diff --git a/docs/how-to/examples.md b/docs/how-to/examples.md
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+++ b/docs/how-to/examples.md
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+# CodeCarbon Examples
+
+The directory [examples/](https://github.com/mlco2/codecarbon/tree/master/examples) contains practical examples demonstrating how to use CodeCarbon to track carbon emissions from your computing tasks. The examples below are organized by use case rather than alphabetically.
+
+## Quick Start Examples
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [print_hardware.py](../../examples/print_hardware.py) | Python Script | Detect and display available hardware (CPU, GPU, RAM) on your system |
+| [command_line_tool.py](../../examples/command_line_tool.py) | Python Script | Track emissions of external command-line tools executed via subprocess |
+
+## Tracking Methods
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [mnist_decorator.py](../../examples/mnist_decorator.py) | Python Script | Track emissions using the `@track_emissions` decorator on functions |
+| [mnist_context_manager.py](../../examples/mnist_context_manager.py) | Python Script | Track emissions using `EmissionsTracker` as a context manager (with statement) |
+| [mnist_callback.py](../../examples/mnist_callback.py) | Python Script | Track emissions using Keras/TensorFlow callbacks during model training |
+| [api_call_demo.py](../../examples/api_call_demo.py) | Python Script | Track emissions and send data to the CodeCarbon API with `@track_emissions` |
+
+## Basic Model Training
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [mnist.py](../../examples/mnist.py) | Python Script | Train a simple neural network on MNIST dataset with TensorFlow |
+| [mnist-sklearn.py](../../examples/mnist-sklearn.py) | Python Script | Train a scikit-learn model on MNIST and track emissions |
+| [pytorch-multigpu-example.py](../../examples/pytorch-multigpu-example.py) | Python Script | PyTorch CNN training on MNIST with multi-GPU support |
+
+## Hyperparameter Search
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [mnist_grid_search.py](../../examples/mnist_grid_search.py) | Python Script | Grid search hyperparameter optimization with emission tracking |
+| [mnist_random_search.py](../../examples/mnist_random_search.py) | Python Script | Random search hyperparameter optimization with emission tracking |
+
+## ML Model Inference
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [bert_inference.py](../../examples/bert_inference.py) | Python Script | BERT language model inference with task-level tracking |
+| [task_inference.py](../../examples/task_inference.py) | Python Script | Track emissions for different inference tasks (load dataset, build model, predict) |
+| [task_loop_same_task.py](../../examples/task_loop_same_task.py) | Python Script | Track emissions running the same task multiple times |
+| [transformers_smollm2.py](../../examples/transformers_smollm2.py) | Python Script | Small language model (SmolLM2) inference from Hugging Face |
+| [ollama_local_api.py](../../examples/ollama_local_api.py) | Python Script | Track emissions of local LLM API calls using Ollama |
+
+## Hardware-Specific Examples
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [intel_npu.py](../../examples/intel_npu.py) | Python Script | Intel Neural Processing Unit (NPU) support for model inference |
+| [full_cpu.py](../../examples/full_cpu.py) | Python Script | Demonstrate full CPU utilization and emission tracking |
+
+## Parallel & Concurrent Processing
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [multithread.py](../../examples/multithread.py) | Python Script | Track emissions from multithreaded workloads |
+| [compare_cpu_load_and_RAPL.py](../../examples/compare_cpu_load_and_RAPL.py) | Python Script | Compare RAPL power measurement vs CPU load estimation in parallel workloads |
+
+## Logging & Output Integration
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [logging_to_file.py](../../examples/logging_to_file.py) | Python Script | Save emissions data to a local CSV file |
+| [logging_to_file_exclusive_run.py](../../examples/logging_to_file_exclusive_run.py) | Python Script | Long-running process with exclusive file logging |
+| [logging_to_google_cloud.py](../../examples/logging_to_google_cloud.py) | Python Script | Send emissions data to Google Cloud Logging |
+| [logfire_metrics.py](../../examples/logfire_metrics.py) | Python Script | Integrate CodeCarbon with Logfire metrics platform |
+| [prometheus_call.py](../../examples/prometheus_call.py) | Python Script | Export emissions metrics to Prometheus |
+| [mnist-comet.py](../../examples/mnist-comet.py) | Python Script | Integrate emission tracking with Comet.ml experiment tracking |
+
+## Metrics & Analysis
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [pue.py](../../examples/pue.py) | Python Script | Calculate Power Usage Effectiveness (PUE) with CodeCarbon |
+| [wue.py](../../examples/wue.py) | Python Script | Calculate Water Usage Effectiveness (WUE) of your computing |
+
+## Interactive Notebooks
+
+| Example | Type | Description |
+|---------|------|-------------|
+| [notebook.ipynb](../../examples/notebook.ipynb) | Jupyter Notebook | Basic CodeCarbon usage in Jupyter environment |
+| [compare_cpu_load_and_RAPL.ipynb](../../examples/compare_cpu_load_and_RAPL.ipynb) | Jupyter Notebook | Compare different power measurement methods (RAPL vs CPU load) |
+| [local_llms.ipynb](../../examples/local_llms.ipynb) | Jupyter Notebook | Track emissions of local LLM inference |
+
+## Setup & Configuration
+
+| Item | Description |
+|------|-------------|
+| [requirements-examples.txt](../../examples/requirements-examples.txt) | Python dependencies for running the examples |
+| [rapl/](../../examples/rapl/) | Setup instructions for RAPL power measurement support |
+| [slurm_rocm/](../../examples/slurm_rocm/) | Configuration for SLURM job scheduler with ROCm GPU support |
+| [notebooks/](../../examples/notebooks/) | Additional Jupyter notebooks |
+
+## Running the Examples
+
+### Prerequisites
+```bash
+# Install CodeCarbon
+pip install codecarbon
+
+# Install example dependencies
+# WARNING: it will download huge pacakge. We recommand you to install only the minimum you need for the example you want to run.
+pip install -r examples/requirements-examples.txt
+```
+
+### Run a Python Example
+```bash
+# Using uv (recommended)
+uv run examples/print_hardware.py
+
+# Or with Python directly
+python examples/print_hardware.py
+```
+
+### Run a Jupyter Notebook
+```bash
+jupyter notebook examples/notebook.ipynb
+```
+
+Or just open it in VS Code.
+
+## Common Patterns
+
+### Track with Decorator
+```python
+from codecarbon import track_emissions
+
+@track_emissions(project_name="my_project")
+def my_function():
+    # Your code here
+    pass
+```
+
+### Track with Context Manager
+```python
+from codecarbon import EmissionsTracker
+
+with EmissionsTracker() as tracker:
+    # Your code here
+    pass
+```
+
+### Track Specific Tasks
+```python
+from codecarbon import EmissionsTracker
+
+tracker = EmissionsTracker()
+tracker.start()
+tracker.start_task("data_loading")
+# Load data...
+tracker.stop_task()
+
+tracker.start_task("training")
+# Train model...
+tracker.stop_task()
+tracker.stop()
+```
diff --git a/examples/intel_npu.py b/examples/intel_npu.py
new file mode 100644
index 000000000..ff52b007b
--- /dev/null
+++ b/examples/intel_npu.py
@@ -0,0 +1,91 @@
+"""
+
+## Prerequisites
+
+Install OpenVINO and PyTorch CPU versions :
+uv pip install -U openvino
+uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+### Linux support for Intel® Neural Processing Unit (Intel® NPU)
+
+Chaeck if drivers are installed and the NPU is available:
+`sudo dmesg | grep vpu` should show something like this if the NPU is properly initialized:
+[nnnnn] [drm] Initialized intel_vpu 1.0.0 for 0000:00:0b.0 on minor 0
+
+Else, have a look to https://github.com/intel/linux-npu-driver/releases
+
+### Windows support
+
+You need to install the [Intel drivers](https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html).
+
+## Running the example
+
+`uv run examples/intel_npu.py`
+
+"""
+
+import os
+
+import openvino as ov
+import torch
+from torchvision.models import ShuffleNet_V2_X1_0_Weights, shufflenet_v2_x1_0
+
+from codecarbon import track_emissions
+
+NUMBER_OF_IMAGES = 30
+
+
+@track_emissions(
+    measure_power_secs=5,
+    save_to_file=False,
+    log_level="debug",
+)
+def openvino_npu_task():
+    print("openvino", ov.__version__)
+    model = shufflenet_v2_x1_0(weights=ShuffleNet_V2_X1_0_Weights.DEFAULT)
+    print("training before eval", model.training)
+    model.eval()
+    print("training after eval", model.training)
+    # Create a dummy input tensor with the appropriate shape for the model (e.g., [10, 3, 224, 224] for image classification).
+    example = torch.randn(NUMBER_OF_IMAGES, 3, 224, 224)
+    ov_model = ov.convert_model(
+        model, example_input=(example,), input=[NUMBER_OF_IMAGES, 3, 224, 224]
+    )
+    print("input partial shape", ov_model.input(0).partial_shape)
+    for i, out in enumerate(ov_model.outputs):
+        print("output", i, out.partial_shape)
+    core = ov.Core()
+    print("devices", core.available_devices)
+    if "NPU" in core.available_devices:
+        compiled = core.compile_model(ov_model, "NPU")
+        print("compiled on", compiled.get_property("EXECUTION_DEVICES"))
+        result = compiled({0: example.numpy()})
+        print("result shape", result[compiled.output(0)].shape)
+        # output_data = result[compiled.output(0)]
+        # Classification outputs are flat vectors (e.g. [10, 1000]).
+        # Render the top-3 scores as simple ASCII bars for terminal readability.
+        output_data = next(iter(result.values()))
+        # For classification output shaped like [batch, classes], show top-k per image.
+        # If output is 1D, treat it as a single-image prediction.
+        if output_data.ndim == 1:
+            output_data = output_data.reshape(1, -1)
+
+        for image_idx, image_scores in enumerate(output_data):
+            top_k = min(3, image_scores.size)
+            top_indices = image_scores.argsort()[-top_k:][::-1]
+            top_values = image_scores[top_indices]
+            max_score = float(top_values.max()) if top_values.size else 1.0
+            scale = max_score if max_score > 0 else 1.0
+
+            print(f"Top predictions for image {image_idx} (index: score | bar):")
+            for idx, score in zip(top_indices, top_values):
+                bar_len = int(40 * float(score) / scale)
+                bar = "#" * max(0, bar_len)
+                print(f"  {int(idx):4d}: {float(score): .6f} | {bar}")
+    else:
+        print("NPU unavailable")
+
+
+# Display my process PID so you can check its power usage with `sudo dmesg | grep vpu` in another terminal.
+print(f"Process PID: {os.getpid()}")
+openvino_npu_task()
diff --git a/mkdocs.yml b/mkdocs.yml
index 90fe0984c..553418fff 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -148,6 +148,7 @@ nav:
       - HuggingFace Diffusers: how-to/diffusers.md
       - LLMs and Agents: how-to/agents.md
   - How-to Guides:
+    - Examples: how-to/examples.md
     - Configure CodeCarbon: how-to/configuration.md
     - Compare Model Efficiency: tutorials/comparing-model-efficiency.md
     - Dashboard & Visualization: