Nvidia has introduced the Jetson Orin Nano Super Developer Kit, a high-performance AI platform delivering a 70% boost in generative AI inference speed while doubling memory bandwidth compared to its predecessor.
Priced at $249, the kit offers developers, researchers, and students a cost-effective tool to build and deploy advanced AI applications at the edge.
The NVIDIA Jetson Orin Nano super Developer Kit is a compact edge AI board designed for creating entry-level AI-powered devices such as robots, drones, and intelligent cameras.
Its combination of improved hardware performance and backward-compatible software optimizations positions it as an attractive solution for robotics, computer vision, and real-time generative AI workloads—areas where computational power must meet energy and space constraints.
Jetson Orin Nano Super Developer Kit configuration comparison
| NVIDIA Jetson Orin Nano Developer Kit (original) | NVIDIA Jetson Orin Nano Super Developer Kit | |
| GPU | NVIDIA Ampere architecture 1,024 CUDA Cores 32 Tensor Cores 635 MHz | NVIDIA Ampere architecture 1,024 CUDA Cores 32 Tensor Cores 1,020 MHz |
| AI PERF | 40 INT8 TOPS (Sparse) 20 INT8 TOPS (Dense) 10 FP16 TFLOPs | 67 TOPS (Sparse) 33 TOPS (Dense) 17 FP16 TFLOPs |
| CPU | 6-core Arm Cortex-A78AE v8.2 64-bit CPU 1.5 GHz | 6-core Arm Cortex-A78AE v8.2 64-bit CPU 1.7 GHz |
| Memory | 8GB 128-bit LPDDR5 68 GB/s | 8GB 128-bit LPDDR5 102 GB/s |
| MODULE POWER | 7W | 15W | 7W | 15W | 25W |
Affordable AI Performance Gains for Edge Computing
The Jetson Orin Nano Super builds on Nvidia’s Ampere GPU architecture, which has been a cornerstone of its AI offerings across data centers, cloud, and now edge computing. The developer kit features the Jetson Orin Nano 8GB system-on-module (SoM) and brings enhanced efficiency for tasks like transformer-based models, language models, and robotics simulations.
Nvidia reports a 1.7x increase in generative AI inference performance, made possible by hardware upgrades and software optimization. Notably, the new system achieves 67 TOPS (trillions of operations per second) sparse compute performance—up from 40 TOPS in the previous Orin Nano series—and increases memory bandwidth to 102GB/s, a significant leap over the earlier 68GB/s.
According to Nvidia, the developer kit retains the same compact form factor while delivering greater efficiency. Deepu Talla, Nvidia’s Vice President of Embedded and Edge Computing, noted the upgrade’s impact, saying, “It’s like we’ve taken Orin Nano and given it a superhero cape.”
These improvements empower developers to handle AI tasks previously limited to cloud systems, such as running compact large language models (LLMs) like Llama-3.1 and Gemma-2. By enabling smaller, optimized models to run locally, the Jetson Orin Nano Super reduces reliance on cloud resources and latency—critical for robotics, autonomous machines, and other real-time applications.
Software Updates Extend Performance Gains to Existing Devices
Alongside the hardware release, Nvidia introduced a JetPack SDK update that benefits the entire Jetson Orin family, including older Orin Nano and NX models. This update unlocks Super Mode, a higher-power setting that boosts GPU and CPU clock frequencies to improve performance.
Developers can activate Super Mode using Nvidia’s Power Mode Selector tool, accessible via the command line or graphical interface. With this feature, existing hardware can achieve performance gains similar to the Jetson Orin Nano Super without additional investment.
By ensuring software parity across its Orin lineup, Nvidia maximizes the lifecycle and value of its edge AI platforms, making it easier for developers to scale their projects.
Real-World Applications: Robotics, Vision Models, and Generative AI
Nvidia’s Jetson Orin Nano Super is purpose-built for edge applications requiring efficient AI processing. Key areas of focus include robotics, computer vision, and generative AI:
In robotics, Nvidia’s Isaac platform offers simulation tools and synthetic data generation to accelerate development. For instance, Isaac Sim enables developers to prototype robotic systems in virtual environments before deploying them in the real world. Similarly, Nvidia’s Omniverse Replicator facilitates the creation of high-quality synthetic data for training AI models.
For computer vision, the Jetson Orin Nano Super supports transformer-based models like Meta’s DINOv2 self-supervised vision transformer model and CLIP, which deliver improved accuracy for image recognition, classification, and object detection tasks.
These models enable systems to analyze high-resolution images and video streams with greater efficiency, making the device ideal for applications such as automated quality control, surveillance, and autonomous navigation.
Generative AI workloads also benefit from the platform’s capabilities. By running compact LLMs locally—such as Llama-3.1 8B or Google’s Gemma-2 — developers can implement applications like retrieval-augmented generation (RAG) chatbots or real-time content summarization.
The combination of low latency and reduced power consumption makes the Jetson Orin Nano Super well-suited for edge environments where bandwidth and cloud access are limited.
Developer Ecosystem and Long-Term Support
Nvidia says it is committed to supporting the Jetson community through comprehensive resources and tools. The Jetson AI Lab provides developers with prebuilt containers, deployment guides, and tutorials for implementing AI models. Examples include integrating Ollama for chatbot deployment and leveraging frameworks from Hugging Face, Google, Microsoft, and Meta.
The developer kit also features support for up to four cameras, enabling multi-stream processing for vision-based tasks—critical for robotics and autonomous systems.
To ensure long-term stability, Nvidia has extended the Jetson Orin product lifecycle through 2032, giving developers and businesses confidence in the platform’s availability for future deployments.
A Step Forward for Edge AI
With the Jetson Orin Nano Super, Nvidia combines improved AI performance with affordability, addressing the growing need for real-time, on-device computing. The platform’s ability to run compact generative AI models, process high-resolution images, and accelerate robotics development makes it an essential tool for developers working on edge solutions.
By offering hardware improvements alongside software optimizations that apply to existing devices, Nvidia has created a robust ecosystem for edge AI development.
