Optimizing and Deploying AI Models on NVIDIA Jetson

You launched YOLOv8 on Jetson Nano and got 5 FPS instead of the expected 30. Typical situation: a model not adapted for edge hardware wastes resources. Optimization via TensorRT gives 3–10x speedup, and DeepStream squeezes the maximum out of the video stream. We have deployed CV models and LLMs on Jetson AGX for industrial robots — here’s how we do it.

The problem is not the hardware itself: Jetson Orin is a powerful edge computer, but without proper optimization you hit memory bandwidth limits and inefficient kernel calls. For example, YOLOv8n on PyTorch with FP32 consumes 8 GB RAM and delivers 12 ms per frame — whereas TensorRT with INT8 reduces latency to 3 ms and RAM drops to 2 GB. Result: 30 FPS on the same camera.

Jetson Model Lineup (Current)

How TensorRT Accelerates Models on Jetson

TensorRT compiles ONNX/PyTorch models for the specific Jetson GPU. The conversion process:

1. Export to ONNX with fixed dynamic axes (batch, height, width).
2. Build an engine via trtexec with selected precision (FP16 by default, INT8 for maximum).
3. Calibrate INT8 on a representative dataset (at least 500 images) to preserve mAP.
4. Integrate via C++ API or Python bindings.

import tensorrt as trt # or via trtexec: # trtexec --onnx=model.onnx --saveEngine=model.trt --fp16 

Typical speedup: 3–10x vs. PyTorch. For ResNet-50 on Orin AGX we got 7x, for YOLOv8 — 5x. We guarantee p99 latency within specification: e.g., YOLOv8 on Orin NX in INT8 — 12 ms per frame.

DeepStream for Video Analytics

NVIDIA DeepStream SDK is an optimized pipeline for multi-camera analytics. The GStreamer-based pipeline provides batch inference, scaling, tracker and output to RTSP or Kafka. Typical performance on Orin AGX: 30+ Full HD cameras with YOLOv8 detection. We also configure primary processing (NvStreamMux, nvdrmvideosink) and integration with ROS2.

Why Choose Orin AGX for Complex Tasks?

Orin AGX delivers 275 TOPS and 64 GB RAM — enough to run Llama 3 8B (4-bit) with context window 8192, RAG with ChromaDB, and parallel inference of 8 CV models. Compared to Orin Nano: AGX is 14x faster in FLOPS, but for a simple single-camera detector the Nano is a budget solution. Our experience: for autonomous robots we always take AGX, for stationary inspection — NX.

RAG on Jetson

We use ollama or llama.cpp for LLM inference, ChromaDB for vector search — everything fits in 16 GB of Orin NX. Typical pipeline: sentence-transformers for 768‑dim embeddings, faiss for indexing, langchain for the calling chain. Response latency: 500 ms per query with 2k token context on Orin AGX.

ROS2 + Jetson

Robotics: ROS2 Humble is natively supported on JetPack 5/6. Isaac ROS provides NVIDIA‑optimized ROS2 packages for computer vision. We integrated Isaac ROS with a custom detector — 60 FPS on Orin NX for two cameras.

What’s Included

• Model conversion to TensorRT/ONNX Runtime
• DeepStream or Triton Inference Server setup
• Peripheral integration: cameras (GMSL/USB), sensors, GPIO
• Performance testing: latency, throughput, power
• CI/CD pipeline for model updates
• Documentation and team training
• Guarantee on specified metrics (p99, FPS)

Estimated Timeline: 2 to 8 Weeks

The cost is calculated individually — depends on model complexity, number of cameras, and latency requirements. We'll evaluate your project in one day. Get a consultation — our engineers will show case studies and select the optimal solution.

Our experience: 5 years in edge AI market, 40+ projects for industry and logistics. NVIDIA certified engineers — we guarantee results.