[DOCS] minor adjustments preparing for 2025 (#28325)

docs/articles_en/about-openvino/compatibility-and-support/supported-devices.rst

@@ -90,16 +90,3 @@ topic (step 3 "Configure input and output").
    | \* **Of the Linux systems, versions 22.04 and 24.04 include drivers for NPU.**
    |  **For Windows, CPU inference on ARM64 is not supported.**
 
-
-.. note::
-
-   With the OpenVINO 2024.0 release, support for GNA has been discontinued. To keep using it
-   in your solutions, revert to the 2023.3 (LTS) version.
-
-   With the OpenVINO™ 2023.0 release, support has been cancelled for:
-
-   - Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X
-   - Intel® Vision Accelerator Design with Intel® Movidius™
-
-   To keep using the MYRIAD and HDDL plugins with your hardware,
-   revert to the OpenVINO 2022.3 (LTS) version.

docs/articles_en/documentation.rst

@@ -16,6 +16,7 @@ Documentation
    Tool Ecosystem <documentation/openvino-ecosystem>
    OpenVINO Extensibility <documentation/openvino-extensibility>
    OpenVINO™ Security <documentation/openvino-security>
+   Legacy Features <documentation/legacy-features>
 
 
 This section provides reference documents that guide you through the OpenVINO toolkit workflow, from preparing models, optimizing them, to deploying them in your own deep learning applications.

docs/articles_en/documentation/legacy-features.rst

@@ -0,0 +1,112 @@
+Legacy Features and Components
+==============================
+
+.. meta::
+   :description: A list of deprecated OpenVINO™ components.
+
+Since OpenVINO has grown very rapidly in recent years, a number of its features
+and components have been replaced by other solutions. Some of them are still
+supported to assure OpenVINO users are given enough time to adjust their projects,
+before the features are fully discontinued.
+
+This section will give you an overview of these major changes and tell you how
+you can proceed to get the best experience and results with the current OpenVINO
+offering.
+
+
+Discontinued:
+#############
+
+.. dropdown:: OpenVINO Development Tools Package
+
+   |   *New solution:* OpenVINO Runtime includes all supported components
+   |   *Old solution:* `See how to install Development Tools <https://docs.openvino.ai/2024/documentation/legacy-features/install-dev-tools.html>`__
+   |
+   |   OpenVINO Development Tools used to be the OpenVINO package with tools for
+       advanced operations on models, such as Model conversion API, Benchmark Tool,
+       Accuracy Checker, Annotation Converter, Post-Training Optimization Tool,
+       and Open Model Zoo tools. Most of these tools have been either removed,
+       replaced by other solutions, or moved to the OpenVINO Runtime package.
+
+.. dropdown:: Model Optimizer / Conversion API
+
+   |   *New solution:* :doc:`Direct model support and OpenVINO Converter (OVC) <../openvino-workflow/model-preparation>`
+   |   *Old solution:* `Legacy Conversion API <https://docs.openvino.ai/2024/documentation/legacy-features/transition-legacy-conversion-api.html>`__
+   |
+   |   The role of Model Optimizer and later the Conversion API was largely reduced
+       when all major model frameworks became supported directly. For converting model
+       files explicitly, it has been replaced with a more light-weight and efficient
+       solution, the OpenVINO Converter (launched with OpenVINO 2023.1).
+
+.. dropdown:: Open Model ZOO
+
+   |   *New solution:* users are encouraged to use public model repositories such as `Hugging Face <https://huggingface.co/OpenVINO>`__
+   |   *Old solution:* `Open Model ZOO <https://docs.openvino.ai/2024/documentation/legacy-features/model-zoo.html>`__
+   |
+   |   Open Model ZOO provided a collection of models prepared for use with OpenVINO,
+       and a small set of tools enabling a level of automation for the process.
+       Since the tools have been mostly replaced by other solutions and several
+       other model repositories have recently grown in size and popularity,
+       Open Model ZOO will no longer be maintained. You may still use its resources
+       until they are fully removed. `Check the OMZ GitHub project <https://github.com/openvinotoolkit/open_model_zoo>`__
+
+.. dropdown:: Multi-Device Execution
+
+   |   *New solution:* :doc:`Automatic Device Selection <../openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection>`
+   |   *Old solution:* `Check the legacy solution <https://docs.openvino.ai/2024/documentation/legacy-features/multi-device.html>`__
+   |
+   |   The behavior and results of the Multi-Device Execution mode are covered by the ``CUMULATIVE_THROUGHPUT``
+       option of the Automatic Device Selection. The only difference is that ``CUMULATIVE_THROUGHPUT`` uses
+       the devices specified by AUTO, which means that adding devices manually is not mandatory,
+       while with MULTI, the devices had to be specified before the inference.
+
+.. dropdown:: Caffe, and Kaldi model formats
+
+   |   *New solution:* conversion to ONNX via external tools
+   |   *Old solution:* model support discontinued with OpenVINO 2024.0
+   |      `The last version supporting Apache MXNet, Caffe, and Kaldi model formats <https://docs.openvino.ai/2023.3/mxnet_caffe_kaldi.html>`__
+   |      :doc:`See the currently supported frameworks <../openvino-workflow/model-preparation>`
+
+.. dropdown:: Post-training Optimization Tool (POT)
+
+   |   *New solution:* Neural Network Compression Framework (NNCF) now offers the same functionality
+   |   *Old solution:* POT discontinued with OpenVINO 2024.0
+   |      :doc:`See how to use NNCF for model optimization <../openvino-workflow/model-optimization>`
+   |      `Check the NNCF GitHub project, including documentation <https://github.com/openvinotoolkit/nncf>`__
+
+.. dropdown:: Inference API 1.0
+
+   |   *New solution:* API 2.0 launched in OpenVINO 2022.1
+   |   *Old solution:* discontinued with OpenVINO 2024.0
+   |      `2023.2 is the last version supporting API 1.0 <https://docs.openvino.ai/archives/index.html#:~:text=2023.2,Release%20Notes>`__
+
+.. dropdown:: Compile tool
+
+   |   *New solution:* the tool is no longer needed
+   |   *Old solution:* discontinued with OpenVINO 2023.0
+   |      If you need to compile a model for inference on a specific device, use the following script:
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            .. doxygensnippet:: docs/articles_en/assets/snippets/export_compiled_model.py
+               :language: python
+               :fragment: [export_compiled_model]
+
+         .. tab-item:: C++
+            :sync: cpp
+
+            .. doxygensnippet:: docs/articles_en/assets/snippets/export_compiled_model.cpp
+               :language: cpp
+               :fragment: [export_compiled_model]
+
+.. dropdown:: TensorFlow integration (OVTF)
+
+   |   *New solution:* Direct model support and OpenVINO Converter (OVC)
+   |   *Old solution:* discontinued in OpenVINO 2023.0
+   |
+   |   OpenVINO now features a native TensorFlow support, with no need for explicit model
+       conversion.
+

docs/articles_en/learn-openvino/llm_inference_guide.rst

@@ -55,7 +55,10 @@ options:
       as well as conversion on the fly. For integration with the final product it may offer
       lower performance, though.
 
-
+Note that the base version of OpenVINO may also be used to run generative AI. Although it may
+offer a simpler environment, with fewer dependencies, it has significant limitations and a more
+demanding implementation process. For reference, see
+`the article on generative AI usage of OpenVINO 2024.6 <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-native-ov.html>`__.
 
 The advantages of using OpenVINO for generative model deployment:
 

docs/articles_en/learn-openvino/openvino-samples/benchmark-tool.rst

@@ -8,7 +8,10 @@ Benchmark Tool
                  devices.
 
 
-This page demonstrates how to use the Benchmark Tool to estimate deep learning inference performance on supported devices.
+This page demonstrates how to use the Benchmark Tool to estimate deep learning inference
+performance on supported devices. Note that the MULTI plugin mentioned here is considered
+a legacy tool and currently is just a mapping of the
+:doc:`AUTO plugin <../../openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection>`.
 
 .. note::