NVIDIA Jetson AGX Orin 64GB Developer Kit with Ethernet, USB, Display Port

Really good platform for running LLM's. Every AI runner is optimized for CUDA and Nvidia, and the performance is really good. Also, the setup is fairly easy; pretty much everything just works. The unified memory works really well and you can run it headless or with a decent UI. You just have to decide if you can't live without the performance of the AGX Spark.

Marketing teams are expected to move fast, personalize messages, and use data to make better decisions. Most AI tools push everything to the cloud, which causes delays, higher costs, and concerns about data privacy. If you work in marketing, this is frustrating. You have good data and strong ideas, but the tools don’t fit how your team actually works, and AI ends up feeling more complicated than helpful. The NVIDIA Jetson AGX Orin 64GB Developer Kit gave us a reliable way to run AI on our own systems. We can analyze data, generate insights, and support campaigns locally, without depending on outside services or slow connections. Instead of testing AI in small experiments, we now use it as part of our daily marketing workflow. It supports planning, content, and analysis in a consistent and secure way. Our marketing efforts are faster, more organized, and more effective. This platform helped us turn AI into a practical tool we trust, not just another piece of technology to manage.

nice choice, it's a good one

Item arrived exactly as described, brand new, factory sealed. Packaging was intact and the unit powered on without issues. Initial setup is straightforward: easy to connect to a monitor, keyboard, and mouse, and it boots immediately. That said, be aware this is older device. It ships with firmware and software last updated around 2023, which is expected, but important to know given how fast the AI ecosystem moves. To use it with modern AI frameworks, you’ll need to update the iGPU firmware, install a newer Jetson Linux release, and rebuild/install updated drivers and libraries. This isn’t plug-and-play for current AI workloads, but if you’re comfortable with Linux and system setup, it’s a great platform. I documented my full update and setup process in a Medium post for anyone interested.

I am revising my original review listed below and downgrading my review to one star. NVIDIA gas finally made available a version of their software that ships with Ubuntu 22, but it is not stable. After executing "apt upgrade" video drivers for the Orin lock up the Orin on reboots, making it unusable. I re-imaged my Orin twice and had the same issue. This is not a new problem for NVIDIA, as I observed the forums for th Oring. A company of NVIDIA's stature should be able to ship more reliable software for their hardware. There are far better options for $2K to the Orin, don't waster your money. Original review ------------------ Most reviewers of hardware drool the Orin hardware specs, and this device has impressive specs. Impressive hardware specs do not matter if there is limited support for the libraries. The NVIDIA Orin is frozen in time in terms of the most important libraries. For example, I use Python 3.11 for most things, but the only version that ships with this product is 3.8. NVIDIA provides versions of Python libraries, like Pytorch, that are needed to take advantage of the Orin GPU, but if you are stuck on Python 3.8, you will be limited in your ability to perform many if not most AI programming tasks. To that end, for support of Large Language Models (LLMs), you are better off running an Intel processor based machine with a GPU board. From a cost standpoint this is certainly cheaper. I realize this device is oriented towards image processing in support of IoT or Robotics projects. If you are processing images from an array of cameras, the Orin is a decent solution. As a platform for running local LLMs look elsewhere. Lastly, buyers need to be aware that if this Orin is to be re-flashed, or re-imaged, you really need to have an Intel based Linux machine, running Ubuntu 20!. I upgraded my Orin using the utility provided for this purpose. It is not easy and something only the most experienced pros can handle, IMHO.

Excelent for prototyping AI projects.Easy to install the sdk and begin working !

Good price

The smaller, cheaper models are more supported by nVidia, than this expensive one. It doesn't support the latest Ubuntu, like the nano, which means most things just don't work. The example containers they give for testing also fail. Even after modifying the sample docker configuration to be valid syntax with the version of the tools provided, the test containers fail to run. After several dozen hours of setup, I haven't been able to get a single llm to run on this, yet... Which is the primary purpose of purchasing the large memory.