diff --git a/docs/docs/configuration/hardware_acceleration.md b/docs/docs/configuration/hardware_acceleration.md
index c9ff695c5..d98b8d22a 100644
--- a/docs/docs/configuration/hardware_acceleration.md
+++ b/docs/docs/configuration/hardware_acceleration.md
@@ -45,8 +45,10 @@ ffmpeg:
 
 These instructions are based on the [jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux)
 
-Add `--gpus all` to your docker run command or update your compose file.
-If you have multiple Nvidia graphic card, you can add them with their ids obtained via `nvidia-smi` command
+Additional configuration is needed for the docker container to be able to access the Nvidia GPU and this depends on how docker is being run:
+
+#### Docker Compose
+
 ```yaml
 services:
   frigate:
@@ -62,6 +64,18 @@ services:
               capabilities: [gpu]
 ```
 
+#### Docker Run CLI
+
+```bash
+docker run -d \
+  --name frigate \
+  ...
+  --gpus=all \
+  ghcr.io/blakeblackshear/frigate:stable
+```
+
+#### Setup Decoder
+
 The decoder you need to pass in the `hwaccel_args` will depend on the input video.
 
 A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)