diff --git a/notebooks/README.md b/notebooks/README.md
index ad6c04cc463..3f1cdbaf2a1 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -61,7 +61,39 @@ Running the example in these notebooks requires:
 * CUDA 11.4+
 * NVIDIA driver 450.51+
 
+### QuickStart
 
+The easiest way to run the notebooks is to get the latest [rapidsai/notebooks](https://hub.docker.com/r/rapidsai/notebooks) docker image with matching cuda version and run a container based on the image.
+
+For example, get the latest (as of writing the document) nightly image (`a` after the version number indicates that an image is nightly) with cuda 12.0 using
+```sh
+docker pull rapidsai/notebooks:24.04a-cuda12.0-py3.9
+```
+
+And, then run a container based on the image using
+
+```sh
+docker run --rm  -it --pull always --gpus all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864   -p 8888:8888 rapidsai/notebooks:24.04a-cuda12.0-py3.9
+```
+You are all set. Run and edit cugraph notebooks from a browser at url
+http://127.0.0.1:8888/lab/tree/cugraph/cugraph_benchmarks
+
+
+If you want to run the container in a remote machine which has access to GPUs, you can use `ssh` tunneling to run/edit the notebooks locally as explained above.
+
+Login to your remote machine with ssh tunneling/port forwarding
+
+```
+ssh -L  127.0.0.1:8888:127.0.0.1:8888 [USER_NAME@][REMOTE_HOST_NAME or REMOTE_HOST_IP]
+```
+and then run the container in your remote machine.
+
+```sh
+docker pull rapidsai/notebooks:24.04a-cuda12.0-py3.9
+docker run --rm  -it --pull always --gpus all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p 8888:8888 rapidsai/notebooks:24.04a-cuda12.0-py3.9
+```
+
+You can run and edit cugraph notebooks at url http://127.0.0.1:8888/lab/tree/cugraph/cugraph_benchmarks as if they are running locally.
 
 ## Additional Notebooks