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NVIDIA

NVIDIA Jetson Orin Nano Super Developer Kit

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Arrives Jun 11 – Jun 13
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Features

  • The NVIDIA Jetson Orin Nano Developer Kit sets a new standard for creating entry-level AI-powered robots, smart drones, and intelligent cameras,and simplifies getting started with the Jetson Orin Nano series. Compact design, lots of connectors and up to 40 TOPS of AI performance make this developer kit perfect for transforming your visionary concepts into reality. With up to 80X the performance of Jetson Nano, it can run all modern AI models, including transformer and advanced robotics models.
  • The developer kit comprises a Jetson Orin Nano 8GB module and a reference carrier board that can accommodate all Orin Nano and Orin NX modules, providing an ideal platform for prototyping your next-gen edge AI product. The Jetson Orin Nano 8GB module features an Ampere GPU and a 6-core ARM CPU, enabling multiple concurrent AI application pipelines and high-performance inference. The carrier board boasts a wide array of connectors, including two MIPI CSI connectors supporting camera modules with up to 4-lanes, allowing higher resolution and frame rate than before.
  • Jetson runs the NVIDIA AI software stack, with available use-case-specific application frameworks, including NVIDIA Isaac for robotics, DeepStream for vision AI, and Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic data generation (SDG), and with NVIDIA TAO Toolkit for fine-tuning pretrained AI models from the NGC catalog.
  • Ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product.
  • Jetson Orin modules are unmatched in performance and efficiency for robots and other autonomous machines, and give you the flexibility to create the next generation of AI solutions with the latest NVIDIA technology. Together with the world-standard NVIDIA AI software stack and an ecosystem of services and products, your road to market has never been faster.

Description

The NVIDIA Jetson Orin™ Nano Super Developer Kit is a compact, yet powerful computer that redefines generative AI for small edge devices. It delivers up to 67 TOPS of AI performance—a 1.7X improvement over its predecessor—to seamlessly run all kinds of generative AI models, like vision transformers, large language models, vision-language models, and more. At just $249, it provides developers, students, and makers with the most affordable and accessible platform with the support of the NVIDIA AI software and a broad AI software ecosystem to democratize generative AI at the edge. Existing Jetson Orin Nano Developer Kit users can experience this performance boost with just a software upgrade, so everyone can now unlock new possibilities with generative AI. The developer kit comprises a Jetson Orin Nano 8GB module and a reference carrier board that can accommodate all Orin Nano and Orin NX modules, providing an ideal platform for prototyping your next-gen edge AI product. The Jetson Orin Nano 8GB module features an Ampere GPU and a 6-core ARM CPU, enabling multiple concurrent AI application pipelines and high- performance inference. The carrier board boasts a wide array of connectors, including two MIPI CSI connectors supporting camera modules with up to 4-lanes, allowing higher resolution and frame rate than before. Jetson runs the NVIDIA AI software stack, with available use-case-specific application frameworks, including NVIDIA Isaac™ for robotics, NVIDIA Metropolis™ for vision AI, and NVIDIA Holoscan™ for sensor processing. You can save significant time with NVIDIA Omniverse™ Replicator for synthetic data generation (SDG), and with NVIDIA TAO Toolkit for fine-tuning pretrained AI models from the NGC™ catalog. Ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product. Jetson Orin modules are unmatched in performance and efficiency for robots and other autonomous machines, and give you the flexibility to create the next generation of AI solutions with the latest NVIDIA technology. Together with the world-standard NVIDIA AI software stack and an ecosystem of services and products, your road to market has never been faster.

Brand: NVIDIA


Model Name: Jetson Orin Nano 8GB


Ram Memory Installed Size: 8 GB


Memory Storage Capacity: 8 GB


CPU Model: 6-core ARM Cortex-A78AE v8.2


RAM Memory Installed: 8 GB


Memory Storage Capacity: 8 GB


CPU Model: 6-core ARM Cortex-A78AE v8.2


Connectivity Technology: USB, DisplayPort, Ethernet, GPIO


Operating System: Linux


Processor Brand: ARM


Wireless Compability: Bluetooth


Compatible Devices: Various


RAM Memory Technology: LPDDR4X


Processor Count: 1


Total Usb Ports: 5


Smart Home Compatibility: Not Smart Home Compatible


Item Dimensions L x W x H: 6"L x 3"W x 8"H


Brand: NVIDIA


Model Name: Jetson Orin Nano 8GB


Built-In Media: Quick Start and Support Guide, Type B (US, JP) Power Cable, Type I (CN) Power Cable


UPC: 812674025261


Global Trade Identification Number: 61


Model Number: 945-137766-0000-000


Mfr Part Number: 945-137766-0000-000


Manufacturer: NVIDIA


Warranty Description: 1 year manufacturer


Unit Count: 1.0 Count


Item Weight: 1.7 Pounds


Frequently asked questions

If you place your order now, the estimated arrival date for this product is: Jun 11 – Jun 13

Yes, absolutely! You may return this product for a full refund within 30 days of receiving it.

To initiate a return, please visit our Returns Center.

View our full returns policy here.

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Top Amazon Reviews

    • Nice piece of hardware
Style: Developer Kit
It’s a 8GB shared GPU/CPU memory that can run quantized LLMs. The fan is not noisy and it run on Ubuntu 22.04 (you cannot update to 24.04+). Installation is a pain, but this was not my first rodeo. I have the latest lt4 drivers and CUDA 13.1 installed after doing some complex upgrades. Nothing is easy there, but the prepared docker containers are life savers. You can find containers set by functionality like Voice, LLM, Ollama, etc. ... show more
Reviewed in the United States on May 17, 2026 by R V
    • Excellent – Powerful, Fast, and Perfect for Advanced AI Projects
Style: Developer Kit
The NVIDIA Jetson Orin Nano Super Developer Kit exceeded every expectation. For its size, this thing delivers incredible performance — fast boot times, smooth CUDA acceleration, and outstanding handling of AI workloads. Running local LLMs, vision models, robotics stacks, and edge-compute pipelines feels effortless. The build quality is solid, setup is straightforward, and the system stays stable even under heavy loads. I’ve tested everything from PyTorch models to engineering diagnostics and it never struggles. For anyone working on edge AI, embedded systems, or real-time machine learning, this is an absolute powerhouse. Highly recommended if you want serious AI performance in a compact, efficient developer kit. This is hands-down one of the best edge-AI boards available right now. ... show more
Reviewed in the United States on November 15, 2025 by Picasso
    • Runs popular models, easy to set up
Style: Developer Kit
1. Impressive Capability: Easily runs heavy models like Meta's SAM and Google's Gemma. 2. Unified Memory Advantage: The shared 8GB CPU/GPU memory is a massive perk for efficiency. 3. Excellent Connectivity: Hassle-free Wi-Fi and a highly convenient USB-Ethernet option for direct laptop tethering. 4. Great Ecosystem Support: OpenAI Codex easily handles setup/HuggingFace scripts, and Nvidia provides excellent Docker documentation. 5. Other thoughts: a) This Jetson is an incredibly fun, compact Linux device. I was easily able to run advanced models like Meta’s Segment Anything Model (SAM) and Google’s Gemma right on the device. b) Getting started is fast. Nvidia provides extensive documentation for running models via downloadable Docker images, and OpenAI Codex knows exactly how to configure the environment to run HuggingFace models within Python scripts. c) I found the USB-Ethernet option particularly handy—it allowed me to connect my MacBook directly to the Jetson and log in via the terminal. If you want to squeeze even more performance out of it, I highly recommend disabling the local GUI entirely and operating solely through the terminal to free up maximum memory for your models. ... show more
Reviewed in the United States on May 30, 2026 by PFD
    • after this experience I won't be able to look at any nvidia product without gagging
Style: Developer Kit
what a waste of time, not worth my sanity. another day and I'd likely take a sledge hammer to it. nvidia software, their os, the sdk, the code examples (jetson lab), all of it is just absolute garbage. first, you must have real computer (vm won't do) with intel and ubuntu 22.04 just to flash the nvme. then you find out nothing works. first clue was their "readme" link they placed on the desktop "for my convience", which doesn't work, points to nothing. snap needs downgrading before you can run any program. then there are the ai software examples from their own lab. I wasted a week so far trying. only ollama native or container work. I can't make anything else work, and these are their own "tutorials" for this board. all I learned from those is to stay far away from nvidia. I don't believe any of that software, the os and the tutorials are tested or that they are maintaned. their support forums have nothing useful. none of the speech or image or vision tutorials work, all I get is errors, or no response. docker containers start, but nothing listens on the ports I'm supposed to browse to. a swap file is necessary to run anything because the os and nvidia crapware already use about 2-3 GB, leaving very little for models. performance is disapointing, the advertised 67 tops is a lie, marketing bs. in the "super" mode it throttles down immediatelly, actually it trottles down in all power modes. the fan does nothing because it defaults to quiet mode, and you must find a way to set it to allow it to do its job of actually cooling the chip. every step is a struggle, hours of trying, hundreds of gigabytes of wasted downloads. I bought this nvidia dev kit because of the hardware specs:, 1024 cudas, 32 tensors, 2 pcie slots, gpio, 2 csi cameras. but it's all useless without working software and drivers and documentation, and nvidia people have no clue how to code. I know nvidia since mid 90s, their video card drivers were always horrible. ... show more
Reviewed in the United States on September 12, 2025 by Chris B.
    • An absolute monster of a board! An absolute monster of a board!
Style: Developer Kit
Review Image by RPDevJesco
Review Image by RPDevJesco
First things first, this board is absolutely beautifully designed. The location of the SD Card and where you can add your NVMe drives make logical sense. It ships with factory firmware that requires an update before use. It is a bit of work to find the firmware update and is a rather large file that you will then need to flash onto an SD Card using BalenaEtcher, which is about 30 minutes of waiting depending on your download and cpu speeds. The UEFI bios is very well organized and structured and does have TPM 2.0. It does not have an OS installed by default, so you will need to install one via SD Card or NVMe slots. Which means you can use official Nvidia images or you can use custom ones. The official image is also a bit of a pain to find, but again, once you download it, you need to flash it onto an SD Card using BalenaEtcher. Your mileage may vary for how long this process will take. For me, it was around 10 minutes. The construction of this thing is super solid. Has a very solid base that the SBC connects to, the CPU is more of a Compute module setup so you could possibly change it for a newer MU unit later without needing a new base. The standard use case for a board like this is local LLM inference, my use case is currently getting my custom OS to boot on it and then move to local LLM inference later. ... show more
Reviewed in the United States on February 19, 2026 Reviewed in the United States on February 19, 2026 by RPDevJesco
    • It's a great board but the setup is not for the faint of heart It's a great board but the setup is not for the faint of heart
Style: Developer Kit
Review Image by Horace P.
I’ve been extremely impressed with the NVIDIA Jetson Nano Super Developer Kit 8GB. For anyone seriously interested in exploring local AI, edge inference, robotics, or embedded AI systems, this little board is an absolute hammer for the price and power envelope. Performance-wise, I was able to achieve over 20 tokens/sec running an 8B model locally, which honestly exceeded my expectations for hardware in this class. NVIDIA’s CUDA ecosystem, TensorRT support, and overall AI tooling make this platform feel much more capable than its size would suggest. It punches well above its weight. That said, I do want to give one honest caveat: setup can be challenging. I’ve configured two of these boards now, and both took nearly a full day of troubleshooting, flashing, configuring, and tuning before everything was stable and running correctly. This is definitely more of an engineer/developer platform than a consumer plug-and-play device. However, if you’re comfortable working through Linux setup, drivers, SDKs, containers, or AI frameworks, the payoff is absolutely worth it. Once configured properly, this thing becomes an incredibly capable local AI platform. Highly recommended for developers, makers, robotics enthusiasts, and anyone wanting to learn real edge AI without spending workstation-level money. ... show more
Reviewed in the United States on May 19, 2026 Reviewed in the United States on May 19, 2026 by Horace P.
    • NVIDIA Jetson Orin Nano Super Review
Style: Developer Kit
Good Products, Value fo rMoney
Reviewed in the United States on May 27, 2026 by jeffm
    • Powerful Hardware, but a Frustrating and Fragmented User Experience
Style: Developer Kit
The NVIDIA Jetson Orin Nano Super is undeniably a powerhouse on paper, offering impressive AI throughput for edge computing. However, after integrating this into my workflow for mobile ALPR and custom security development, I’ve found that the actual user experience is marred by several design choices and technical hurdles that make it far from a "plug-and-play" professional tool. Installation and Hardware Ergonomics The physical layout of the board leaves much to be desired. The SD card slot location is remarkably inconvenient, especially if you have the board mounted in a custom enclosure or near other hardware. Furthermore, the complexity of getting the system to boot and run reliably from an NVMe drive is far higher than it should be in 2026. For a developer kit that essentially requires NVMe for any serious work, this process should be streamlined and native, rather than a multi-step technical hurdle that feels like a workaround. Stability Issues The most frustrating aspect has been the repeated system lockups. I’ve experienced multiple freezes during standard operation with no immediate or obvious cause. When you are trying to benchmark AI models or test long-term stability for a vehicle-mounted deployment, having the hardware randomly hang is a dealbreaker. It undermines the confidence you need in a board intended for "industrial" or "super" applications. Documentation and Support Fragmentation Finding clear, concise information is an uphill battle. NVIDIA’s documentation is scattered across too many different models and JetPack versions, making it incredibly difficult to find specific answers for the Orin Nano Super. You often find yourself digging through forum posts and outdated wiki pages to solve basic configuration issues. For a "Super" edition product, the support ecosystem feels fragmented and disorganized. What I Like: Raw Compute: When it is actually running, the CUDA performance is excellent for localized inference. Form Factor: It packs a lot of power into a small footprint, which is ideal for mobile security builds. What Needs Improvement: UI/UX for Setup: The NVMe boot process needs to be modernized and simplified. Reliability: Firmware or kernel stability needs to be addressed to stop the random lockups. Consolidated Documentation: A single, authoritative source of truth for this specific hardware would save developers hours of wasted time. Final Thoughts I like the potential of this product, and the hardware specs are exactly what I need for my security startup's infrastructure. However, the execution "leaves some to be had." If you aren't prepared to spend significant time troubleshooting and navigating a labyrinth of documentation, you might find the "Super" experience more frustrating than it’s worth. It’s a powerful tool, but it currently feels like it’s still in beta. ... show more
Reviewed in the United States on April 28, 2026 by Matthew VanDruff

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