diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst
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+Run LLMs with OpenVINO GenAI Flavor on NPU
+==========================================
+
+.. meta::
+   :description: Learn how to use the OpenVINO GenAI flavor to execute LLM models on NPU.
+
+This guide will give you extra details on how to utilize NPU with the GenAI flavor.
+:doc:`See the installation guide <../../get-started/install-openvino/install-openvino-genai>`
+for information on how to start.
+
+Export an LLM model via Hugging Face Optimum-Intel
+##################################################
+
+1. Create a python virtual environment and install the correct components for exporting a model:
+
+   .. code-block:: console
+
+      python -m venv export-npu-env
+      export-npu-env\Scripts\activate
+      pip install transformers>=4.42.4 openvino==2024.2.0 openvino-tokenizers==2024.2.0 nncf==2.11.0 onnx==1.16.1 optimum-intel@git+https://github.com/huggingface/optimum-intel.git
+
+2. A chat-tuned TinyLlama model is used in this example. The following conversion & optimization settings are recommended when using the NPU:
+
+   .. code-block:: python
+
+      optimum-cli export openvino -m TinyLlama/TinyLlama-1.1B-Chat-v1.0 --weight-format int4 --sym --group-size 128 --ratio 1.0 TinyLlama
+
+Run generation using OpenVINO GenAI
+##########################################
+
+1. Create a python virtual environment and install the correct components for running the model on the NPU via OpenVINO GenAI:
+
+   .. code-block:: console
+
+      python -m venv run-npu-env
+      run-npu-env\Scripts\activate
+      pip install openvino>=2024.3.1 openvino-tokenizers>=2024.3.1 openvino-genai>=2024.3.1
+
+2. Perform generation using the new GenAI API
+
+   .. tab-set::
+
+      .. tab-item:: Python
+         :sync: py
+
+         .. code-block:: python
+
+            import openvino_genai as ov_genai
+            pipe = ov_genai.LLMPipeline(model_path, "NPU")
+            print(pipe.generate("The Sun is yellow because", max_new_tokens=100))
+
+      .. tab-item:: C++
+         :sync: cpp
+
+         .. code-block:: cpp
+
+            #include "openvino/genai/llm_pipeline.hpp"
+            #include <iostream>
+
+            int main(int argc, char* argv[]) {
+               std::string model_path = argv[1];
+               ov::genai::LLMPipeline pipe(model_path, "NPU");
+               std::cout << pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(100));
+            }
+
+Additional configuration options
+################################
+
+Compiling models for NPU may take a while. By default, the LLMPipeline for the NPU
+is configured for faster compilation, but it may result in lower performance.
+To achieve better performance at the expense of compilation time, you may try these settings:
+
+.. tab-set::
+
+   .. tab-item:: Python
+      :sync: py
+
+      .. code-block:: python
+
+         plugin_config = { "NPU_COMPILATION_MODE_PARAMS": "compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add_RMSNorm" }
+         pipeline_config = { "PREFILL_CONFIG": plugin_config, "GENERATE_CONFIG": plugin_config }
+         pipe = ov_genai.LLMPipeline(model_path, "NPU", pipeline_config)
+
+   .. tab-item:: C++
+      :sync: cpp
+
+      .. code-block:: cpp
+
+         ov::AnyMap plugin_config = { { "NPU_COMPILATION_MODE_PARAMS", "compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add_RMSNorm" } };
+         ov::AnyMap pipeline_config = { { "PREFILL_CONFIG",  plugin_config }, { "GENERATE_CONFIG", plugin_config } };
+         ov::genai::LLMPipeline pipe(model_path, "NPU", pipeline_config);
+
+
+Additional Resources
+####################
+
+* :doc:`NPU Device <../../openvino-workflow/running-inference/inference-devices-and-modes/npu-device>`
+* `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__
+* `Neural Network Compression Framework <https://github.com/openvinotoolkit/nncf>`__
+
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
index bf434fa6bf9cb3..06ac3642df7334 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
@@ -4,6 +4,13 @@ Run LLM Inference on OpenVINO with the GenAI Flavor
 .. meta::
    :description: Learn how to use the OpenVINO GenAI flavor to execute LLM models.
 
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   genai-guide-npu
+
+
 This guide will show you how to integrate the OpenVINO GenAI flavor into your application, covering
 loading a model and passing the input context to receive generated text. Note that the vanilla flavor of OpenVINO
 will not work with these instructions, make sure to