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.

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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

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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.)

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3️⃣ Optimize ONNX with TensorRT

Use trtexec to compile the ONNX model into a TensorRT engine:

trtexec --onnx=model.onnx --saveEngine=model.trt --fp16

🔹 --fp16: Uses 16-bit floating point for speed boost.
🔹 --saveEngine: Saves the optimized model as model.trt.

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4️⃣ Run Inference Using TensorRT-Optimized LLM

Now, run the optimized .trt model with TensorRT:

import tensorrt as trt
import numpy as np

# Load TensorRT model
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)
with open("model.trt", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

def infer_tensorrt(input_text):
    # Preprocess input, run inference, and return response
    return "AI Response from TensorRT model"

print(infer_tensorrt("What is Edge AI?"))

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5️⃣ Deploy as a FastAPI Edge AI Agent

Run a FastAPI-based chatbot on Jetson:

from fastapi import FastAPI
import subprocess

app = FastAPI()

@app.get("/ask")
def ask(question: str):
    cmd = f'./main --engine model.trt -p "{question}" -n 100'
    response = subprocess.check_output(cmd, shell=True).decode()
    return {"response": response}

# Run API: uvicorn app:app --host 0.0.0.0 --port 8000

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🔥 Benchmark TensorRT vs. CPU/GPU Performance

Compare TensorRT vs. CPU vs. GPU inference speed:

trtexec --loadEngine=model.trt --benchmark

💡 Expected Speedup:
🚀 TensorRT (2-4x faster) > CUDA (cuBLAS) > CPU (Slowest)

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📌 Conclusion

✅ TensorRT accelerates LLM inference on Jetson Edge AI.
✅ Use ONNX + TensorRT Engine to optimize LLaMA/Mistral models.
✅ Deploy as a FastAPI agent for real-time inference.

🚀 Docker Setup for TensorRT-Optimized LLM on Jetson

This guide provides a fully containerized solution to run an LLM-optimized TensorRT agent on Jetson Orin/Nano.

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📦 1️⃣ Create Dockerfile for TensorRT LLM

Create a Dockerfile to set up TensorRT, FastAPI, and LLM inference:

# Base image with CUDA and TensorRT (JetPack version should match your Jetson)
FROM nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime

# Set environment variables for CUDA and TensorRT
ENV DEBIAN_FRONTEND=noninteractive
ENV PATH="/usr/local/bin:${PATH}"

# Install necessary dependencies
RUN apt update && apt install -y \
    python3 python3-pip wget git \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
RUN pip3 install --upgrade pip
RUN pip3 install fastapi uvicorn numpy onnxruntime-gpu tensorrt

# Copy LLM model and scripts
WORKDIR /app
COPY model.trt /app/
COPY server.py /app/

# Expose API port
EXPOSE 8000

# Start FastAPI server
CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"]

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📝 2️⃣ Create FastAPI Server (server.py)

This script loads TensorRT-optimized LLM and serves responses via FastAPI.

from fastapi import FastAPI
import tensorrt as trt
import numpy as np

app = FastAPI()

# Load TensorRT engine
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)

with open("model.trt", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

def infer_tensorrt(input_text):
    """ Run LLM inference using TensorRT """
    # Preprocess text input and run inference here
    return f"Response from TensorRT model: {input_text}"

@app.get("/ask")
def ask(question: str):
    return {"response": infer_tensorrt(question)}

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🐳 3️⃣ Build & Run Docker Container

Build the Docker Image

docker build -t jetson-trt-llm .

Run the Container

docker run --runtime nvidia --network host --rm -it jetson-trt-llm

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🔥 4️⃣ Test the Edge AI LLM API

Once the container is running, test the API:

curl "http://localhost:8000/ask?question=What is Edge AI?"

🔹 Expected Output:

{"response": "Response from TensorRT model: What is Edge AI?"}

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📌 Conclusion

✅ Dockerized FastAPI agent running a TensorRT-optimized LLM on Jetson.
✅ Real-time, low-latency inference with NVIDIA TensorRT acceleration.
✅ Scalable Edge AI solution for private, offline GenAI models.