diff --git "a/analyse/analyse_latency_data.ipynb" "b/analyse/analyse_latency_data.ipynb"
new file mode 100644--- /dev/null
+++ "b/analyse/analyse_latency_data.ipynb"
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aec713d7",
+   "metadata": {},
+   "source": [
+    "# Analysation of latency data\n",
+    "In this notebook we want to analyse all the latency data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "48002406",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 8 stats CSV files:\n",
+      "✅ fabian/stats_experiment_Llama-3-2-1B-Instruct-ONNX_always_device_once-per-sec_2025-12-03T20-58-00.csv -> shape (3, 7)\n",
+      "✅ fabian/stats_experiment_gemma-3-270m-it-ONNX_always_device_once-per-sec_2025-12-03T20-41-45.csv -> shape (3, 7)\n",
+      "✅ fabian/stats_experiment_granite-4-0-micro-ONNX-web_always_device_once-per-sec_2025-12-03T22-46-10.csv -> shape (3, 7)\n",
+      "✅ nicolas/lenovo_büro_stats_experiment_gemma-3-270m-it-ONNX_always_device_once-per-sec_2025-12-04T10-26-03.csv -> shape (3, 7)\n",
+      "✅ philip/stats_experiment_Llama-3-2-1B-Instruct-ONNX_always_device_once-per-sec_2025-12-04T08-10-53.csv -> shape (3, 7)\n",
+      "✅ philip/stats_experiment_Qwen3-4B-ONNX_always_device_once-per-sec_2025-12-04T10-12-42.csv -> shape (3, 7)\n",
+      "✅ philip/stats_experiment_gemma-3-270m-it-ONNX_always_device_once-per-sec_2025-12-04T08-01-13.csv -> shape (3, 7)\n",
+      "✅ philip/stats_experiment_granite-4-0-micro-ONNX-web_always_device_once-per-sec_2025-12-04T09-03-29.csv -> shape (3, 7)\n",
+      "\n",
+      "Loaded 8 dataframes\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Define root results directory\n",
+    "results_dir = Path('../results')\n",
+    "\n",
+    "# Find all files containing \"stats\" and ending with .csv in all subdirectories\n",
+    "stats_files = sorted(results_dir.glob('**/*stats*.csv'))\n",
+    "\n",
+    "print(f\"Found {len(stats_files)} stats CSV files:\")\n",
+    "\n",
+    "# Load all stats files into a dictionary of dataframes\n",
+    "stats_dfs = {}\n",
+    "for file_path in stats_files:\n",
+    "    try:\n",
+    "        df = pd.read_csv(file_path)\n",
+    "        # Strip whitespace from column names\n",
+    "        df.columns = df.columns.str.strip()\n",
+    "        # Create key: subfolder_name/filename_stem\n",
+    "        relative_path = file_path.relative_to(results_dir)\n",
+    "        key = str(relative_path.parent / relative_path.stem)\n",
+    "        stats_dfs[key] = df\n",
+    "        print(f\"✅ {relative_path} -> shape {df.shape}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error loading {file_path.name}: {e}\")\n",
+    "\n",
+    "print(f\"\\nLoaded {len(stats_dfs)} dataframes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "20017446",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Extract data for plotting\n",
+    "inference_times = []\n",
+    "accuracies = []\n",
+    "labels = []\n",
+    "\n",
+    "for key, df in stats_dfs.items():\n",
+    "    # Get the \"overall\" row\n",
+    "    overall_row = df[df['route'] == 'overall']\n",
+    "    \n",
+    "    if not overall_row.empty:\n",
+    "        avg_inference = overall_row['avg_inference_time_ms'].values[0]\n",
+    "        accuracy = overall_row['accuracy_percent'].values[0]\n",
+    "        \n",
+    "        inference_times.append(avg_inference)\n",
+    "        accuracies.append(accuracy)\n",
+    "        \n",
+    "        # Extract model name from filename and subfolder\n",
+    "        # key format: \"subfolder/stats_experiment_MODEL_NAME_...\"\n",
+    "        subfolder, filename = key.rsplit('/', 1) if '/' in key else ('root', key)\n",
+    "        model_name = filename.replace('stats_experiment_', '').split('_')[0:3]  # adjust based on your naming\n",
+    "        label = f\"{subfolder}\\n{filename.replace('stats_experiment_', '')[:50]}\"  # truncate for readability\n",
+    "        labels.append(label)\n",
+    "\n",
+    "# Create scatter plot\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "plt.scatter(inference_times, accuracies, s=100, alpha=0.6, edgecolors='black')\n",
+    "\n",
+    "# Add labels to each point\n",
+    "for i, label in enumerate(labels):\n",
+    "    plt.annotate(label, (inference_times[i], accuracies[i]), \n",
+    "                fontsize=8, ha='right', xytext=(5, 5), \n",
+    "                textcoords='offset points', wrap=True)\n",
+    "\n",
+    "plt.xlabel('Avg Inference Time (ms)', fontsize=12)\n",
+    "plt.ylabel('Accuracy (%)', fontsize=12)\n",
+    "plt.title('Inference Time vs Accuracy Across All Experiments', fontsize=14, fontweight='bold')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "648c05a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "genai",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}