How to Increase FPS on Jetson Xavier NX for Real-Time AI & Video Processing?

Robotics & Edge Computing Jetson & Embedded Systems Jetson Xavier NX
1

Hello,

I am using a Jetson Xavier NX for real-time Object detection and tracking usually with Yolov8. My current setup includes:

YOLOv8n model converted to TensorRT

Torch, TorchVision, and ONNX installed with CUDA support

OpenCV compiled with CUDA support

Despite this, I am only achieving 12–15 FPS.

I would like to know:

How to properly increase FPS on the Jetson Xavier NX for real-time AI inference and video streaming.

Which settings, optimizations, or modes can maximize performance for TensorRT models on this platform.

Any best practices, recommended configurations, or system-level tweaks specific to Jetson Xavier NX.

waiting for your help.

2

Did you convert the model to FP16?

4

Hi,

Could you check GPU utilization with tegrastats?

$ sudo tegrastats

It’s common that the pre-processing or post-processing, which run on CPU, takes long time to finish.
To solve this, it’s recommended to run YOLO with Deepstream:

Thanks.

5

Thank you.
yes I did:
/usr/src/tensorrt/bin/trtexec --onnx=model.onnx --saveEngine=model.engine --fp16
now it is:

J_in
J_in630×224 37.2 KB

6

Thank you!

Okay, I’ll use DeepStream.

I used this command:

$ sudo tegrastats

Screenshot from 2025-10-07 05-12-47
Screenshot from 2025-10-07 05-12-47728×220 56 KB

Are there other ways to increase Jetson’s speed or FPS?