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TENSORRT
High-performance deep learning inference optimization SDK
Apache-2.0
ABOUT
Deploying trained neural networks to production requires optimizing them for the target GPU hardware — converting from training frameworks like PyTorch or TensorFlow, applying reduced precision (FP16, INT8), fusing layers, and managing memory. Doing this manually is error-prone and leaves performance on the table. TensorRT automates this whole pipeline: it takes trained models from any framework, applies graph optimizations and kernel auto-tuning for the specific GPU architecture, and outputs a deployable inference engine that runs 2-10x faster than the unoptimized model. Without TensorRT, production AI systems waste GPU cycles and deliver higher latency to users.
INSTALL
pip install tensorrtINTEGRATION GUIDE
1. Deploy a production object detection model at 5x lower latency using INT8 quantization
2. Optimize a PyTorch vision model for real-time inference on edge NVIDIA devices (Jetson)
3. Serve high-throughput NLP models with reduced precision (FP16/INT8) in data centers
4. Build a video analytics pipeline with sub-millisecond inference per frame on T4 GPUs
5. Integrate with Triton Inference Server for production-grade model serving at scale
TAGS
inferencenvidiagpucudaoptimizationquantizationdeep-learningcompiler