Jetson Benchmarks
Jetson is used to deploy a wide range of popular DNN models, optimized transformer models and ML frameworks to the edge with high performance inferencing, for tasks like real-time classification and object detection, pose estimation, semantic segmentation, and natural language processing (NLP).
MLPerf Inference Benchmarks
The tables below show inferencing benchmarks from the NVIDIA Jetson submissions to the MLPerf Inference Edge category.
Jetson AGX Orin MLPerf v4.0 Results
| Model | NVIDIA Jetson AGX Orin (TensorRT) | |
|---|---|---|
| Single Stream Latency (ms) | Offline (Samples/s) | |
| LLM Summarization GPT-J 6B |
10204.46 | 0.15 |
| Image Generation stable-diffusion-xl |
12941.92 | 0.08 |
- Full Results can be found at v4.0 Results | MLCommons
- These results were achieved with the NVIDIA Jetson AGX Orin Developer Kit running JetPack 5.1.1, TensorRT 9.0.1 and CUDA 11.4
- These MLPerf Results can be reproduced with the code in the following link: https://github.com/mlcommons/inference_results_v4.0/tree/main/closed/NVIDIA
Jetson AGX Orin and Jetson Orin NX MLPerf v3.1 Results
| Model | NVIDIA Jetson AGX Orin (TensorRT) | NVIDIA Orin MaxQ (TensorRT) | NVIDIA Jetson Orin NX | NVIDIA Jetson Orin NX MaxQ | ||||
|---|---|---|---|---|---|---|---|---|
| Single Stream Latency (ms) | Offline (Samples/s) | Multi Stream Latency(ms) | Offline (Samples/s) | System Power(W) | Offline (Samples/s) | Offline (Samples/s) | System Power(W) | |
| Image Classification ResNet |
0.64 | 6423.63 | 2.18 | 3526.29 | 23.57 | 2640.51 | 1681.87 | 14.95 |
| Object Detection Retinanet |
11.67 | 148.71 | 82.92 | 74.71 | 22.27 | 66.5 | 47.59 | 15.57 |
| Medical Imaging 3D-Unet-99.0 |
4371.46 | 0.51 | K.A. | K.A. | K.A. | 0.2 | 0.19 | 22.04 |
| Speech-to-text RNN-T |
94.01 | 1169.98 | K.A. | K.A. | K.A. | 431.92 | 327.79 | 17.25 |
| Natural Language Processing BERT |
5.71 | 553.69 | K.A. | K.A. | K.A. | 194.5 | 136.59 | 17.04 |
- Full Results can be found at v3.1 Results | MLCommons
- These results were achieved with the NVIDIA Jetson AGX Orin Developer Kit and Orin NX 16GB running JetPack 5.1.1, TensorRT 8.5.2 and CUDA 11.4 A
- These MLPerf Results can be reproduced with the code in the following link: https://github.com/mlcommons/inference_results_v3.1/tree/main/closed/NVIDIA
Jetson AGX Orin Jetson Orin NX MLPerf v3.0 Results
| Model | NVIDIA Jetson AGX Orin (TensorRT) | NVIDIA Orin MaxQ (TensorRT) | NVIDIA Jetson Orin NX | ||||
|---|---|---|---|---|---|---|---|
| Single Stream (Samples/s) | Offline (Samples/s) | Multi Stream (Samples/s) | Offline (Samples/s) | System Power(W) | Offline (Samples/s) | ||
| Image Classification ResNet-50 |
1538 | 6438.10 | 3686 | 3525.91 | 23.06 | 2517.99 | |
| Object Detection Retinanet |
51.57 | 92.40 | 60.00 | 34.6 | 22.4 | 36.14 | |
| Medical Imaging 3D-Unet |
.26 | .51 | K.A. | 3.28 | 28.64 | .19 | |
| Speech-to-text RNN-T |
9.822 | 1170.23 | K.A. | 14472 | 25.64 | 405.27 | |
| Natural Language Processing BERT |
144.36 | 544.24 | K.A. | 3685.36 | 25.91 | 163.57 | |
- Steps to reproduce these results can be found at v3.0 Results | MLCommons
- These results were achieved with the NVIDIA Jetson AGX Orin Developer Kit running a preview of TensorRT 8.5.0, and CUDA 11.4
- Note different configurations were used for single stream, offline and multistream. Reference the MLCommons page for more details
Gen AI Benchmarks
NVIDIA Jetson AI Lab is a collection of tutorials showing how to run optimized models on NVIDIA Jetson, including the latest generative AI and transformer models. These tutorials span a variety of model modalities like LLMs (for text), VLMs (for text and vision data), ViT (Vision Transformers), image generation, and ASR or TTS (for audio).
Large Language Models (LLM)
Small Language Models (SLM)
Vision Transformers (ViT)
Riva
- Full Results can be found at Jetson AI Lab Benchmarks