Hardware Edge Integration
Deploy RetinX models on NVIDIA Jetson, Apple CoreML, and other edge platforms with optimized performance and minimal latency
NVIDIA Jetson Series
High-Performance Edge AI
Deploy RetinX models on NVIDIA Jetson Nano, Xavier NX, and Orin platforms with TensorRT optimization for maximum throughput.
Key Features
- TensorRT INT8/FP16 optimization
- CUDA acceleration support
- Multi-stream processing
- DeepStream SDK integration
- Power-efficient inference
- Industrial-grade reliability
Supported Models
Apple CoreML
iOS & macOS Native Inference
Run RetinX models natively on Apple Silicon with CoreML optimization. Perfect for mobile document scanning and AR applications.
Key Features
- Neural Engine acceleration
- Swift/Objective-C native API
- Background processing support
- Metal GPU optimization
- On-device privacy
- App Store ready
Supported Models
Integration Process
From model training to production deployment in four streamlined steps
Model Export
Export your trained RetinX model to ONNX, TensorRT, or CoreML format
Hardware Optimization
Apply platform-specific optimizations (quantization, pruning, kernel fusion)
Integration Testing
Validate performance on target hardware with real-world test scenarios
Deployment
Deploy to production with monitoring and continuous improvement pipeline
Performance Benchmarks
Real-world performance metrics across different hardware platforms and model sizes
| Platform | Model | FPS | Latency | Power |
|---|---|---|---|---|
| Jetson Orin NX | YOLOv8 Medium | 120 | 8.3ms | 15W |
| Jetson Xavier NX | YOLOv8 Small | 60 | 16.7ms | 10W |
| iPhone 14 Pro | Document OCR | 60 | 16.7ms | 2W |
| MacBook Pro M2 | Face Tracking | 240 | 4.2ms | 5W |
| Jetson Nano | YOLOv8 Nano | 30 | 33.3ms | 7W |
Ready to Deploy on Edge?
Let our team help you optimize and deploy RetinX models on your target hardware platform.
Get Hardware Consultation