diff --git a/docs/quickstart/inference_gpu_windows.md b/docs/quickstart/inference_gpu_windows.md
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+# Install the `inference-gpu` Python Package and NVIDIA CUDA on Windows
+
+!!! warning
+    We strongly recommend [installing Inference with Docker on Windows](https://inference.roboflow.com/quickstart/docker/). The guide below should only be used if you are unable to use Docker on your system.
+
+You can use Inference with `inference-gpu` and NVIDIA CUDA on Windows devices.
+
+This guide walks through how to configure your Windows GPU setup.
+
+## Prerequisites
+
+To follow this guide, you must have a Windows machine that runs either Windows 10 or Windows 11. Your machine must have an NVIDIA GPU.
+
+## Step #1: Install Python
+
+Download and the latest Python 3.11.x from the [Python Windows version list](https://www.python.org/downloads/windows/).
+
+Do not install the Python version from the windows store as it is not compatible with onnxruntime.
+
+Click the "Windows Installer (64 Bit)" link and follow instructions to install python on the machine.
+
+When the installation is finished, type `py --version` to ensure your Python installation was successful.
+
+You should see a message showing your Python version.
+
+## Step #2: Install Inference GPU
+
+In a powershell terminal, run:
+
+```
+py -m pip install inference-gpu
+```
+
+## Step #3: Install CUDA Toolkit 11.8
+
+Next, we need to install CUDA Toolkit 11.8. This software will allow Inference to use CUDA.
+
+[Download CUDA Toolkit](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows&target_arch=x86_64).
+
+From the download page, choose the correct parameters for your system, and then choose "exe (Network)" and follow the link to download the toolkit.
+
+![Toolkit download screenshot](https://media.roboflow.com/cuda_toolkit_windows/toolkit.png)
+
+Open the installation file and accept all defaults in order to install the toolkit.
+
+## Step #3: Install cuDNN
+
+Next, we need to install cuDNN.
+
+Navigate to the [cuDNN installation page on the NVIDIA website](https://developer.nvidia.com/rdp/cudnn-archive).
+
+Select "cuDNN v8.7.0 (November 28th, 2022), for CUDA 11.x"
+
+Choose the link for 11.x, as others will not be compatible with your CUDA version and will fail.
+
+Choose "Local Installer for Windows (Zip)" and continue to download the ZIP file. You will need to sign up for an NVIDIA account in order to download the software.
+
+Open the file in downloads folder, right click and choose "Extract All" to extract all files to the download folder.
+
+Type ctrl+n to open a new Explorer window, and in this window navigate to `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`.
+
+Copy all .dll files from the bin/ folder of the cuDNN download into the bin/ folder of the CUDA toolkit:
+
+![DLL copy process](https://media.roboflow.com/cuda_toolkit_windows/dll.png)
+
+Copy all .h files from the include/ folder of the cuDNN download into the include/ folder of the CUDA toolkit:
+
+![h file copy process](https://media.roboflow.com/cuda_toolkit_windows/h.png)
+
+Copy the x64 folder from the lib/ directory of the cuDNN download into the lib/ directory of the CUDA installation:
+
+![x64 file copy process](https://media.roboflow.com/cuda_toolkit_windows/x64.png)
+
+Right click the start menu and choose System → Advanced system settings → Environment Variables.
+
+Create a new environment variable called CUDNN with the value:
+
+```
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib;
+```
+
+## Step #4: Install zlib.
+
+Find the file C:\Program Files\NVIDIA Corporation\Nsight Systems 2022.4.2\host-windows-x64\zlib.dll Right click and choose "Copy".
+
+Now navigate to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin in the finder.
+
+Paste the zlib.dll file into this folder and rename to zlibwapi.dll.
+
+## Step #5: Install Visual Studio 2019 C++ Runtime
+
+Finally, install the Visual Studio 2019 C++ runtime ([download link](https://aka.ms/vs/17/release/vc_redist.x64.exe)).
+
+Create a new file with the following contents:
+
+```python
+from inference import InferencePipeline
+from inference.core.interfaces.stream.sinks import render_boxes
+
+pipeline = InferencePipeline.init(
+    api_key="REPLACE API KEY", ## <- update API KEY
+    model_id="rock-paper-scissors-sxsw/11",
+    video_reference='https://media.roboflow.com/rock-paper-scissors.mp4',
+    on_prediction=render_boxes,
+)
+pipeline.start()
+pipeline.join()
+```
+
+Using a text editor (we recommend Visual Studio Code), add your Roboflow API key in the string on line 5.
+
+Open a PowerShell terminal in the location of the file, and type py infer.py. If the installation is successful, you should see a few frames of annotated images displayed, with no errors or warnings in the console.
diff --git a/mkdocs.yml b/mkdocs.yml
index c7b9c4c7b..0fb11a98b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -100,6 +100,7 @@ nav:
           - Inference API Reference: docs/reference/
           - Running With Docker: quickstart/docker.md
           - Docker Configuration Options: quickstart/docker_configuration_options.md
+          - Install “bare metal” Inference GPU on Windows: quickstart/inference_gpu_windows.md
           - Contribute to Inference: contributing.md
           - Changelog: https://github.com/roboflow/inference/releases
   - Cookbooks: cookbooks.md