The NVIDIA Jetson Orin Nano is a compelling upgrade for anyone serious about edge AI and robotics. Offering a significant performance boost over its predecessor, it strikes a strong balance between power, size, and price. While it’s not without quirks—such as occasional hiccups with very large models and limited onboard encoding—it remains one of the best compact AI developer kits on the market today. At $250, it’s an investment aimed at developers, engineers, and creators who need real-time AI processing at the edge without relying on cloud resources.
**Best for:** AI developers, robotics engineers, and edge computing enthusiasts
**Rating:** 8.5/10
**Price:** Around $250
—
What is NVIDIA Jetson Orin Nano?
The NVIDIA Jetson Orin Nano is a small yet powerful AI computer designed specifically for edge applications like robotics, smart cameras, and automation systems. It’s part of NVIDIA’s Jetson family, which has long been a go-to platform for embedded AI development. The Orin Nano succeeds the popular Jetson Nano, boasting nearly 80 times the AI performance, thanks to its Ampere GPU architecture and upgraded CPU.
Unlike traditional AI setups that rely on cloud servers, the Orin Nano enables developers to run complex deep learning models locally, reducing latency and improving privacy. This makes it ideal for real-world applications where real-time decision-making is critical—think autonomous drones, factory robots, or smart city sensors.
—
Key Features
| Feature | Description |
|—————|————————————————–|
| CPU | 6-core ARM Cortex-A78AE 64-bit processor |
| GPU | NVIDIA Ampere architecture with 1024 CUDA cores |
| RAM | 8 GB LPDDR5 memory |
| Storage | NVMe SSD support (via M.2 slot) |
| Connectivity | Gigabit Ethernet, Wi-Fi (via module), USB 3.1 |
| Video I/O | HDMI 2.1, DisplayPort, MIPI CSI camera inputs |
| Power | Efficient 15W power envelope with quiet cooling |
| OS | Linux-based NVIDIA JetPack SDK |
—
Detailed Review
Design & Build Quality
The Jetson Orin Nano retains the compact form factor that made the original Nano popular, but with a more refined and robust build. Its small footprint (roughly the size of a credit card) makes it a natural fit for embedded projects where space is limited. The aluminum heatsink and quiet fan assembly do a great job of managing heat without generating distracting noise—a crucial factor for robotics or embedded systems running in close proximity to humans.
Connectivity options are plentiful: HDMI and DisplayPort outputs for video, multiple USB 3.1 ports, a Gigabit Ethernet jack, and a full set of GPIO pins for sensors and actuators. The inclusion of MIPI CSI camera interfaces is particularly useful for vision-based AI projects. The device feels solid and thoughtfully designed, though it’s worth noting that some models don’t include onboard Wi-Fi, requiring an external module.
Performance
Performance is where the Orin Nano really shines. Powered by a 6-core ARM Cortex-A78AE CPU and a 1024-core Ampere GPU, it delivers a substantial leap over the original Jetson Nano’s Maxwell GPU. In real-world tests, the Orin Nano comfortably handles AI workloads such as object detection, speech recognition, and even running medium-sized transformer models for natural language processing.
For example, I tested it running NVIDIA’s DeepStream SDK for video analytics, and it processed multiple 1080p video streams with minimal lag. For robotics applications, the device’s low latency and high throughput make it well-suited for real-time sensor fusion and control loops.
That said, when pushing very large models—think GPT-style language models with billions of parameters—the Orin Nano can struggle, occasionally freezing or requiring a reboot. This is expected given its edge-focused design and 8GB RAM limit, but it’s important to set realistic expectations.
Features & Functionality
The Orin Nano supports NVMe SSDs via an M.2 slot, which is a welcome addition for developers who need fast, expandable storage for large AI models and datasets. The device’s power efficiency is impressive, consuming just 15W under load, which translates to less heat and longer battery life in portable setups.
The JetPack SDK, NVIDIA’s Linux-based OS tailored for Jetson devices, offers an excellent development environment. It comes preloaded with AI frameworks like TensorFlow, PyTorch, and NVIDIA’s own TensorRT for optimized inference. This software stack is mature, well-documented, and supported by an active developer community, making onboarding smoother for newcomers and veterans alike.
One minor downside is the lack of onboard video encoding hardware in some versions, which can be a limitation for projects requiring video streaming or recording.
User Experience
Setting up the Orin Nano is straightforward, especially if you’re familiar with Linux and embedded systems. The JetPack installer guides you through flashing the OS image and installing necessary drivers. Once running, the system feels responsive and stable for most AI workloads.
In daily use, the quiet fan and efficient cooling mean the device can be deployed in noise-sensitive environments without issue. GPIO pin headers and camera interfaces are well-documented, making hardware integration relatively painless.
However, beginners may encounter a learning curve when configuring network connectivity or peripheral devices, especially since Wi-Fi is not always built in. The community forums and NVIDIA’s documentation are invaluable resources here.
—
NVIDIA Jetson Orin Nano vs Competitors
| Feature | NVIDIA Jetson Orin Nano | Raspberry Pi 4 Model B | Google Coral Dev Board |
|———————|————————————-|————————————|———————————-|
| CPU | 6-core ARM Cortex-A78AE 64-bit | Quad-core Cortex-A72 1.5 GHz | Quad-core Cortex-A53 1.5 GHz |
| GPU | 1024-core NVIDIA Ampere | Broadcom VideoCore VI | Edge TPU (specialized AI chip) |
| RAM | 8 GB LPDDR5 | 4 GB LPDDR4 | 4 GB LPDDR4 |
| Storage | NVMe SSD support | microSD card | 8 GB eMMC |
| AI Performance | ~40 TOPS (INT8) | Limited (no dedicated AI hardware) | 4 TOPS (Edge TPU) |
| Connectivity | Gigabit Ethernet, USB 3.1, MIPI CSI | Gigabit Ethernet, USB 3.0, Wi-Fi | Gigabit Ethernet, USB 3.0, Wi-Fi |
| Price | ~$250 | ~$35 | ~$150 |
**Comparison Notes:**
– The Raspberry Pi 4 is a versatile single-board computer but lacks dedicated AI acceleration, limiting its performance for demanding AI workloads.
– The Google Coral Dev Board offers specialized AI acceleration with its Edge TPU, excelling at inferencing but less flexible for general-purpose AI tasks.
– The Orin Nano sits between these options, delivering a powerful GPU-based AI platform capable of running a wide variety of models, making it more versatile for complex AI and robotics projects.
—
Pros & Cons
Pros
– **Significant performance boost** over previous Jetson Nano models
– **Compact, lightweight, and robust build** ideal for embedded and robotics projects
– **Supports NVMe SSDs** for fast, expandable storage
– **Quiet, efficient cooling** suitable for noise-sensitive environments
– **Comprehensive JetPack SDK** with popular AI frameworks and tools
– **Versatile connectivity** including multiple video and camera interfaces
Cons
– **Limited onboard video encoding** on some models, which may impact video streaming projects
– **Can struggle with very large AI models** due to RAM and thermal constraints
– **Wi-Fi not always included onboard**, requiring additional modules
– **Higher price point** compared to entry-level SBCs like Raspberry Pi
—
Who Should Buy NVIDIA Jetson Orin Nano?
If you’re a developer or engineer working on AI-powered robotics, smart cameras, or edge computing projects that demand real-time inference, the Jetson Orin Nano is an excellent choice. It’s particularly well-suited for those building computer vision applications, autonomous machines, or smart automation systems where latency and privacy are critical.
It’s also a great platform for students and makers looking to deepen their skills in applied AI and robotics, thanks to NVIDIA’s rich software ecosystem and community support.
However, if your projects require heavy video encoding, extremely large AI models, or you’re on a tight budget, you might want to explore other options or wait for future iterations.
—
Final Verdict
The NVIDIA Jetson Orin Nano is a standout in the embedded AI space. It offers a rare combination of compact size, powerful AI performance, and developer-friendly software. While it’s not perfect—some limitations in encoding and occasional freezes with massive models exist—it’s a robust platform that will serve most edge AI and robotics projects admirably.
For anyone serious about pushing AI to the edge, the Orin Nano is worth the investment. It bridges the gap between hobbyist boards and enterprise-grade AI hardware, delivering professional-level performance in a small package.
—