diff --git "a/blog/multi-lora-serving/multi-lora-cost.ipynb" "b/blog/multi-lora-serving/multi-lora-cost.ipynb"
new file mode 100644--- /dev/null
+++ "b/blog/multi-lora-serving/multi-lora-cost.ipynb"
@@ -0,0 +1,1208 @@
+{
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+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ee85de-bff3-4243-9aae-ca7fa4d074e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q huggingface-hub plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "644fd907-3912-49dd-bc25-b73eb0abf532",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed69c6f0-44a0-43cf-96cf-143d2d92ad30",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Create Plot"
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+  {
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+   "id": "da6a0eba-9b3d-43d2-a689-f9399e680fa5",
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+          "text": "Number of Fine-Tuned Models"
+         },
+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "range": [
+          0,
+          8.76842105263158
+         ],
+         "tickfont": {
+          "size": 16
+         },
+         "title": {
+          "font": {
+           "size": 28
+          },
+          "text": "Cost ($/million tokens)"
+         },
+         "type": "linear"
+        }
+       }
+      },
+      "image/png": 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of Fine-Tuned Models\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16},\"type\":\"category\"},\"yaxis\":{\"title\":{\"text\":\"Cost ($\\u002fmillion tokens)\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16}},\"legend\":{\"title\":{\"font\":{\"size\":28}},\"font\":{\"size\":24},\"x\":0.5,\"y\":-0.1,\"orientation\":\"h\",\"xanchor\":\"center\",\"yanchor\":\"top\"},\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('1d4ef925-74c3-4370-8148-03c1171b5426');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "# Updated data\n",
+    "number_of_models = [\"1\", \"2\", \"4\", \"8\", \"16\", \"32\"]\n",
+    "tgi_multilora = [0.26, 0.26, 0.26, 0.26, 0.26, 0.26]\n",
+    "tgi_dedicated = [0.26, 0.52, 1.04, 2.08, 4.16, 8.33]\n",
+    "gpt_3_5_turbo = [4.83, 4.83, 4.83, 4.83, 4.83, 4.83]\n",
+    "\n",
+    "# Create traces\n",
+    "fig = go.Figure()\n",
+    "fig.add_trace(go.Bar(x=number_of_models, y=tgi_multilora, name='TGI Multi-LoRA', marker_color='rgb(255, 215, 0)'))  # Golden color\n",
+    "fig.add_trace(go.Bar(x=number_of_models, y=tgi_dedicated, name='TGI Dedicated', marker_color='rgb(64, 64, 64)'))  # Dark grey\n",
+    "fig.add_trace(go.Bar(x=number_of_models, y=gpt_3_5_turbo, name='gpt-3.5-turbo', marker_color='rgb(169, 169, 169)'))  # Grey\n",
+    "\n",
+    "# Layout settings with larger text sizes\n",
+    "fig.update_layout(\n",
+    "    # title='Cost per Million Tokens by Model Configuration',\n",
+    "    title_font_size=24,  # Larger title font size\n",
+    "    xaxis_title='Number of Fine-Tuned Models',\n",
+    "    yaxis_title='Cost ($/million tokens)',\n",
+    "    xaxis=dict(\n",
+    "        type='category',\n",
+    "        title_font_size=28,  # Larger axis title font size\n",
+    "        tickfont_size=16  # Larger tick label font size\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        title_font_size=28,  # Larger axis title font size\n",
+    "        tickfont_size=16  # Larger tick label font size\n",
+    "    ),\n",
+    "    legend=dict(\n",
+    "        x=0.5,  # Horizontal position, 0 is left\n",
+    "        y=-0.1,\n",
+    "        # y=-0.3,  # Vertical position, negative values to move it down\n",
+    "        orientation=\"h\",  # Horizontal layout\n",
+    "        xanchor='center',  # Anchor the legend at the left\n",
+    "        yanchor='top'  # Anchor the legend at the top\n",
+    "    ),\n",
+    "    barmode='group',\n",
+    "    # legend_title_text='Model',\n",
+    "    legend_title_font_size=28,  # Larger legend title font size\n",
+    "    legend_font_size=24  # Larger legend font size\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5620af3-037f-4f78-81b8-ed80dea6ec6e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Push Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "da5bfebe-fe17-4e97-8d71-5db736642ac9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Set the size for 16:9 aspect ratio and save as PNG\n",
+    "image_out = \"multi-lora-cost.png\"\n",
+    "fig.write_image(image_out, width=1920, height=1080, scale=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "33bdcc3f-f94e-4a6b-bb96-05d39267994d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " Cleaning up notebook\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/huggingface/documentation-images/commit/d6302d45be5810016615dc065b07f244a6f94f98', commit_message='Cleaning up notebook', commit_description='', oid='d6302d45be5810016615dc065b07f244a6f94f98', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "\n",
+    "commit_message_image = input()\n",
+    "api = HfApi()\n",
+    "api.upload_file(\n",
+    "    path_or_fileobj=image_out,\n",
+    "    path_in_repo=f\"blog/multi-lora-serving/{image_out}\",\n",
+    "    repo_id=\"huggingface/documentation-images\",\n",
+    "    repo_type=\"dataset\",\n",
+    "    commit_message=commit_message_image,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62fd36dc-2f6c-4e3f-8441-a544803258f5",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Push Notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d6b741-a888-463a-9ea3-78437e89d453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "\n",
+    "notebook = \"multi-lora-cost.ipynb\"\n",
+    "commit_message_notebook = input()\n",
+    "\n",
+    "api = HfApi()\n",
+    "api.upload_file(\n",
+    "    path_or_fileobj=notebook,\n",
+    "    path_in_repo=f\"blog/multi-lora-serving/{notebook}\",\n",
+    "    repo_id=\"huggingface/documentation-images\",\n",
+    "    repo_type=\"dataset\",\n",
+    "    commit_message=commit_message_notebook,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc00cbac-773f-4acd-a338-6a22d2624edb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}