diff --git a/notebooks/community/model_garden/model_garden_pytorch_qwq_deployment.ipynb b/notebooks/community/model_garden/model_garden_pytorch_qwq_deployment.ipynb
index 5e2d26f77..0db62b605 100644
--- a/notebooks/community/model_garden/model_garden_pytorch_qwq_deployment.ipynb
+++ b/notebooks/community/model_garden/model_garden_pytorch_qwq_deployment.ipynb
@@ -224,17 +224,25 @@
         "use_dedicated_endpoint = True  # @param {type:\"boolean\"}\n",
         "\n",
         "accelerator_type = \"NVIDIA_L4\"  # @param [\"NVIDIA_L4\", \"NVIDIA_H100_80GB\"] {isTemplate: true}\n",
-        "max_model_len = 32768  # Maximum context length without YaRN.\n",
-        "gpu_memory_utilization = 0.9\n",
         "\n",
+        "# @markdown For inputs exceeding 8,192 tokens, enable [YaRN](https://arxiv.org/abs/2309.00071) on the deployment to improve the model's ability to capture long-sequence information effectively.\n",
+        "# @markdown Enabling YaRN will allow for 128k context-length but may require more GPUs.\n",
+        "enable_yarn_scaling = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "if enable_yarn_scaling:\n",
+        "    max_model_len = 131072\n",
+        "else:\n",
+        "    max_model_len = 32768\n",
         "\n",
         "if accelerator_type == \"NVIDIA_L4\":\n",
-        "    accelerator_count = 4\n",
-        "    # Sets machine type to g2-standard-48 for 4 L4s\n",
-        "    machine_type = \"g2-standard-48\"\n",
+        "    if enable_yarn_scaling:\n",
+        "        accelerator_count = 8\n",
+        "        machine_type = \"g2-standard-96\"\n",
+        "    else:\n",
+        "        accelerator_count = 4\n",
+        "        machine_type = \"g2-standard-48\"\n",
         "elif accelerator_type == \"NVIDIA_H100_80GB\":\n",
         "    accelerator_count = 2\n",
-        "    # Sets machine type to a3-highgpu-2g for 2 NVIDIA H100 80GBs.\n",
         "    machine_type = \"a3-highgpu-2g\"\n",
         "else:\n",
         "    raise ValueError(\n",
@@ -276,6 +284,7 @@
         "    use_dedicated_endpoint: bool = False,\n",
         "    max_num_seqs: int = 256,\n",
         "    model_type: str = None,\n",
+        "    enable_yarn_scaling: bool = False,\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
         "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
         "    endpoint = aiplatform.Endpoint.create(\n",
@@ -324,6 +333,10 @@
         "        vllm_args.append(\n",
         "            f\"--host-prefix-kv-cache-utilization-target={host_prefix_kv_cache_utilization_target}\"\n",
         "        )\n",
+        "    if enable_yarn_scaling:\n",
+        "        vllm_args.append(\n",
+        "            '--rope-scaling=\\'{\"factor\":4.0,\"original_max_position_embeddings\":32768,\"type\":\"yarn\"}\\''\n",
+        "        )\n",
         "\n",
         "    if model_type:\n",
         "        vllm_args.append(f\"--model-type={model_type}\")\n",
@@ -383,9 +396,11 @@
         "    accelerator_type=accelerator_type,\n",
         "    accelerator_count=accelerator_count,\n",
         "    max_model_len=max_model_len,\n",
-        "    gpu_memory_utilization=gpu_memory_utilization,\n",
+        "    gpu_memory_utilization=0.9,\n",
         "    enable_chunked_prefill=True,\n",
         "    use_dedicated_endpoint=use_dedicated_endpoint,\n",
+        "    max_num_seqs=128,\n",
+        "    enable_yarn_scaling=enable_yarn_scaling,\n",
         ")\n",
         "\n",
         "# @markdown Click \"Show Code\" to see more details."