The gap between robotics prototypes and production deployments has always been an infrastructure problem disguised as a hardware problem. Teams build incredible computer vision models and robotic control systems on NVIDIA Jetson developer kits, only to hit a wall when scaling to production fleets. The bottleneck isn't the AI or the algorithms—it's the months spent building custom Linux systems, provisioning infrastructure, and OTA mechanisms that should have been solved problems.
Today, we're announcing native provisioning support for NVIDIA Jetson Orin Nano, Orin NX and AGX Orin in Avocado OS. This completes our production software stack for the industry's leading AI edge hardware, delivering deterministic Linux, secure OTA updates, and fleet management from day one.
Through partnerships with companies like RoboFlow and SoloTech, and conversations with teams building everything from autonomous mobile robots to industrial smart cameras, a clear pattern emerged. The technical challenges weren't about AI models or robotic control algorithms—teams had those figured out. The bottleneck was infrastructure.
Teams consistently hit the same obstacles:
These aren't edge cases. This is the standard experience of taking Jetson from prototype to production. And it's exactly backward—teams solving hard problems in robotics and computer vision shouldn't be rebuilding the same embedded Linux infrastructure.
NVIDIA Jetson Orin Nano delivers 67 TOPS of AI performance with exceptional power efficiency. It's the computational foundation for modern edge AI—supporting everything from multi-camera vision systems to real-time SLAM processing to local LLM inference. The hardware is production-ready.
The software needs to match.
What "production-grade" actually means:
Stable Base OS: Deterministic Linux that supports robust solutions. Not Ubuntu images that drift with package updates. Reproducible, image-based systems where every device runs identical, validated software.
Full NVIDIA Tool Suite: CUDA, TensorRT, OpenCV—pre-integrated and production-tested. Not reference implementations that require months of BSP work. The complete NVIDIA stack, ready to support inference solutions from partners like RoboFlow and SoloTech.
Day One Provisioning: Factory-ready deployment without custom scripts and USB ceremonies. Cryptographically verified images, hardware-backed credentials, and deterministic flashing workflows that integrate with manufacturing partners.
Fleet-Scale Operations: Atomic OTA updates with automatic rollback. Phased releases with cohort targeting. Air-gapped update delivery for secure environments. Infrastructure that works reliably across thousands of devices.
This is what we mean by production-ready hardware meeting production-grade software. Jetson provides the computational horsepower. Avocado OS and Peridio Core provide the operational infrastructure to actually ship products.
With Jetson provisioning now available, teams get the complete deployment pipeline:
This isn't a reference design or example code. It's production infrastructure that scales from 10 devices to 10,000 and beyond.
The robotics industry is accelerating at an unprecedented pace. The foundational layer—perception—is rapidly maturing, unlocking capabilities that seemed years away just months ago. Vision language models (VLMs) and vision-language-action models (VLAs) are fundamentally changing how robots understand and interact with their environments. Engineers who once relied entirely on deterministic control systems are now integrating fine-tuned AI models that can handle ambiguity and adapt to novel situations. The innovation happening right now suggests 2026 will be a breakout year for practical robotics deployment.
Last week at Circuit Launch's Robotics Week in the Valley, we saw this firsthand. Teams that aren't roboticists or computer vision experts were training models with RoboFlow, integrating VLA platforms like SoloTech, and building working demonstrations in hours—not weeks.
The AI tooling has advanced exponentially. Inference frameworks are mature. Hardware platforms like Jetson deliver exceptional performance. But embedded Linux infrastructure has been the persistent bottleneck preventing teams from shipping at the pace they're prototyping.
This matters because:
When prototyping velocity increases 10x, production infrastructure can't remain a 6-month investment. Teams building breakthrough applications need to move from working demo to deployed fleet at the same pace they move from idea to working demo.
The companies winning in robotics will be the ones focused on their core innovation—better vision algorithms, more sophisticated manipulation, smarter navigation. Not the ones rebuilding Yocto layers and debugging RTC drivers.
The challenge with Jetson provisioning isn't technical complexity—it's reproducibility at scale. Most teams start by configuring their development board manually: installing packages, setting up environments, tweaking configurations until everything works. Then they try to capture those steps in scripts to replicate the setup on the next device.
This manual-to-scripted approach falls apart quickly. What runs perfectly on your desk becomes unpredictable in production. By the time you're managing even a handful of devices, you're troubleshooting subtle environment differences, dealing with drift from package updates, and questioning whether any two devices are truly running the same stack.
Production provisioning solves this fundamentally differently. Instead of scripting manual steps, you're building reproducible system images where every device boots into an identical, validated environment. The OS becomes a clean foundation—deterministic, verifiable, and ready to run whatever AI toolchain your application requires. No configuration drift. No "it works on my machine" surprises.
This is where Avocado OS and NVIDIA's tegraflash tooling come together. We've integrated deeply with NVIDIA's BSP to automate the entire provisioning workflow—partition layouts, bootloader configuration, cryptographic verification, hardware initialization sequences. The complexity is still there, but it's handled systematically rather than cobbled together through scripts.
We document the Linux host requirement explicitly because it matters. Provisioning workflows require reliable hardware enumeration and direct device access. macOS and Windows introduce VM-in-VM architectures that create timing issues and device passthrough complexity. Native Linux (Ubuntu 22.04+, Fedora 39+) ensures consistent, reliable provisioning.
For production deployments, this integrates with manufacturing partners. Advantech, Seeed Studio, and ecosystem partners can run provisioning at end-of-line, delivering pre-configured devices directly to deployment sites. Zero-touch deployment at scale.
Teams can scale up and down within the Jetson family with unified toolchains and processes across the Jetson family:
One development workflow. Consistent provisioning. Predictable behavior across the product line. This matters when your prototype needs to scale, or when different deployment scenarios require different performance tiers.
For teams ready to move from prototype to production, our provisioning guide walks through the complete workflow—from initializing your project to flashing your first device.
The entire process, from clean hardware to production-ready deployment, takes minutes, not months. The guide covers everything you need: Linux host setup, project initialization, building production images, and first boot configuration.
Provisioning is the foundation. What comes next is ecosystem momentum.
We're working with partners across the robotics and computer vision stack—from inference platforms like RoboFlow and SoloTech to hardware manufacturers like Advantech and ASUS. The goal is creating a complete solution ecosystem where teams can focus entirely on their application layer while we handle everything below it.
We should talk if you are:
Our thesis has always been that embedded engineers should ship applications, not operating systems. The robotics acceleration we're seeing validates this more than ever. Teams have breakthrough ideas for autonomous systems, vision AI, and robotic manipulation. They shouldn't spend months on Linux infrastructure.
Jetson provisioning is production-ready today. It's the result of deep technical work, extensive partner validation, and clear understanding of what teams actually need when taking hardware to production.
Production-ready hardware. Production-grade software. Available now.
Ready to deploy production-ready Jetson? Check out our Jetson solution overview, explore the provisioning guide, or request a demo to discuss your use case.
If you're working with Jetson and want to connect about production deployment challenges, join our Discord or reach out directly—we'd love to learn about your use case and how we can help.