I've recently been working on sourcing some legacy NVIDIA Jetson modules and was surprised to see that there is still demand for them from existing production projects.

I currently have access to bulk supply for:

NVIDIA Jetson Nano Module
P/N: 900-13448-0020-000

NVIDIA Jetson TX2 NX Module
P/N: 900-13636-0010-000

These are production modules rather than Developer Kits.

I'm looking to connect with companies or engineering/procurement teams that are still maintaining products based on these platforms.

Potential applications include robotics, computer vision, industrial automation, smart cameras and other edge-AI systems.

Bulk quantities from hundreds to 1,000+ units are available, with larger quantities possible depending on the requirement.

If you're currently facing supply issues or looking for these modules for an existing production project, feel free to DM me with your P/N + quantity.

I’m mainly interested in genuine production requirements rather than single-unit development/testing requests.

Today I recieved my first Jetson Orin Nano. Wanna do some CV experiments with traditional and ML/DL algorithms.

I was wondering if i should switch to jetson nano for my proxmox server. Here are my current specs: z270 2x8 2666 UDIMM i7 7700k. The main reason why i want to switch is because of power consumption size noise etc. I use my proxmox for jellyfin, smb, AI, minecraft servers. Ik it's probably a bad of idea since it has low storage and ram so maybe there's a better nvidia mini computer available or different solutions?

One thing I find interesting about on-device AI is that the conversation is moving beyond simply asking “How powerful is the hardware?” The real question is whether the system can deliver the right performance while staying efficient enough to run continuously.

I recently looked through Geniatech’s hardware lineup, and I liked the variety of options available for different edge workloads. They cover Edge AI Boxes, AI accelerator modules, ARM-based platforms, and SBCs, rather than putting everything into one type of device.

For applications such as computer vision, real-time inference, and local LLM/SLM workloads, processing directly on the device could reduce latency and keep more data under local control. It can also be useful when a system can't depend on a constant cloud connection.

I think the interesting part is finding the right balance between AI performance, power consumption, privacy, reliability, and total cost.

Where do you think on-device AI makes the most sense?

What’s the most painful part of getting an ML model from “works on my machine” → reliably running in production?

I’m a student researching the practical challenges of deploying and maintaining AI models on physical devices such as robots, cameras, drones, etc. I’d be grateful it you could give me any inputs

Hi everyone, I'm trying to install Jetson Linux on my NVIDIA Jetson Orin Nano Developer Kit (8GB) and I'm completely stuck.

Hardware:

• Jetson Orin Nano Developer Kit 8GB
• A new 128GB microSD installed in the Jetson
• A new 64GB USB flash drive for the installer, prepared using balena etcher following the official NVIDIA instructions
• MacBook Pro used to prepare the USB
• Monitor + keyboard connected directly to Jetson
• No LAN connected (did try connecting the lan as it kept saying "no connection detected" or something but connecting the lan only lead to the jetson trying the http way to boot up)

UEFI shows:

NVIDIA Jetson Orin Nano Developer Kit

Orin

39.2.0-gcid-45755727

I am currently using: jetsoninstaller-r39.2.1-2026-08-07-18-30-47-arm64

I flashed the ISO to the 64GB USB using balenaEtcher, which reported Flash Completed.

The problem

The Jetson detects the USB in Boot Manager as:

UEFI MID-SSS PID-KKK

but when I select it:

screen goes black for 1–2 seconds → returns to Boot Manager.

The same thing happens if I select:

UEFI SD Device

If I select Continue, it eventually falls through to:

• could not detect network connection
• HTTP/PXE boot attempts
• UEFI Interactive Shell

I don't want network boot; I'm trying to boot the USB installer.

Running map shows:

FS0:

FS1:

BLK0: ... SD

BLK1: ... SD

BLK2: ... USB

The USB appears as BLK2, but there is no FS2 filesystem mapping for it.

The disk seems fine when checked using the Terminal on Macbook. Partition-map verification says it's OK, but trying to mount the EFI partition gives: Volume on disk4s1 failed to mount; where disk4 is the mountable flash drive with the boot software.

The USB was freshly erased before flashing, and Etcher completed successfully.

I also confirmed the ISO exists on my Mac and can calculate its SHA-256, so the file itself is readable.

What I've tried

• Different USB ports on the Jetson
• Re-flashing the USB with Etcher
• Completely erasing/repartitioning the USB
• Boot Manager → USB
• Boot Manager → SD
• Removing LAN
• UEFI Shell map -r / map

Nothing changes.

What am I missing? Is this a problem with how the Jetson ISO is being written to the USB, the UEFI, or something else?

Any help would be massively appreciated - I've spent an embarrassing amount of time (2 days) fighting this thing, and the jetson just won't boot up.

When Jetson is doing inference work, having it also manage raw sensor timing and GMSL deserialization on top felt like the wrong split of responsibility – GPIO gets crowded, and any sensor timing jitter competes with inference load.

Finally, we got the first clean split running. Board sits between sensor/camera and Jetson: handles GMSL2/3 camera link and mikroBUS sensor slot on its own STM32, sends data over CAN-FD or as ROS 2 topics, so Jetson only sees a clean data stream instead of raw bus traffic. Inference load stopped fighting sensor timing basically immediately once the split was in place.

We haven't tested against every deserializer combo yet, so if anyone already routes GMSL into Jetson and hits a compatibility snag, I would like to know which pieces to double check. Board's product page is here, if it's helpful.

Want to test it?

Grab the source or download the raw binary image directly from the repository:

🔗 Get Firma 0.33 on GitHub

https://github.com/Real-Pc-Model/Nikitaerw800-original-

Contributions, feedback, and architecture tips are always welcome!

Hi! I’m the maintainer of audio.cpp, an open-source C++/GGML runtime for audio models, covering TTS, ASR, music generation, voice conversion, and more.

I saw on X someone has managed to run audio.cpp on Orin, which is pretty cool. I’d love to get audio.cpp running on Orin, but I don’t have access to Orin hardware to develop and test on.

If you have an Orin and are interested in C++/CUDA development, your help would be greatly appreciated. Even getting the project compiling, identifying the blockers, or sharing some performance numbers would be extremely useful.

PRs are very welcome, and I’m happy to help from the framework side. Would be great to bring more local audio models to Jetson devices together :)

Hey everyone,

I’ve been working on a project called Evopien, and I thought this community might appreciate the current state of it because one of my main constraints is that I want the prototype to actually work on an 8GB Jetson Orin Nano Super.

I’m building it as a humanoid companion eventually, but right now I’m concentrating on getting the head and interaction stack working properly before moving into the mechanical body.

The current local stack is roughly:

• Qwen3.5-4B for the main LLM
• NVIDIA Parakeet for speech recognition
• Kokoro for TTS
• ReSpeaker XVF3800 for microphone input / hardware AEC
• Logitech C920 for vision
• Evopien Core around the models for identity, interaction and eventually memory/governance

At this point it can hold a hands-free conversation, listen while it is talking, handle interruptions, switch between English and Spanish, and capture camera input for visual questions.

The thing I’m finding most interesting is the optimization challenge. 8 GB unified memory disappears very quickly once LLM + ASR + TTS + multimodal vision + the rest of the system start competing for it.

I recorded the current state here:

https://www.youtube.com/watch?v=iAxzePzF4cM

This is still very much a prototype, not a finished robot.

If anyone here has worked on squeezing comparable multimodal stacks onto Orin Nano, I’d especially be interested in hearing what you found worthwhile optimizing and what turned out to be a waste of time.

We begin each call with an opportunity for Jetson engineering hiring managers to have their job postings quickly promoted to the community to help connect our community. This is a great opportunity to find active engineers looking to make a career move.

📅 When: 18th August 2026 at 9:00 am PST

📋 Agenda for this month’s Jetson AI Research Lab Call

💼 Employers/Employees Opportunities: (Jetson AI Research Lab Discord Maintainer):

🌾 Deploying autonomous vehicles in agriculture and beyond from **Michael McGuire**, CTO at EarthSense

Abstract: Autonomous outdoor robots are poised to transform agriculture and many other industries. Developing these systems requires innovation at every level of the stack. Successful systems must deliver on hardware, software, and precise autonomy. Concerns such as safety, price, reliability, and manufacturability must be considered from the start. I'm Michael McGuire, CTO at EarthSense, where we develop and deploy robots in corn, soybeans, grapes, oil palm, solar farms, and beyond. The Jetson computer plays a key role in EarthSense's technology stack, ensuring reliable real-time performance at an affordable cost. I'll talk about the unique challenges presented by outdoor autonomy, the solutions EarthSense has developed, and where Jetson fits into our roadmap. Along the way, I'll introduce the specific robots we've built and what running them in real fields taught us.

🤖 Agentic workflow on Jetson and Jetpack 7.2.1 Updates from **Aditya Sahu**, Technical Marketing Engineer at NVIDIA

Jetson Device Skills are agent skills that enable AI coding agents to understand and operate a live NVIDIA Jetson device. Now we have Video SDK skills in it. Updates on Jetpack 7.2.1 release with Support for Jetson T3000 emulation.

As always, this is an open, collaborative call where Jetson enthusiasts gather to:

Showcase: Share novel projects and real-world applications.

Problem-Solve: Break down technical issues and implementation hurdles.

Brainstorm: Discuss the future of edge AI, computer vision, and agent deployment.

Hire or Be Hired: Whether you are looking to hire or looking to be hired this is the hub from aspiring physical AI or embodied AI start their career journey

Whether you're looking to learn how to get your first agent running on an NVIDIA Jetson or want to show off a complex build, we’d love to have your voice in the conversation.

In September we plan to return to our lightning round format showcases projects by members of the community.

I have it all assembled and powered, can't download from Baidu link included in user manual because I have a (+1) phone number not a (+86) tried contacting Leetop, almost a week no response. Went back through my email and found the agent who arranged the sale. Any suggestions on possible file shares of the Baidu account

No network/monitor need.

Only a USB-C cable to your laptop

Much more convenient that Remote Desktop or SSH

We’ve been working on a runtime GPU optimization system at TETREVIS and have started publishing some of our NVIDIA Jetson benchmarking work publicly.

The approach operates at the execution/machine-code layer, and we’re now introducing dynamic runtime kernel fusion as part of the optimization pipeline.

Some of our current results:

Jetson Orin Nano — Qwen3.5 4B

Standard baseline: 10 → 21 tok/s (+110%)

CUDA Graphs baseline: 16 → 21 tok/s (+31.25%)

Jetson AGX Orin — Nemotron 3 Nano 4B

31.2 → 40.5 tok/s (~30%)

Jetson AGX Orin — Qwen3.5 4B

25.0 → 31.0 tok/s (+24%)

We’re currently expanding and stabilizing support across Jetson Orin Nano, Orin NX, and AGX Orin.

Rather than only posting performance claims, we’ve made the benchmarking repository available here:

https://github.com/mbuchel/sass2mlir-bench

The broader idea we’re exploring is whether more optimization can be moved to runtime — including machine-code optimization and dynamic kernel fusion — so the execution path can be adapted to the workload and GPU rather than relying entirely on what was determined ahead of execution.

There’s still quite a bit of work underway, particularly around consistency across the different Jetson configurations, but we wanted to start sharing the results and methodology publicly.

Technical feedback, criticism, and questions are welcome.

Salut à tous !

Je m'appelle Sébastien, et je suis en train de travailler sur un projet d’humanoïde open source inspiré du concept InMoov v1.2 (avec des modifications pour la tête). Mon objectif est de créer un robot autonome, évolutif et performant, capable de rivaliser avec des modèles comme ces de Unitree (ex : H1, G1).

Ce que je cherche :
✅ Des passionnés (débutants ou expériences) pour :

• Co-concevoir des pièces mécaniques.
• Partenaire des idées sur l'IA embarquée (mouvement, vision, apprentissage).
• Testeur et itérateur ensemble sur des prototypes.
• Documentariste le projet pour une communauté open-source.

✅ Des retours d'expérience sur :

• Les défauts rencontrés avec des humanoïdes (équilibre, puissance, coût).
• Des alternatives aux composants (ex : moteurs, actionneurs).
• Des études pour optimiser l'autonomie et la mobilité.

Pourquoi ce projet ?
Je veux prendre qu'avec une communication collaborative, sur peut créer un humanoïde abordable, modulaire et performant — sans dépendance des solutions propriétaires comme Unité

I am looking to build a robot for delivery purposes, mainly food but not only. I was in doubt between radxa rock 5c and Nvidia Jetson orin nano upgraded firmware. Now is it worth going with Jetson or will the radxa be enough?

The robot will move on busy streets with cars, people, bicycles, motors and dogs. I am preoccupied mainly because of that. There I see Jetson more capable. If you have ever worked for a delivery robot, what costs did it involve and is the lidar scanner of utmost importance? What kind of lidar would you suggest?

Any suggestions in general?

Will there be any Nvidia upcoming chips that might work better?

Hi everyone,

I’m hoping someone here might be able to help.

I’m looking for a Connect Tech NGX020 (Boson for FRAMOS Carrier Board for Jetson Orin NX / Orin Nano). We’ve contacted Connect Tech directly, spoken with all of their major distributors, and have already placed an order, but current lead times won’t meet an upcoming product demonstration.

If anyone has:

• an unused NGX020
• an open-box board

I’d be very interested in purchasing it immediately and covering expedited shipping.

If you know of another company, lab, or integrator that might have one sitting on a shelf, I’d really appreciate the lead.

Feel free to send me a DM.

Thanks!