RidgeRun Helps Accelerate Edge AI Development on NVIDIA Jetson Orin Nano 2

RidgeRun today announced its accelerating edge AI development on NVIDIA Jetson Orin Nano 2, a new computer for entry-level Edge AI applications on compact, power-efficient embedded platforms.

For engineering teams developing on NVIDIA Jetson, production deployment typically involves more than AI inference alone. Camera and sensor integration, multimedia pipelines, hardware-accelerated video processing and encoding, low-latency streaming, and the coordination of AI inference with real-time video workflows are often key components of the overall system architecture. RidgeRun works across these areas as part of its engineering activities on Jetson-based platforms.

NVIDIA Jetson Orin Nano 2 provides 78 TOPS of AI compute, 8 GB of memory, and an 8-core Arm CPU. These specifications provide additional compute capacity for Edge AI workloads while retaining the compact form factor associated with embedded deployments.

A New Performance Baseline for Entry-Level Edge AI

NVIDIA Jetson Orin Nano 2 delivers 2x the inference performance of Jetson Orin Nano Super, supported by improved Tensor Cores and higher memory bandwidth, while maintaining the same compact form factor and a 40 W power envelope.

It also consumes 40% less power at equivalent performance. For embedded systems operating under strict power, thermal, and physical constraints, that efficiency can create greater flexibility when designing deployable AI products.

Performance, however, is only part of the story. The value of an edge platform depends on how effectively its compute, video, sensor, and software capabilities can be integrated into a reliable end-to-end system.

Turning NVIDIA Jetson Compute into Complete Edge Systems 

Edge AI products do more than run models. Intelligent cameras, autonomous machines, robots, delivery and inspection drones, and next-generation vision systems combine sensors, cameras, video pipelines, inference, metadata, networking, and application logic. These components must operate together reliably and in real time.

On the NVIDIA Jetson platform, RidgeRun helps product teams:

• Integrate cameras and sensors into embedded systems.

• Bring up custom hardware BSP.

• Build and optimize GStreamer pipelines for demanding multimedia workloads.

• Enable hardware-accelerated video processing, encoding, and streaming.

• Design low-latency architectures for real-time applications.

• Connect AI inference with live video and metadata workflows.

With the additional performance available in NVIDIA Jetson Orin Nano 2, developers have more room to process multiple video streams, combine vision AI with intelligent decision-making, and support more sophisticated pipelines while preserving the compact footprint expected from embedded products.

Building the Next Generation of Edge AI

NVIDIA Jetson Orin Nano 2 represents more than an incremental increase in compute from Jetson Orin Nano. Higher inference performance, improved energy efficiency, stronger video processing capabilities, and NVIDIA's growing AI software ecosystem expand what developers can expect from an entry-level Edge AI platform.

For RidgeRun, this creates new opportunities to help teams build increasingly ambitious camera, multimedia, computer vision, robotics, and Edge AI applications on NVIDIA Jetson, while keeping the performance, efficiency, and integration requirements of production systems at the center of the design.

The NVIDIA Jetson Orin Nano 2 module and developer kit are planned for availability in the first half of 2027. As teams prepare future products, early architecture choices; including sensor support, pipeline design, hardware acceleration, AI-video integration, latency, memory use, and thermal behavior; will remain critical to moving successfully from prototype to deployment.

Planning a Jetson-based product?

Ready to build your next Jetson-based product? Contact RidgeRun to discuss your project and learn how our engineering team can help bring it from concept to deployment.