Why hardware choice matters
NVIDIA Jetson devices are the industry standard for edge AI, but deploying YOLO requires a careful balance of model complexity, latency requirements and power envelope.
- Model size (Nano vs extra-large)
- Inference speed (real-time vs batch)
- Power budget (5W to 60W)
- Environment (indoor vs rugged)
- 275+
- Peak TOPS (AGX Orin)
- 100+
- Real-world FPS (AGX Orin)
Jetson modules compared
Search criteria first: how much model you need to run, how fast, and inside which power budget.
Legacy / entry
Jetson Nano
- AI performance
- 0.5 TFLOPS
- Memory
- 4GB LPDDR4
- YOLO FPS (engine)
- 5-10 FPS
Best for
Educational projects, static detection, proof-of-concept prototypes.
Performance mid
Xavier NX
- AI performance
- 21 TOPS
- Memory
- 8GB LPDDR4x
- YOLO FPS (engine)
- 25-35 FPS
Best for
Advanced robotics, drones, and multi-camera streams.
Modern entry
Orin Nano
- AI performance
- 40 TOPS
- Memory
- 8GB LPDDR5
- YOLO FPS (engine)
- 35-45 FPS
Best for
IoT devices, transformer-based models, and retail analytics.
Industrial
AGX Orin
- AI performance
- 275 TOPS
- Memory
- 64GB LPDDR5
- YOLO FPS (engine)
- 120+ FPS
Best for
Autonomous driving, large-scale industrial automation, server-grade edge.
System benchmarks
Typical production requirements cross-referenced against hardware constraints.
Recommended module by application
| Application | Target FPS | Recommended module | Confidence |
|---|---|---|---|
| Smart Camera / Security | 15-20 | Jetson Orin Nano | 95% |
| Drones & UAVs | 30-40 | Xavier NX | 90% |
| Industrial Quality Control | 60-90 | AGX Orin 32GB | 99% |
| Low-Power Mobile Vision | 10-15 | Orin Nano 4GB | 85% |
Deployment support
Deploying production edge AI with AxcelerateAI
Picking the module is step one; getting a model to run fast and stay running is the rest of the work.
TensorRT optimisation
We compile and tune YOLO engines so Jetson cores return every millisecond they can.
Fleet management
Secure over-the-air updates across large-scale hardware deployments.
Model pruning and quantisation
Fitting larger YOLO models onto memory-constrained edge modules.
