Install OpenVINO™ Runtime on macOS from an Archive File
=========================================================


.. meta::
   :description: Learn how to install OpenVINO™ Runtime on macOS operating
                 system, using an archive file.


.. note::

   Note that the Archive distribution:

   * offers both C/C++ and Python APIs
   * additionally includes code samples
   * is dedicated to macOS users (archives for other systems are also available)
   * is only supported for CPU Plugin

   Before installing OpenVINO, see the
   :doc:`System Requirements page <../../../about-openvino/release-notes-openvino/system-requirements>`.


Installing OpenVINO Runtime
###########################

Step 1: Install OpenVINO Core Components
++++++++++++++++++++++++++++++++++++++++


1. Open a command prompt terminal window.
2. Create the ``/opt/intel`` folder for OpenVINO by using the following command. If the folder already exists, skip this command.

   .. code-block:: sh

      sudo mkdir /opt/intel


   .. note::

      The ``/opt/intel`` path is the recommended folder path for installing OpenVINO. You may use a different path if desired.


3. Browse to the current user's ``Downloads`` folder:

   .. code-block:: sh

      cd <user_home>/Downloads


4. Download the `OpenVINO Runtime archive file for macOS <https://storage.openvinotoolkit.org/repositories/openvino/packages/2025.3/macos/>`__,
   extract the files, rename the extracted folder and move it to the desired path:

   .. tab-set::

      .. tab-item:: x86, 64-bit
         :sync: x86-64

         .. code-block:: sh


            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2025.3/macos/openvino_toolkit_macos_12_6_2025.3.0.19807.44526285f24_x86_64.tgz --output openvino_2025.3.0.tgz
            tar -xf openvino_2025.3.0.tgz
            sudo mv openvino_toolkit_macos_12_6_2025.3.0.19807.44526285f24_x86_64 /opt/intel/openvino_2025.3.0

      .. tab-item:: ARM, 64-bit
         :sync: arm-64

         .. code-block:: sh


            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2025.3/macos/openvino_toolkit_macos_12_6_2025.3.0.19807.44526285f24_arm64.tgz --output openvino_2025.3.0.tgz
            tar -xf openvino_2025.3.0.tgz
            sudo mv openvino_toolkit_macos_12_6_2025.3.0.19807.44526285f24_arm64 /opt/intel/openvino_2025.3.0


5. (Optional) Install *numpy* Python Library:

   .. note::

      This step is required only when you decide to use Python API.

   You can use the ``requirements.txt`` file from the ``/opt/intel/openvino_2025.3.0/python`` folder:

   .. code-block:: sh

      cd /opt/intel/openvino_2025.3.0
      python3 -m pip install -r ./python/requirements.txt

6. For simplicity, it is useful to create a symbolic link as below:

   .. code-block:: sh


      sudo ln -s /opt/intel/openvino_2025.3.0 /opt/intel/openvino_2025


   .. note::

      If you have already installed a previous release of OpenVINO 2025, a symbolic link to the ``openvino_2025`` folder may already exist. Unlink the previous link with ``sudo unlink openvino_2025``, and then re-run the command above.


Congratulations, you have finished the installation! For some use cases you may still
need to install additional components. Check the description below, as well as the
:doc:`list of additional configurations <./configurations>`
to see if your case needs any of them.

The ``/opt/intel/openvino_2025`` folder now contains the core components for OpenVINO.
If you used a different path in Step 2, for example, ``/home/<USER>/intel/``,
OpenVINO is now in ``/home/<USER>/intel/openvino_2025``. The path to the ``openvino_2025``
directory is also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.


Step 2: Configure the Environment
+++++++++++++++++++++++++++++++++

You must update several environment variables before you can compile and run OpenVINO applications. Open a terminal window and run the ``setupvars.sh``
script as shown below to temporarily set your environment variables. If your ``<INSTALL_DIR>`` (the folder you used to install OpenVINO) is not
the default ``/opt/intel/openvino_2025``, use the correct one instead.

.. code-block:: sh

   cd /opt/intel/openvino_2025
   source /opt/intel/openvino_2025/setupvars.sh


If you have more than one OpenVINO™ version on your machine, you can easily switch its version by sourcing the ``setupvars.sh`` of your choice.

.. note::

   The above command must be re-run every time you start a new terminal session. To set up macOS to automatically run the command every time a new terminal is opened, open ``~/.zshrc`` in your favorite editor and add ``source /opt/intel/openvino_2025/setupvars.sh`` after the last line. Next time when you open a terminal, you will see ``[setupvars.sh] OpenVINO™ environment initialized``. Changing ``~/.zshrc`` is not recommended when you have multiple OpenVINO versions on your machine and want to switch among them.



What's Next?
####################

Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning applications! Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials.

.. tab-set::

   .. tab-item:: Get started with Python
      :sync: get-started-py

      Try the `Python Quick Start Example <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/vision-monodepth>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.

      .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
         :width: 400

      Visit the :doc:`Tutorials <../../../get-started/learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:

      * `OpenVINO Python API Tutorial <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/openvino-api>`__
      * `Basic image classification program with Hello Image Classification <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/hello-world>`__
      * `Convert a PyTorch model and use it for image background removal <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/vision-background-removal>`__

   .. tab-item:: Get started with C++
      :sync: get-started-cpp

      Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.

      .. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
         :width: 400

      Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:

      * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
      * :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`

Uninstalling Intel® Distribution of OpenVINO™ Toolkit
#####################################################

If you have installed OpenVINO Runtime from archive files, you can uninstall it by deleting the archive files and the extracted folders.
Uninstallation removes all Intel® Distribution of OpenVINO™ Toolkit component files but does not affect user files in the installation directory.

If you have created the symbolic link, remove the link first:

.. code-block:: sh

   sudo rm /opt/intel/openvino_2025

To delete the files:

.. code-block:: sh

   rm -r <extracted_folder> && rm <path_to_archive>


Additional Resources
####################

* :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <../install-openvino>`
* :doc:`Convert models for use with OpenVINO™ <../../../openvino-workflow/model-preparation/convert-model-to-ir>`
* :doc:`Write your own OpenVINO™ applications <../../../openvino-workflow/running-inference>`
* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`
* Pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__
