[DOCS] New Key Features in docs - master (openvinotoolkit#26308)

New Key Features article for the documentation.

---------

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>

Author: 3 people

docs/articles_en/about-openvino.rst

@@ -1,11 +1,11 @@
 About OpenVINO
 ==============
 
-
 .. toctree::
    :maxdepth: 1
    :hidden:
 
+   about-openvino/key-features
    about-openvino/performance-benchmarks
    about-openvino/compatibility-and-support
    about-openvino/contributing
@@ -15,29 +15,19 @@ OpenVINO is a toolkit for simple and efficient deployment of various deep learni
 In this section you will find information on the product itself, as well as the software
 and hardware solutions it supports.
 
-OpenVINO (Open Visual Inference and Neural network Optimization) is an open-source software toolkit designed to optimize, accelerate, and deploy deep learning models for user applications. OpenVINO was developed by Intel to work efficiently on a wide range of Intel hardware platforms, including CPUs (x86 and Arm), GPUs, and NPUs.
+OpenVINO (Open Visual Inference and Neural network Optimization) is an open-source software
+toolkit designed to optimize, accelerate, and deploy deep learning models for user applications.
+OpenVINO is actively developed by Intel® to work efficiently on a wide range of Intel® hardware platforms,
+including CPUs (x86 and Arm), GPUs, and NPUs.
 
 
 Features
 ##############################################################
 
-One of the main purposes of OpenVINO is to streamline the deployment of deep learning models in user applications. It optimizes and accelerates model inference, which is crucial for such domains as Generative AI, Large Language models, and use cases like object detection, classification, segmentation, and many others.
-
-* :doc:`Model Optimization <openvino-workflow/model-optimization>`
-
-OpenVINO provides multiple optimization methods for both the training and post-training stages, including weight compression for Large Language models and Intel Optimum integration with Hugging Face.
-
-* :doc:`Model Conversion and Framework Compatibility <openvino-workflow/model-preparation>`
-
-Supported models can be loaded directly or converted to the OpenVINO format to achieve better performance. Supported frameworks include ONNX, PyTorch, TensorFlow, TensorFlow Lite, Keras, and PaddlePaddle.
-
-* :doc:`Model Inference <openvino-workflow/running-inference>`
-
-OpenVINO accelerates deep learning models on various hardware platforms, ensuring real-time, efficient inference.
-
-* `Deployment on a server <https://github.com/openvinotoolkit/model_server>`__
+One of the main purposes of the OpenVINO toolkit is to streamline integration and deployment of
+deep learning models. Yet its feature set is much wider, offering various optimization and implementation options.
 
-A model can be deployed either locally using OpenVINO Runtime or on a model server. Runtime is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions. The model server enables quick model inference using external resources.
+To learn about the main properties of OpenVINO, see the :doc:`Key Features <about-openvino/key-features>`.
 
 Architecture
 ##############################################################

docs/articles_en/about-openvino/key-features.rst

@@ -0,0 +1,69 @@
+Key Features
+==============
+
+Easy Integration
+#########################
+
+| :doc:`Support for multiple frameworks <../openvino-workflow/model-preparation/convert-model-to-ir>`
+| Use deep learning models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle, and ONNX
+  directly or convert them to the optimized OpenVINO IR format for improved performance.
+
+| :doc:`Close integration with PyTorch <../openvino-workflow/torch-compile>`
+| For PyTorch-based applications, specify OpenVINO as a backend using
+  :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
+  OpenVINO optimizations to your PyTorch models directly with a single line of code.
+
+| :doc:`GenAI Out Of The Box <../learn-openvino/llm_inference_guide/genai-guide>`
+| With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
+  Check out the GenAI guide for instructions on how to do it.
+
+| `Python / C++ / C / NodeJS APIs <https://docs.openvino.ai/2024/api/api_reference.html>`__
+| OpenVINO offers the C++ API as a complete set of available methods. For less resource-critical
+  solutions, the Python API provides almost full coverage, while C and NodeJS ones are limited
+  to the methods most basic for their typical environments. The NodeJS API, is still in its
+  early and active development.
+
+| :doc:`Open source and easy to extend <../about-openvino/contributing>`
+| If you need a particular feature or inference accelerator to be supported, you are free to file
+  a feature request or develop new components specific to your projects yourself. As open source,
+  OpenVINO may be used and modified freely. See the extensibility guide for more information on
+  how to adapt it to your needs.
+
+Deployment
+#########################
+
+| :doc:`Local or remote <../openvino-workflow>`
+| Integrate the OpenVINO runtime directly with your application to run inference locally or use
+  `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ to shift the inference
+  workload to a remote system, a separate server or a Kubernetes environment. For serving,
+  OpenVINO is also integrated with `vLLM <https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html>`__
+  and `Triton <https://github.com/triton-inference-server/openvino_backend>`__ services.
+
+| :doc:`Scalable and portable <release-notes-openvino/system-requirements>`
+| Write an application once, deploy it anywhere, always making the most out of your hardware setup.
+  The automatic device selection mode gives you the ultimate deployment flexibility on all major
+  operating systems. Check out system requirements.
+
+| **Light-weight**
+| Designed with minimal external dependencies, OpenVINO does not bloat your application
+  and simplifies installation and dependency management. The custom compilation for your specific
+  model(s) may further reduce the final binary size.
+
+Performance
+#########################
+
+| :doc:`Model Optimization <../openvino-workflow/model-optimization>`
+| Optimize your deep learning models with NNCF, using various training-time and post-training
+  compression methods, such as pruning, sparsity, quantization, and weight compression. Make
+  your models take less space, run faster, and use less resources.
+
+| :doc:`Top performance <../about-openvino/performance-benchmarks>`
+| OpenVINO is optimized to work with Intel hardware, delivering confirmed high performance for
+  hundreds of models. Explore OpenVINO Performance Benchmarks to discover the optimal hardware
+  configurations and plan your AI deployment based on verified data.
+
+| :doc:`Enhanced App Start-Up Time <../openvino-workflow/running-inference/optimize-inference>`
+| If you need your application to launch immediately, OpenVINO will reduce first-inference latency,
+  running inference on CPU until a more suited device is ready to take over. Once a model
+  is compiled for inference, it is also cached, improving the start-up time even more.
+

docs/sphinx_setup/_static/css/homepage_style.css

@@ -163,13 +163,21 @@ h1 {
     }
 }
 
+p.sd-card-text > .sd-btn-outline-primary {
+    position: absolute;
+    bottom: 20px;
+}
+
 .sd-btn-outline-primary {
     color: #0054AE !important;
     text-decoration: none !important;
     background-color: #FFF !important;
     border-radius: 0;
-    position: absolute;
-    bottom: 20px;
+}
+
+p.sd-text-right:has(a.key-feat-btn) {
+    position:relative;
+    top:-50px;
 }
 
 .homepage-begin-tile {

docs/sphinx_setup/index.rst

@@ -148,6 +148,13 @@ Places to Begin
 Key Features
 ++++++++++++++++++++++++++++
 
+.. button-link:: about-openvino/key-features.html
+   :color: primary
+   :outline:
+   :align: right
+   :class: key-feat-btn
+
+   See all features
 
 .. grid:: 2 2 2 2
    :class-container: homepage_begin_container