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 +1,157 @@
+{
+ "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": 7,
+   "id": "48002406",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 7 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",
+      "✅ 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 7 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": 8,
+   "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
+}