| framework | Backend | Device | Single | Half | Quant. | Results |
|---|---|---|---|---|---|---|
TensorFlow Lite | CPU | Intel(R) N150 | 601 | 612 | 430 | https://browser.geekbench.com/ai/v1/247221 |
ONNX | CPU | Intel(R) N150 | 755 | 295 | 1449 | https://browser.geekbench.com/ai/v1/247226 |
OpenVINO | CPU | Intel(R) N150 |
| Workload (TF Lite) | Accuracy | Score | Workload (ONNX) | Accuracy | Score | |||
|---|---|---|---|---|---|---|---|---|
| Image Classification (SP) | 100% | 406 75.5 IPS | Image Classification (SP) | 100% | 522 97.0 IPS | |||
| Image Classification (HP) | 100% | 411 76.5 IPS | Image Classification (HP) | 100% | 97 18.1 IPS | |||
| Image Classification (Q) | 99% | 275 51.2 IPS | Image Classification (Q) | 97% | 1131 211.0 IPS | |||
| Image Segmentation (SP) | 100% | 517 8.38 IPS | Image Segmentation (SP) | 100% | 445 7.22 IPS | |||
| Image Segmentation (HP) | 100% | 515 8.35 IPS | Image Segmentation (HP) | 100% | 146 2.37 IPS | |||
| Image Segmentation (Q) | 98% | 332 5.40 IPS | Image Segmentation (Q) | 99% | 954 15.5 IPS | |||
| Pose Estimation (SP) | 100% | 632 0.74 IPS | Pose Estimation (SP) | 100% | 997 1.16 IPS | |||
| Pose Estimation (HP) | 100% | 649 0.76 IPS | Pose Estimation (HP) | 100% | 753 0.88 IPS | |||
| Pose Estimation (Q) | 96% | 611 0.72 IPS | Pose Estimation (Q) | 94% | 2459 2.88 IPS | |||
| Object Detection (SP) | 100% | 369 29.3 IPS | Object Detection (SP) | 100% | 489 38.8 IPS | |||
| Object Detection (HP) | 100% | 378 30.0 IPS | Object Detection (HP) | 100% | 122 9.68 IPS | |||
| Object Detection (Q) | 85% | 286 23.0 IPS | Object Detection (Q) | 86% | 1114 89.5 IPS | |||
| Face Detection (SP) | 100% | 771 9.16 IPS | Face Detection (SP) | 100% | 1086 12.9 IPS | |||
| Face Detection (HP) | 100% | 870 10.3 IPS | Face Detection (HP) | 100% | 207 2.46 IPS | |||
| Face Detection (Q) | 97% | 719 8.57 IPS | Face Detection (Q) | 97% | 2934 35.0 IPS | |||
| Depth Estimation (SP) | 100% | 724 5.58 IPS | Depth Estimation (SP) | 100% | 1312 10.1 IPS | |||
| Depth Estimation (HP) | 99% | 747 5.76 IPS | Depth Estimation (HP) | 99% | 462 3.56 IPS | |||
| Depth Estimation (Q) | 63% | 549 5.15 IPS | Depth Estimation (Q) | 78% | 2596 20.7 IPS | |||
| Style Transfer (SP) | 100% | 1256 1.61 IPS | Style Transfer (SP) | 100% | 2473 3.18 IPS | |||
| Style Transfer (HP) | 100% | 1264 1.63 IPS | Style Transfer (HP) | 100% | 1993 2.56 IPS | |||
| Style Transfer (Q) | 98% | 1454 1.87 IPS | Style Transfer (Q) | 98% | 4651 6.00 IPS | |||
| Image Super-Resolution (SP) | 100% | 362 13.4 IPS | Image Super-Resolution (SP) | 100% | 588 21.7 IPS | |||
| Image Super-Resolution (HP) | 100% | 338 12.5 IPS | Image Super-Resolution (HP) | 100% | 430 15.9 IPS | |||
| Image Super-Resolution (Q) | 97% | 357 13.2 IPS | Image Super-Resolution (Q) | 99% | 876 32.4 IPS | |||
| Text Classification (SP) | 100% | 612 816.3 IPS | Text Classification (SP) | 100% | 297 397.0 IPS | |||
| Text Classification (HP) | 100% | 614 819.4 IPS | Text Classification (HP) | 100% | 180 240.0 IPS | |||
| Text Classification (Q) | 92% | 266 356.9 IPS | Text Classification (Q) | 97% | 412 552.4 IPS | |||
| Machine Translation (SP) | 100% | 819 14.1 IPS | Machine Translation (SP) | 100% | 875 15.1 IPS | |||
| Machine Translation (HP) | 100% | 833 14.3 IPS | Machine Translation (HP) | 100% | 267 4.60 IPS | |||
| Machine Translation (Q) | 58% | 253 5.95 IPS | Machine Translation (Q) | 65% | 1085 21.8 IPS |
Install
# OpenVino wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB echo "deb https://apt.repos.intel.com/openvino/2025 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2025.list apt update apt-cache search openvino apt install openvino python3 /usr/share/openvino/samples/python/hello_query_device/hello_query_device.py #Geekbench mkdir Geekbench cd Geekbench wget https://cdn.geekbench.com/GeekbenchAI-1.3.0-Linux.tar.gz tar xvf GeekbenchAI-1.3.0-Linux.tar.gz cd GeekbenchAI-1.3.0-Linux/
Check Available frameworks
root@server5:/storage/apps/Geekbench/GeekbenchAI-1.3.0-Linux# ./banff --ai-list Geekbench AI 1.3.0 : https://www.geekbench.com/ai/ Geekbench AI requires an active internet connection and automatically uploads benchmark results to the Geekbench Browser. Framework | Backend | Device 1 TensorFlow Lite | 1 CPU | 0 Intel(R) N150 3 ONNX | 1 CPU | 0 Intel(R) N150 4 OpenVINO | 1 CPU | 0 Intel(R) N150
help
root@server5:/storage/apps/Geekbench/GeekbenchAI-1.3.0-Linux# ./banff --help Geekbench AI 1.3.0 : https://www.geekbench.com/ai/ Usage: ./banff [ options ] Options: -h, --help print this message AI Benchmark Options: --ai run the AI benchmark --ai-framework [ID] use AI framework ID --ai-backend [ID] use AI backend ID --ai-device [ID] use AI device ID --ai-list list available AI settings If no options are given, the default action is to run the inference benchmark.
example run
root@server5:/storage/apps/Geekbench/GeekbenchAI-1.3.0-Linux# ./banff --ai-framework 1 Geekbench AI 1.3.0 : https://www.geekbench.com/ai/ Geekbench AI requires an active internet connection and automatically uploads benchmark results to the Geekbench Browser. AI Information Framework TensorFlow Lite Backend CPU Device Intel(R) N150 System Information Operating System Ubuntu 24.04.2 LTS Model GMKtec NucBoxG9 Motherboard GMKtec GMKtec BIOS American Megatrends International, LLC. 5.27 CPU Information Name Intel(R) N150 Topology 1 Processor, 4 Cores Identifier GenuineIntel Family 6 Model 190 Stepping 0 Base Frequency 3.60 GHz Memory Information Size 11.4 GB Running Image Classification (SP) INFO: Initialized TensorFlow Lite runtime. INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Image Classification (HP) INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Image Classification (Q) Running Image Segmentation (SP) INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Image Segmentation (HP) INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Image Segmentation (Q) Running Pose Estimation (SP) INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Pose Estimation (HP) INFO: Applying 1 TensorFlow Lite delegate(s) lazily. Running Pose Estimation (Q) ...
