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to manage multiple video pipelines efficiently. By running inference locally, the systems avoid cloud dependency, reduce latency and improve data privacy. In practice, this results in scaleable and cost-effective traffic solutions that improve road safety and operational efficiency.
Smart agriculture with NVIDIA Jetson Orin Agricultural AI systems often operate in remote locations with limited connectivity. Decisions such as disease detection and crop treatment must be taken directly in the fi eld, making edge inference essential. Aetina’s AIE-KN32-S2 and AIE-KN42-S2 systems, based on NVIDIA Jetson Orin NX, are designed for these scenarios. Jetson Orin NX delivers up to 157TOPS with the newer Jetson Orin NX models and optimised confi gurations using Super Mode, this performance can be pushed further while maintaining a compact footprint. In AI-powered plant health monitoring deployments, cameras
capture detailed images of crops, and deep learning models analyse leaf texture, colour and patterns to detect diseases early. Models trained using PyTorch are optimised with TensorRT before deployment, enabling real-time inference on site. This approach eliminates the need for laboratory testing and allows faster intervention, improving yield and reducing waste.
Compact intelligence with NVIDIA Jetson Orin Nano with Super Mode Not every edge AI application requires high compute density. In smart buildings, access control and localised analytics, system size, power consumption and cost are often the primary constraints. Aetina’s AIB-SO31, powered by NVIDIA Jetson Orin Nano,
targets these applications. Jetson Orin Nano provides up to 67TOPS, making it suitable for entry-level vision tasks such as people counting, basic object detection and fl ow analysis. With the introduction of NVIDIA JetPack 6.2, Jetson Orin Nano can operate with Super Mode, unlocking additional performance without hardware changes. This allows developers to deploy slightly more demanding models or increase input resolution while maintaining low power consumption. The same CUDA and TensorRT stack used on higher-end systems ensures software consistency across platforms.
Industrial inspection and machine vision In industrial inspection, AI systems must handle real-time data processing while maintaining robustness against vibration, dust and temperature variations. Latency directly impacts production throughput and quality control. Aetina deploys Jetson AGX Orin-based systems for
advanced visual and ultrasonic inspection, where AI models analyse sensor data continuously to detect defects and anomalies. CUDA accelerates data processing pipelines, while TensorRT ensures deterministic inference performance. Processing data locally improves responsiveness and
reduces dependency on centralised infrastructure, which is often impractical in industrial environments.
Preparing for the next step with Jetson Thor While most current deployments rely on Jetson Orin Nano, Orin NX and AGX Orin, Aetina is actively preparing for the next generation of edge AI systems with NVIDIA Jetson Thor models AIB-AT78/AT68, scheduled to launch in March. NVIDIA Jetson Thor is designed for applications that exceed
today’s edge AI requirements, including autonomous machines, advanced robotics and large-scale sensor fusion systems. With AI performance reaching up to 2,070 FP4 TFLOPS class, a metric aligned with modern transformer-based AI models and generative workloads, higher memory bandwidth and high- speed networking support, Jetson Thor enables systems that combine perception, planning and control in real time. For Aetina, NVIDIA Jetson Thor represents a natural
extension of the DeviceEdge portfolio. Experience gained from deploying NVIDIA Jetson Orin powered systems directly translates into Jetson Thor powered designs, providing customers with a scaleable path toward higher autonomy.
Software continuity across the portfolio Across all Aetina edge AI devices, software continuity is a key advantage. CUDA, TensorRT, DeepStream and mainstream frameworks such as PyTorch and TensorFlow form a consistent stack from NVIDIA Jetson Orin Nano to Jetson AGX Orin and Jetson Thor. This allows developers to prototype on entry- level systems and scale to higher-performance platforms with minimal rework. Combined with Aetina’s industrial-grade hardware design and long-term support, this approach reduces deployment risk and accelerates time to market.
Deployment tips • For deep learning training and model development, use NVIDIA Jetson AGX Orin with full JetPack and CUDA support.
• For low latency and real-time inference, always optimise models using TensorRT.
• For low power and cost-sensitive deployments, Jetson Orin Nano is a strong entry point, especially with Super Mode enabled via JetPack 6.2.
Turning platforms into real solutions The value of edge AI is measured in deployment success, not benchmark numbers. By focusing on complete systems rather than standalone modules, Aetina bridges the gap between NVIDIA Jetson platforms and real-world applications. From traffic infrastructure and agriculture to smart buildings and industrial inspection, Aetina’s edge AI devices demonstrate how Jetson technology can be transformed into reliable, scaleable solutions. With Jetson Thor on the horizon, this approach is set to support the next wave of autonomous and intelligent systems, where edge AI moves from supporting decisions to making them.
www.aetina.com
www.electronicsworld.co.uk February 2026 07
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