diff --git a/docs/docs/features/ml-hardware-acceleration.md b/docs/docs/features/ml-hardware-acceleration.md
index 7f92c25449..685f23932c 100644
--- a/docs/docs/features/ml-hardware-acceleration.md
+++ b/docs/docs/features/ml-hardware-acceleration.md
@@ -57,6 +57,22 @@ You do not need to redo any machine learning jobs after enabling hardware accele
 - Ensure the server's kernel version is new enough to use the device for hardware acceleration.
 - Expect higher RAM usage when using OpenVINO compared to CPU processing.
 
+#### OpenVINO-WSL
+
+- Ensure your container can access the /dev/dri directory, you can verify this by doing `docker exec -t immich_machine_learning ls -la /dev/dri`. If this is not the case execute `getent group render` and `getent group video` on the WSL host, then add those groups to hwaccel.ml.yaml
+  ```yaml
+  openvino-wsl:
+    devices:
+      - /dev/dri:/dev/dri
+      - /dev/dxg:/dev/dxg
+    volumes:
+      - /dev/bus/usb:/dev/bus/usb
+      - /usr/lib/wsl:/usr/lib/wsl
+    group_add:
+      - 44 # Replace this number with the number you found with getent group video
+      - 992 # Replace this number with the number you found with getent group render
+  ```
+
 #### RKNN
 
 - You must have a supported Rockchip SoC: only RK3566, RK3568, RK3576 and RK3588 are supported at this moment.