near a machine, on a lab bench, or in a small service that needs to run without a workstation nearby. OpenVINO gives Raspberry Pi boards a standard deployment shape.
Source: https://www.raspberrypi.com/news/run-ultralytics-yolo-on-raspberry-pi-with-openvino/