diff --git a/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb b/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb
index 89582fc..f629b00 100644
--- a/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb
+++ b/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb
@@ -29,28 +29,41 @@
     "**[Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)** [(Section 2)](#Section-2:-Serverless-Inference)\n",
     "- ✅ Pay only for active inference time, no infrastructure management\n",
     "- ✅ Cost-efficient for intermittent or unpredictable traffic\n",
-    "- ❌ Cold start latency on first request after idle, CPU only, 6GB memory limit\n",
+    "- ❌ Cold start latency on first request after idle, CPU only, lowest throughput of all options\n",
     "- ❌ [More complex setup](#Setup-for-Serverless-and-Batch-Transform) (requires repackaging model artifacts)\n",
     "\n",
     "**[Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html)** [(Section 3)](#Section-3:-Batch-Transform)\n",
     "- ✅ Pay only for active compute time, no persistent infrastructure\n",
     "- ✅ Cost-efficient for large-scale batch prediction jobs\n",
-    "- ❌ Highest latency (not for real-time use), CPU only, requires data in S3\n",
+    "- ❌ Initialization takes severa minutes for each job (not for real-time use), CPU only, requires data in S3\n",
     "- ❌ [More complex setup](#Setup-for-Serverless-and-Batch-Transform) (requires repackaging model artifacts)\n",
     "\n",
-    "**Reference benchmark** on M5 dataset (30K daily retail time series, prediction_length=28):\n",
-    "| Mode | Instance | Time |\n",
+    "**Reference benchmark** on a dataset with 1M rows (2000 time series with 500 observations each) and prediction length of 28:\n",
+    "| Mode | Instance | Inference time (s) |\n",
     "|------|----------|------|\n",
-    "| Real-time (GPU) | ml.g5.2xlarge | X min |\n",
-    "| Real-time (CPU) | ml.c5.4xlarge | X min |\n",
-    "| Serverless | 6GB memory | X min |\n",
-    "| Batch Transform | ml.c5.4xlarge | X min |\n",
+    "| Real-time (GPU) | ml.g5.2xlarge | 18 |\n",
+    "| Real-time (CPU) | ml.c5.4xlarge | 50 |\n",
+    "| Serverless | 6GB memory | 120 |\n",
+    "| Batch Transform | ml.c5.4xlarge | 60 (+200s setup) |\n",
     "\n",
     "We recommend starting with **Real-time Inference** as it offers the simplest setup and highest throughput. Consider Serverless or Batch Transform when you need to optimize costs and don't require GPU acceleration.\n",
     "\n",
     "For a complete specification of all supported request parameters, see the [Endpoint API Reference](#Endpoint-API-Reference) at the end of this notebook."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15b5fd55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GPU: 18s\n",
+    "# CPU: 50s\n",
+    "# Serverless: 120s\n",
+    "# Batch transform: 60s (+200s setup)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "78b40323",
@@ -879,7 +892,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    "## Setup for Serverless and Batch Transform\n",
+    "## Setup for Serverless Inference and Batch Transform\n",
     "\n",
     "Serverless Inference and Batch Transform only support CPU instances. Unlike real-time inference with JumpStart, these modes require you to create a custom SageMaker Model with repackaged artifacts.\n",
     "\n",
@@ -999,6 +1012,30 @@
     "chronos_model.create()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "129bc389",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can load an existing model as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7cb0f14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model_info = boto3.client(\"sagemaker\").describe_model(ModelName=\"chronos-2-cpu\")\n",
+    "# model = Model(\n",
+    "#     model_data=model_info[\"PrimaryContainer\"][\"ModelDataUrl\"],\n",
+    "#     image_uri=model_info[\"PrimaryContainer\"][\"Image\"],\n",
+    "#     role=model_info[\"ExecutionRoleArn\"],\n",
+    "#     name=model_info[\"ModelName\"],\n",
+    "# )"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ba12b52d",