diff --git a/examples/llm_inference_speedtest.ipynb b/examples/llm_inference_speedtest.ipynb
new file mode 100644
index 0000000000..f9b32b8d89
--- /dev/null
+++ b/examples/llm_inference_speedtest.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing time: 3.0881738662719727\n",
+      "                                         prompt  \\\n",
+      "0               What is the capital of France?    \n",
+      "1                 What is the meaning of life?    \n",
+      "2   What is the smoothest part of the universe?   \n",
+      "\n",
+      "                                            response  \n",
+      "0                                            Paris\\n  \n",
+      "1  There's no single, universally accepted answer...  \n",
+      "2  The smoothest part of the universe, on the lar...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Preprocess and query LLMs:\n",
+    "- Install llm package using pip install llm\n",
+    "- Set keys for the model you'd like to use, using llm keys set [MODEL]\n",
+    "\"\"\"\n",
+    "\n",
+    "import pandas as pd\n",
+    "import time\n",
+    "from dotenv import load_dotenv\n",
+    "import llm\n",
+    "\n",
+    "load_dotenv()\n",
+    "MODEL = \"gemini-1.5-flash-8b-latest\"\n",
+    "model = llm.get_model(MODEL) \n",
+    "\n",
+    "def query_model(prompt):\n",
+    "    \"\"\"\n",
+    "    Sends a prompt to the AI Suite and returns the response.\n",
+    "    \"\"\"\n",
+    "    response = model.prompt(prompt) \n",
+    "    return response.text()\n",
+    "\n",
+    "def query_model_all(df):\n",
+    "    \"\"\"Clean up prompts and query the model for all prompts in the dataframe.\"\"\"\n",
+    "    t0 = time.time()\n",
+    "    cleaned_prompts = df[\"prompt\"].str.strip().str.lower()\n",
+    "    df[\"response\"] = cleaned_prompts.map(query_model)\n",
+    "    print(\"Processing time:\", time.time() - t0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    raw_prompts = [\n",
+    "        \" What is the capital of France? \",\n",
+    "        \" What is the meaning of life? \",\n",
+    "        \" What is the smoothest part of the universe?\",\n",
+    "    ]\n",
+    "\n",
+    "    # Repeat prompts 10 times for testing purposes\n",
+    "    df = pd.DataFrame({\"prompt\": raw_prompts})\n",
+    "    out_df = query_model_all(df)\n",
+    "    print(out_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing time: 0.4763470000000325\n",
+      "                                              prompt  response\n",
+      "0                    What is the capital of France?     Paris\n",
+      "\n",
+      "1          What is the meaning of life in one word?   Purpose\n",
+      "\n",
+      "2   What is the smoothest part of the universe, i...   Cosmic\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Preprocess and query LLMs, Bodo edition:\n",
+    "- Install llm package using pip install llm\n",
+    "- Set keys for the model you'd like to use, using llm keys set [MODEL]\n",
+    "\"\"\"\n",
+    "\n",
+    "import pandas as pd\n",
+    "import bodo\n",
+    "import time\n",
+    "from dotenv import load_dotenv\n",
+    "import llm\n",
+    "\n",
+    "load_dotenv()\n",
+    "MODEL = \"gemini-1.5-flash-8b-latest\"\n",
+    "model = llm.get_model(MODEL) \n",
+    "\n",
+    "@bodo.wrap_python(bodo.string_type)\n",
+    "def query_model(prompt):\n",
+    "    \"\"\"\n",
+    "    Sends a prompt to the AI Suite and returns the response.\n",
+    "    \"\"\"\n",
+    "    response = model.prompt(prompt) \n",
+    "    return response.text()\n",
+    "\n",
+    "@bodo.jit\n",
+    "def query_model_all(df):\n",
+    "    \"\"\"Clean up prompts and query the model for all prompts in the dataframe.\"\"\"\n",
+    "    t0 = time.time()\n",
+    "    cleaned_prompts = df[\"prompt\"].str.strip().str.lower()\n",
+    "    df[\"response\"] = cleaned_prompts.map(query_model)\n",
+    "    print(\"Processing time:\", time.time() - t0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    raw_prompts = [\n",
+    "        \" What is the capital of France? \",\n",
+    "        \" What is the meaning of life? \",\n",
+    "        \" What is the smoothest part of the universe?\",\n",
+    "    ]\n",
+    "\n",
+    "    # Repeat prompts 10 times for testing purposes\n",
+    "    df = pd.DataFrame({\"prompt\": raw_prompts})\n",
+    "    out_df = query_model_all(df)\n",
+    "    print(out_df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "global_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}