diff --git a/docs/examples/llm/client.py b/docs/examples/llm/client.py
index 63cabf5f..86e3ce7e 100644
--- a/docs/examples/llm/client.py
+++ b/docs/examples/llm/client.py
@@ -1,5 +1,9 @@
 import openai
 
+
+#
+# Parse the server info from the job comment
+#
 def parse_meta(comment):
     data = dict()
     if comment != "(null)":
@@ -22,10 +26,12 @@ def get_job_comment(name="inference_server.sh"):
 
 server = parse_meta(get_job_comment())
 
+# Override OpenAPI API URL with out custom server
 openai.api_key = "EMPTY"
 openai.api_base = f"http://{server['host']}:{server['port']}/v1"
 
 
+# profit
 completion = openai.Completion.create(
     model=server['model'], 
     prompt=args.prompt
diff --git a/docs/examples/llm/inference_server.sh b/docs/examples/llm/inference_server.sh
index 985a4ee5..acf2a857 100644
--- a/docs/examples/llm/inference_server.sh
+++ b/docs/examples/llm/inference_server.sh
@@ -16,8 +16,45 @@
 #SBATCH --ntasks-per-node=1
 #SBATCH --mem=32G
 
-MODEL="$1"
-PATH="$2"
+usage() {
+  echo "Usage: $0 [-m] [-p] 
+  echo "  -h              Display this help message."
+  echo "  -m MODEL        Specify a file to process."
+  echo "  -p PATH         Specify a directory to work in."
+  echo "  ARGUMENT        Any additional argument you want to process."
+  exit 1
+}
+
+MODEL=""
+PATH=""
+ENV="./env"
+
+
+while getopts ":hf:d:" opt; do
+  case $opt in
+    h)
+      usage
+      ;;
+    m)
+      MODEL="$OPTARG"
+      ;;
+    p)
+      PATH="$OPTARG"
+      ;;
+    e)
+      ENV="$OPTARG"
+      ;;
+    \?)
+      echo "Invalid option: -$OPTARG" >&2
+      usage
+      ;;
+    :)
+      echo "Option -$OPTARG requires an argument." >&2
+      usage
+      ;;
+  esac
+done
+
 
 export MILA_WEIGHTS="/network/weights/"
 
@@ -33,19 +70,19 @@ source $CONDA_BASE/../etc/profile.d/conda.sh
 #
 #   Create a new environment
 #
-conda create --prefix ./env python=3.9 -y 
-conda activate ./env
+if [ ! -d "$ENV" ]; then
+     conda create --prefix $ENV python=3.9 -y 
+fi
+conda activate $ENV
 pip install vllm
 
-#
-#   Save metadata for retrival
-#
-
 PORT=$(python -c "import socket; sock = socket.socket(); sock.bind(('', 0)); print(sock.getsockname()[1])")
 HOST="$(hostname)"
 NAME="$WEIGHTS/$MODEL"
 
-echo " -> $HOST:$PORT"
+#
+#   Save metadata for retrival
+#
 scontrol update job $SLURM_JOB_ID comment="model=$MODEL|host=$HOST|port=$PORT|shared=y"
 
 # 
diff --git a/docs/examples/llm/vllm.rst b/docs/examples/llm/vllm.rst
index 0715ce2c..e55c1291 100644
--- a/docs/examples/llm/vllm.rst
+++ b/docs/examples/llm/vllm.rst
@@ -2,12 +2,58 @@ LLM Inference
 =============
 
 
+Server
+------
+
+`vLLM <https://github.com/vllm-project/vllm>`_ comes with its own server entry point that mimicks OpenAI's API.
+It is very easy to setup and supports a wide range of models through Huggingfaces.
+
+
+.. code-block:: 
+
+   # sbatch inference_server.sh -m MODEL_NAME -p WEIGHT_PATH -e CONDA_ENV_NAME_TO_USE
+   sbatch inference_server.sh -m Llama-2-7b-chat-hf -p /network/weights/llama.var/llama2/Llama-2-7b-chat-hf -e base
+
+
+By default the script will launch the server on an rtx8000 for 15 minutes.
+You can override the defaults by specifying arguments to sbatch.
 
-Dependencies
-------------
 
 .. code-block:: 
 
-   sbatch inference_server.sh Llama-2-7b-chat-hf /network/weights/llama.var/llama2/Llama-2-7b-chat-hf
+   sbatch --time=00:30:00 inference_server.sh -m Llama-2-7b-chat-hf -p /network/weights/llama.var/llama2/Llama-2-7b-chat-hf -e base
+
+.. note::
+
+    We are using job comment to store hostname, port and model names,
+    which enable the client to automatically pick them up on its side.
+
+
+.. literalinclude:: inference_server.sh
+    :language: bash
+
+
+Client
+------
+
+Becasue vLLM replicates OpenAI's API, the client side is quite straight forward.
+Own OpenAI's client can be reused. 
+
+.. warning::
+   
+   The server takes a while to setup you might to have to wait a few minutes
+   before the server is ready for inference.
+
+   You can check the job log of the server.
+   Look for 
+
+
+.. note::
+
+   We use squeue to look for the inference server job to configure the 
+   url endpoint automatically.
 
+   Make sure your job name is unique!
 
+.. literalinclude:: client.py
+    :language: python