diff --git a/notebooks/en/_toctree.yml b/notebooks/en/_toctree.yml
index 85b58533..b5a23049 100644
--- a/notebooks/en/_toctree.yml
+++ b/notebooks/en/_toctree.yml
@@ -79,6 +79,8 @@
title: Interactive Development In HF Spaces
- local: enterprise_hub_serverless_inference_api
title: Inference API (Serverless)
+ - local: enterprise_dedicated_endpoints
+ title: Inference Endpoints (Dedicated)
- local: enterprise_cookbook_argilla
title: Data annotation with Argilla Spaces
diff --git a/notebooks/en/enterprise_cookbook_overview.md b/notebooks/en/enterprise_cookbook_overview.md
index 56656714..ef23885f 100644
--- a/notebooks/en/enterprise_cookbook_overview.md
+++ b/notebooks/en/enterprise_cookbook_overview.md
@@ -17,7 +17,7 @@ With our serverless Inference API, you can test a range of open source models wi
For more details, read also the [serverless API](https://huggingface.co/docs/api-inference/index) documentation.
-## Inference Endpoints (dedicated) (coming soon)
+## Inference Endpoints (dedicated)
With our dedicated Inference Endpoints, you can easily deploy any model on a wide range of hardware, essentially creating your personal production-ready API in a few clicks. Read this recipe to learn how to create and configure your own dedicated Endpoint.
diff --git a/notebooks/en/enterprise_dedicated_endpoints.ipynb b/notebooks/en/enterprise_dedicated_endpoints.ipynb
new file mode 100644
index 00000000..302f8ac6
--- /dev/null
+++ b/notebooks/en/enterprise_dedicated_endpoints.ipynb
@@ -0,0 +1,924 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "66fc45b5-48e6-47b1-95d9-743143bed2ef",
+ "metadata": {},
+ "source": [
+ "# Inference Endpoints (dedicated) \n",
+ "\n",
+ "Did you ever want to create your own machine learning API? That's what we will do in this recipe with the [HF Dedicated Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index). Inference Endpoints enable you to pick any of the hundreds of thousands of models on the HF Hub and create your own API in a few clicks in a deployment you control, on hardware you choose. \n",
+ "\n",
+ "[Serverless Inference APIs](link-to-recipe) are great for initial testing, but they are limited to a pre-configured selection of popular models and they are rate limited, because the serverless API's hardware is used by many users at the same time. With a Dedicated Inference Endpoint, you can customize the deployment of your model and the hardware is exclusively dedicated to you. \n",
+ "\n",
+ "In this recipe, we will: \n",
+ "- Create an Inference Endpoint via a simple UI and send standard HTTP requests to the Endpoint\n",
+ "- Create and manage different Inference Endpoints programmatically with the `huggingface_hub` library\n",
+ "- Cover three use-cases: text generation with an LLM, image generation with Stable Diffusion, and reasoning over images with Idefics2. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "68cbd86f-c3fb-46f5-8470-37a23dfeac6a",
+ "metadata": {},
+ "source": [
+ "## Install and login\n",
+ "In case you don't have a HF Account, you can create your account [here](https://huggingface.co/join). If you work in a larger team, you can also create a [HF Organization](https://huggingface.co/organizations) and manage all your models, datasets and endpoints via this organization. Dedicated Inference Endpoints are a paid service and you will therefore need to add a credit card the [billing settings](https://huggingface.co/settings/billing) of your personal HF account, or of your HF organization. \n",
+ "\n",
+ "You can then create a user access token [here](https://huggingface.co/docs/hub/security-tokens). A token with `read` or `write` permissions will work for this guide, but we encourage the use of fine-grained tokens for increased security. For this notebook, you'll need a fine-grained token with `User Permissions > Inference > Make calls to Inference Endpoints & Manage Inference Endpoints` and `Repository permissions > google/gemma-1.1-2b-it & HuggingFaceM4/idefics2-8b-chatty`.\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cf4e7353-d503-4b12-819a-4ea43e1305b3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!pip install huggingface_hub~=0.23.3\n",
+ "!pip install transformers~=4.41.2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "908c9c20-ca5e-4ff9-b5b3-9a0c477a21b5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Login to the HF Hub. We recommend using this login method \n",
+ "# to avoid the need for explicitly storing your HF token in variables \n",
+ "import huggingface_hub\n",
+ "huggingface_hub.interpreter_login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b0d609d-60eb-42d9-9524-7be8917509e6",
+ "metadata": {},
+ "source": [
+ "## Creating your first endpoint\n",
+ "\n",
+ "With this initial setup out of the way, we can now create our first Endpoint. Navigate to https://ui.endpoints.huggingface.co/ and click on `+ New` next to `Dedicated Endpoints`. You will then see the interface for creating a new Endpoint with the following options (see image below):\n",
+ "\n",
+ "- **Model Repository**: Here you can insert the identifier of any model on the HF Hub. For this initial demonstration, we use [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it), a small generative LLM (2.5B parameters). \n",
+ "- **Endpoint Name**: The Endpoint Name is automatically generated based on the model identifier, but you are free to change the name. Valid Endpoint names must only contain lower-case characters, numbers or hyphens (\"-\") and are between 4 to 32 characters long.\n",
+ "- **Instance Configuration**: Here you can choose from a wide range of CPUs or GPUs from all major cloud platforms. You can also adjust the region, for example if you need to host your endpoint in the EU. \n",
+ "- **Automatic Scale-to-Zero**: You can configure your Endpoint to scale to zero GPUs/CPUs after a certain amount of time. Scaled-to-zero Endpoints are not billed anymore. Note that restarting the endpoint requires the model to be re-loaded into memory (and potentially re-downloaded), which can take saveral minutes for large models. \n",
+ "- **Endpoint Security Level**: The standard security level is `Protected`, which requires an authorized HF token for accessing the endpoint. `Public` Endpoints are accessible by anyone without token authentification. `Private` Endpoints are only available through an intra-region secured AWS or Azure PrivateLink connection.\n",
+ "- **Advanced configuration**: Here you can select some advanced options like the Docker container type. As Gemma is compatible with [Text Generation Inference (TGI)](https://huggingface.co/docs/text-generation-inference/index) containers, the system automatically selects TGI as the container type and other good default values.\n",
+ "\n",
+ "For this guide, select the options in the image below and click on `Create Endpoint`. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b56622c2-334d-4523-b342-c1a9ccae66ad",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
![](\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-endpoints-creation.png\")
\n",
+ "
\n",
+ "
![](\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-endpoint-overviewpage.png\")
\n",
+ "