MLOps
Deploying AI Models to the Edge with TensorRT & ONNX
2025-01-22 11 min readRohit Raj
TensorRTONNXEdge AIDeployment
Edge AI deployment is crucial for real-time applications where cloud latency is unacceptable. Here's how I optimized our sign language models for edge deployment.
The Pipeline
PyTorch → ONNX → TensorRT → Edge Device
Each step reduces model size and improves inference speed at the cost of some flexibility.
Key Optimizations
- ▸**Quantization**: INT8 quantization reduced model size by 4x with minimal accuracy loss
- ▸**Pruning**: Removed 30% of weights with negligible impact on performance
- ▸**TensorRT**: Achieved 3x speedup over vanilla PyTorch inference
Results
Our final model ran at 45ms inference time on an NVIDIA Jetson Nano — fast enough for real-time sign language translation.