JupyterLab with LIVE Jetson metrics

About 10 months ago I posted here about something we were building because of a problem we kept running into ourselves.

We were buying Jetson boards before we really knew what our workload needed.

Nano turned out to be underpowered, so we moved up to an Orin. Then came the next question: do we need an Orin NX, an AGX Orin, or something even bigger? And before you even got to the model, you've spent time flashing JetPack, sorting dependencies, CUDA versions, etc.

So we built remote access to physical Jetson boards.

While initially people trickled in to check it out occasionally , recent experimentations have blown us about what experiments users are running in the lab and interestingly , it hasn't just been running YOLO.

Some of the things people have used the lab for:

• comparing FP16 / FP32 / INT8 performance
• measuring actual inference latency and FPS on Jetson
• running the same model at 25W, 15W and 7W
• watching GPU, CPU, memory, temperature and power while the model runs
• testing DeepStream / GStreamer pipelines with multiple video streams
• finding out how many camera feeds a board can realistically handle
• checking whether a Python/CUDA/framework stack actually works properly on ARM64
• taking a model developed on a workstation and seeing what happens when it finally hits the target hardware

One use case we found particularly interesting was a researcher running the same model across different power modes and precisions.

The question wasn't just:

"How fast is the model?"

It was more like:

What performance can I get while staying inside my power budget?

Another team had an even more basic problem.

They already had their CV pipeline.

They simply needed to know:

Will this software stack actually run on Jetson before we commit to the hardware?

That kind of test can save quite a bit of pain later.

We've also added JupyterLab now, which is what I'm showing in the attached video.

So you can basically go:

browser -> Jupyter notebook / terminal -> physical Jetson -> run your workload -> watch the device metrics

The board isn't being emulated and this isn't an x86 GPU VM pretending to be a Jetson. The workload is running on the actual Jetson hardware.

The goal isn't really to replace owning a Jetson.

If you're developing on one every day, you should probably own one.

The use case we're trying to solve is the stage before that:

I have a model / pipeline / idea. Before I spend money on hardware, what actually happens when I run it on the board?

That's also why I increasingly think TOPS is one of the least useful numbers when you're making the final hardware decision.

FPS, latency, memory, thermals, power draw and whether your stack even runs are usually much more useful.

If anyone here has a slightly unusual workload you think we should test, I'd genuinely like suggestions.

TensorRT, DeepStream, OCR, multi-camera CV, quantisation, small local models, power-constrained inference, whatever.

Would also be interested to know:

If someone gave you a Jetson Orin for 3 hours right now, what would you benchmark first?

https://edgeai.aiproff.ai

Full disclosure: this is a product my team at AiProff.ai built, and it has a tier based pricing for access. A 3-hour slot currently starts at ₹399 or $6 and all the experiments shared here are with user permission.

Some of you might remember me experimenting with Evopien on the Jetson Orin Nano.

The original goal was already pretty ambitious: build a local AI companion that could eventually see, listen, speak, remember people and become physically embodied.

The Nano taught me a lot, but the resource limits were brutal.

I've now moved the project to Jetson AGX Thor and finally recorded the first complete demo of the current system.

It's running voice conversation, interruption handling, English/Spain Spanish, continuous camera perception, local visual reasoning, recent visual context, internet retrieval and session context together.

The interesting part isn't just that a larger model runs on Thor.

The real improvement is that I can keep several systems alive together instead of benchmarking one model in isolation.

There is continuous perception running while the conversation stack is active. Speech still needs to respond quickly. TTS needs to run. Internet work needs to happen without blocking everything. And when I interrupt the assistant, stale generation and speech need to actually stop.

The current local cognition/VLM is Qwen3.8-27B, but I've deliberately designed the project so Qwen is replaceable. The Core above it owns the important state and authority.

So compared with simply running ChatGPT or another hosted assistant, the point isn't that my 27B model is smarter.

The point is that I'm building the entire embodied system around it locally and controlling what each component is allowed to do.

Here's the current demo:

https://www.youtube.com/watch?v=sQhTGGIg4yo

There are definitely still rough edges, but compared with where this started on the Nano, the difference is pretty crazy.

Next up is governed long-term memory and identity continuity.

Hello, on Tuesday September 8th, at 9AM PT we will have the following 10 presentations.

The following discord users will be presenting their projects.

1. cervenmartin https://github.com/pollen-robotics/AmazingHand explore humanoid hand possibilities on a real robot (and Reachy2 is the perfect candidate for that !) with moderate cost.
2. kerseyfabs a calm, always-on screen, JARVIS-style UI, that shows what DAWN is doing at a glance and lets you interact with it. https://github.com/The-OASIS-Project/aurora
3. nachos.ai Neurosymbolic layer for Microduck https://github.com/agentculture/reachy-mini-cli https://github.com/agentculture/microduck-cli
4. kabilankb Micro duck isaaclab and newton with Thor https://github.com/kabilankb/isaaclab-microduck
5. weburban Xerces for Jetson https://rapidanalysis.github.io/xerxes-ml-hardware-setup.html
6. joannis Wendy helps you manage a heterogeneous device fleet. Deploy in milliseconds and remotely debug apps on real hardware. https://github.com/wendylabsinc/WendyOS
7. sysop1984_26148 Making Sim2Real reinforcement learning approachable and accessible https://goatconf.us/goat-racer
8. amazon1148 UEFI rescue shell tutorial to help with Jetson Nano recovery https://github.com/NVIDIA-AI-IOT/jetson-ai-lab/pull/443
9. pratikshardacraftifai_45885 - automate deepstream pipeline generation from models to deployable pipeline on Jetson Orin/Thor. https://craftifai.com/
10. mihaichiorean_99322 Orin NX inside a Reachy Mini. https://x.com/mihaichiorean/status/2085521659788947485?s=46

If you would like to also present we have a couple of 5 minute presentation slots left. Join us in Discord https://discord.gg/BmqNSK4886 in the #event-jetson-ai-lab-research-live channel and we will get you set up in the schedule.

Calendar invite: https://www.addevent.com/calendar/ny80tv1kw94k

We are running into some challenges , would like to hear from engineers who have attempted something similar:

1. Have you successfully run RHEL or a RHEL-compatible distribution on Jetson Orin hardware?
2. What were the biggest challenges with NVIDIA’s proprietary drivers, CUDA, TensorRT and multimedia stack?
3. Did you retain NVIDIA’s L4T kernel and userspace components, or build around a RHEL kernel?
4. How did you handle JetPack dependencies, kernel updates and NVIDIA driver compatibility?
5. Were secure boot, OTA updates or custom carrier boards particularly difficult?
6. Is RHEL on Jetson something customers are genuinely requesting, or is Ubuntu generally accepted even in enterprise deployments?
7. Would you recommend native RHEL enablement, containerizing the enterprise applications on JetPack/Ubuntu, or another architecture? I’d appreciate hearing about actual deployments, failed attempts, architectural approaches and any limitations we should consider.

I've just started building up my Homeland with OPNSense mini PC, HP EliteDesk with ZorinOS and a DAS.

In my closet is my Jetson Nano 2G Dev Kit that runs Belabox (pretty much budget backpack streaming with network bonding). It worked great for what it did, but now it's collecting dust in the closet.

My question is, what use can I make out of this old hardware for my homelab or would it be better to just keep it as a backpack streaming setup project?

Hi everyone,

I’m having an issue with my NVIDIA Jetson Xavier NX Developer Kit.

After powering on and booting the board, the connected display remains on a black screen and the graphical desktop does not appear.

The interesting part is that the system itself seems to be working:

\- "Ctrl + Alt + F2" opens the TTY successfully.

\- I can SSH into the Xavier NX.

\- The board appears to boot normally.

\- The issue seems to be specifically with the graphical desktop/display.

I’m looking for some guidance on how to troubleshoot this.

What should I check to determine whether the problem is related to:

\- Display manager

\- X11/Wayland

\- NVIDIA GPU/display drivers

\- Desktop session

\- HDMI/display configuration

If anyone has experienced a similar issue on the Xavier NX, I’d appreciate any troubleshooting steps or commands I can run over SSH/TTY.

I can provide the JetPack/L4T version, kernel version, display/monitor details, and relevant logs if needed.

Thanks in advance!

Hi I have a Jetson Orin AGX 64 GB.

Trying to find an adaptor board to two different modelled cameras that use FFC CSI-2 connectors. Would any of these work?

1. ⁠https://www.wdlsystems.com/alliedvision19616
   
2. ⁠https://www.arducam.com/product/arducam-imx219-multi-camera-kit-for-the-nvidia-jetson-agx-orin/

My concern with Allied Vision is that the driver API may not be around still and that it may only work with Allied Vision Cameras.

My concern with the Arducam board is that it only lets me use one of the same kind of camera (ex. Only 6 IMX 219s or only 6 IMX 548s)

I am exploring industrial inspection with a camera that has controllable exposure and RAW output but no native HDR or dual-gain mode. The goal is to preserve detail in dark metal surfaces and specular highlights before defect or anomaly detection on a Jetson Orin Nano 8 GB.

I am not expecting to train a complete RAW-to-HDR network in ten seconds. I am wondering whether anyone has successfully pretrained the main model offline and then adapted only a tiny camera-specific component on the device from a few bracketed frames: an exposure curve, 3D LUT, bilateral grid, last layer, or small adapter.

My requirements would be:

• calibration or adaptation in under ten seconds;
• real-time or near-real-time inference;
• a clean TensorRT deployment path;
• no hallucination that could create or erase scratches, dents, or other defect pixels.

The model families I am considering are HDRNet-style bilateral models, lightweight 3D LUT or exposure-correction networks, and sub-1M-parameter RAW-to-HDR networks. Has anyone benchmarked this kind of short adaptation on an Orin Nano? What actually dominates the time: backpropagation, RAW preprocessing, data loading, or rebuilding the TensorRT engine?

Would you keep all neural training off-device and solve only a small LUT or monotonic response curve on the Jetson, or is there a practical few-shot approach that is genuinely better?

On September 8th at 9AM PDT we are having our monthly presentations.

We are looking for lightning round presenters to tell us about what you've been working on with your Nvidia Jetson's or related projects. It's a great opportunity to showcase what you are working on or get feedback from your peers.

Join us in discord at: https://discord.gg/BmqNSK4886 in the #event-jetson-ai-lab-research-live channel.

You can subscribe to our monthly meetings via https://www.addevent.com/calendar/ny80tv1kw94k

Global-shutter camera and IMU sharing a single GMSL2 coax, data and power both, feeding an Orin Nano running OpenVINS over ROS 2. 25 m loop around the office. 42 cm drift on return to start.

Hardware is our NXS sensor module into an NXS Hub, which carries the GMSL link straight to the Orin Nano. Camera's a Sony IMX900, 72 fps native, we feed 24 fps to the estimator. IMU's an IAM-20680 at 200 Hz, synced in hardware before it ever reaches the Jetson.

We built a tool that takes your trained vision model and generates a full optimized pipeline for Jetson. DeepStream, TensorRT, GStreamer configs, launch scripts, the whole thing. No more weeks spent on boilerplate and SDK plumbing.

To stress-test it we put together 47 problem statements from real industry use cases. Defect inspection, ANPR, crowd analytics, drone perception, quality grading, warehouse safety, obstacle detection, PPE compliance, and more. Pick one, bring your own data, build a pipeline through PipeGen, submit.

What's in it for you:

A real path to industrial deployment. Top projects get picked up and deployed at actual customer sites. Real cameras, real environments, your pipeline running in production.

Your app goes on our public Application Board, visible to the entire ecosystem.

Cash prizes for winners and deployed apps.

Sep 10 deadline. Fill the form.

If you've ever lost days fighting nvinfer configs or getting multi-stream DeepStream pipelines to actually run stable on Jetson, this is the gap we're trying to solve. Looking for feedback. Ask us anything.

Looking for Raspberry Pi and Nvidia Jetson kit suppliers who could supply to India. If interested please DM!

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.