TensorRT-Specific LLM Optimizations for Jetson (NVIDIA Edge AI)
🚀 TensorRT-Specific LLM Optimizations for Jetson (NVIDIA Edge AI) TensorRT is NVIDIA’s deep learning optimizer that dramatically improves inference speed for LLMs on Jetson devices . It enables: ✅ Faster inference (2-4x speedup) with lower latency. ✅ Lower power consumption on edge devices. ✅ Optimized memory usage for LLMs. 1️⃣ Install TensorRT & Dependencies First, install TensorRT on your Jetson Orin/Nano : sudo apt update sudo apt install -y nvidia-cuda-toolkit tensorrt python3-libnvinfer Confirm installation: dpkg -l | grep TensorRT 2️⃣ Convert LLM to TensorRT Engine TensorRT requires models in ONNX format before optimization. Convert GGUF/Quantized Model → ONNX First, convert your LLaMA/Mistral model to ONNX format: python convert_to_onnx.py --model model.gguf --output model.onnx (Use onnx_exporter.py from Hugging Face if needed.) 3️⃣ Optimize ONNX with TensorRT Use trtexec to compile the ONNX model into a TensorRT engine: trtexec --onnx=mo...