Edge AI with NVIDIA Jetson: Deploy AI on Embedded Devices

What is Edge AI and Why Jetson?

Edge AI means running AI models directly on devices instead of sending to cloud. Advantages: ultra-low latency (<30ms), offline operation, data security, bandwidth savings. In robotics, edge AI is mandatory — robot can't wait 200ms round-trip to cloud to avoid obstacle.

NVIDIA Jetson is SBC line designed for AI inference with integrated CUDA GPUs.

Jetson Product Line

Module	GPU Cores	RAM	AI Performance	Price (~)
Jetson Nano	128 CUDA	4GB	472 GFLOPS	$149
Jetson Orin Nano	1024 CUDA	8GB	40 TOPS	$249
Jetson Orin NX	1024 CUDA	16GB	100 TOPS	$599
Jetson AGX Orin	2048 CUDA	64GB	275 TOPS	$1999

For mobile robots, Jetson Orin Nano optimal — best performance/price/power (15W).

Environment Setup

JetPack SDK

# Install JetPack (includes CUDA, cuDNN, TensorRT)
sudo apt update && sudo apt install -y nvidia-jetpack

# Verify CUDA
nvcc --version
python3 -c "import torch; print(torch.cuda.is_available())"

Development Container

NVIDIA provides ready-made containers:

# Pull PyTorch container for Jetson
sudo docker pull nvcr.io/nvidia/l4t-pytorch:r36.2.0-pth2.1-py3

# Run with GPU access
sudo docker run -it --runtime nvidia --network host \
  nvcr.io/nvidia/l4t-pytorch:r36.2.0-pth2.1-py3

TensorRT Optimization

TensorRT accelerates models 2-5x vs PyTorch native.

YOLOv8 to TensorRT Conversion

from ultralytics import YOLO

model = YOLO("yolov8n.pt")

# Export to TensorRT FP16
model.export(format="engine", device=0, half=True, imgsz=640)

Performance on Jetson Orin Nano

Format	FPS (640x640)	Latency
PyTorch FP32	12 FPS	83ms
TensorRT FP32	28 FPS	36ms
TensorRT FP16	45 FPS	22ms
TensorRT INT8	62 FPS	16ms

Quantization: FP16 and INT8

FP16 reduces memory half and speeds inference with minimal accuracy loss — default choice for Jetson.

INT8 needs calibration but maximum speed:

import tensorrt as trt

config.set_flag(trt.BuilderFlag.INT8)
config.int8_calibrator = EntropyCalibrator(
    calibration_data="/path/to/calib_images/",
    cache_file="calibration.cache"
)

Complete Real-time Pipeline

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.engine")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Inference
    results = model.predict(frame, conf=0.5, verbose=False)

    # Process results
    for box in results[0].boxes:
        cls = int(box.cls[0])
        conf = float(box.conf[0])
        x1, y1, x2, y2 = map(int, box.xyxy[0])

        # Send command to robot based on detection
        if cls == 0:  # person detected
            distance = estimate_distance(y2 - y1)
            if distance < 1.0:
                send_stop_command()

Power Management

# View power mode
sudo nvpmodel -q

# MAXN mode (15W, high performance)
sudo nvpmodel -m 0

# Maximize performance
sudo jetson_clocks

Deployment Tips

Always use TensorRT FP16 — baseline, only switch INT8 for extra FPS
Pre-process on GPU: use cv2.cuda instead of CPU OpenCV
Batch inference: group multiple frames if latency allows
Monitor temperature: Jetson throttles at 80°C

Edge AI with Jetson enables deploying powerful AI directly on robots without cloud dependency, meeting strictest real-time requirements. Combined with Python robot programming, build complete AI-to-action pipeline.