Breaking the Complexity Barrier: Real-Time ML Model Updates on NVIDIA Jetson with Avocado OS

I’ve always been interested in NVIDIA's Triton Inference Server –it’s an incredibly powerful framework for managing ML model inference at scale. But I could never quite close the gap in getting it onto edge devices. Cross-compilation, dependency management, and embedded deployment presented non-trivial challenges.

Recently, with a little love from the OE4T community and leveraging our hardware-in-the-loop workflows, we had a major unlock. In less than six commands, I had Triton running on a Jetson Orin Nano. Not only running, but seamlessly integrated into a hardware-in-the-loop development workflow that lets users update ML models in real-time without reflashing or rebooting.

If you've ever tried to get sophisticated AI inference frameworks working on embedded hardware, you know it's incredibly challenging due to the size and complexity of the libraries, drivers, and code dependencies involved. But doing it fast and without reflashing is possible—and next week at Open Source Summit, we're going to show you exactly how.

The Demo: From Broken to Beautiful in Seconds

Picture this: A live camera feed running through a people detection pipeline on a Jetson Orin Nano. The model is intentionally corrupted—detecting phantom people everywhere, drawing bounding boxes around empty space. It's the kind of broken AI behavior that usually means hours of debugging, recompiling, and reflashing.

When I save an updated model from my developer workstation across the network. Instantly—without any device restart or tedious deployment process—the broken model is replaced. The phantom detections disappear. Real people in the frame are now properly detected with accurate bounding boxes.

This isn't just a neat trick. It's a fundamental shift in how we can develop and maintain AI applications on edge devices.

The Technical Architecture: Where System Extensions Meet Hardware-in-the-Loop

At the heart of this demo is our Avocado OS extension system combined with hardware-in-the-loop development capabilities. Here's how it works:

The Stack:

• NVIDIA Jetson Orin Nano with 128GB storage
• NVIDIA Triton Inference Server handling model inference
• Live camera feed processed through a DeepStream pipeline
• Real-time performance metrics streamed via Peridio’s remote access tunnels
• Hardware-in-the-loop connection between developer workstation and target device

The Magic: Instead of baking everything into a monolithic image, Avocado OS uses systemd's extension capabilities to create composable, overlay-based root filesystems. When I install Triton on my development workstation, I'm building a system extension—a filesystem image that contains all the necessary components. This extension is then mounted over NFS to the running Jetson device, where systemd seamlessly integrates it into the runtime environment.

The result? Package management benefits at build time, with the deterministic and immutable guarantees you need for production embedded systems.

Why This Matters: Threading the Needle Between Yocto and Ubuntu

The Embedded Linux world has long been trapped between two polar opposite choices:

Yocto: Incredible for long-term scalability and customization, but with a steep learning curve that keeps many developers away. Getting complex AI frameworks like Triton to cross-compile for embedded targets has been a significant challenge of dependency management and build system complexity.

Ubuntu/JetPack: Quick to get started, familiar package management, but challenging to scale for fleet deployments. Runtime package management creates the kind of system state variability that makes embedded engineers nervous.

Avocado OS threads this needle by taking inspiration from advancements in systemd and Linux user space communities, applying them to embedded constraints. We get the familiar developer experience of package installation (think apt install simplicity) but at build time, not runtime. We get the determinism and reproducibility of image-based systems, but with the composability that makes iteration fast.

The Triton Breakthrough: From Months to Minutes

Here’s why this was such a moment for me. Triton Inference Server is powerful—it's designed to handle model ensembling, resource management across GPUs, and seamless model updates. But getting it to work on edge devices has historically been tough to say the least.

The compilation process is massive and fragile. Cross-compilation for embedded targets adds another layer of complexity that upstream maintainers don’t typically anticipate. I've spent countless hours over several years trying to make this work for client projects, often giving up in frustration.

But with Avocado's extension system, those compilation complexities are handled once, in our build infrastructure. The result is a pre-built package that just works. Six commands. First try. No debugging required.

This isn't just about Triton—it's about making the entire ecosystem of AI and ML tools accessible to embedded developers without requiring them to become build system experts.

Beyond the Demo: Production Realities

What makes this architecture compelling isn't just the development experience—it's how it scales to production:

Deterministic Updates: Instead of managing package dependencies at runtime, you're managing filesystem image overlays. System updates become atomic operations with clear rollback capabilities.

Fleet Management: Through Peridio’s Cloud integration, the same hardware-in-the-loop mechanism that enables development becomes your production deployment pipeline. Push model updates to thousands of devices with the same reliability you just saw in the demo.

Observability: The remote access tunnels we're using to stream Triton's performance metrics in real-time become your production monitoring infrastructure. Debug issues on devices in the field as easily as if they were on your desk.

The Bigger Picture: Composable Embedded Systems

This demo represents something larger than just an AI inference showcase. We're demonstrating a new approach to embedded system architecture—one that embraces composability without sacrificing the guarantees that embedded systems require.

By leveraging systemd's extension capabilities we can build systems that are:

• Deterministic: Read-only base systems with well-defined extension points
• Reproducible: Build-time composition means every device gets exactly the same bits
• Maintainable: Distributed fleet management without the complexity of traditional embedded update mechanisms
• Developer-friendly: Familiar workflows that don't require specialized embedded expertise

What's Next: ROS, Holoscan, and the Future of Edge AI

The robotics community is already moving toward Yocto-based deployments for production systems, fueled by IP concerns with Ubuntu and the need for more deterministic behavior. Adding ROS 2 support to Avocado OS is a natural next step—imagine the same hardware-in-the-loop workflow for robotics applications.

Even more exciting is NVIDIA's Holoscan framework for medical and other latency-critical applications. Being able to compose Holoscan-based systems with the same ease we've demonstrated with Triton could unlock entirely new categories of edge AI applications.

See It Live at Open Source Summit

We'll be demonstrating this live at Open Source Summit next week. Come find us at the booth (#B9)—we'll walk through the entire technical architecture, show you the hardware-in-the-loop development workflow, and discuss how this approach could work for your embedded AI projects.

The future of embedded development doesn't have to be a choice between complexity and capability. Sometimes you can have both.

This is just the beginning. 🥑

— Justin

Avocado OS is entering beta with support for NVIDIA Jetson, Raspberry Pi, and NXP platforms. The complete demo stack, including the Triton integration, will be open-sourced following Open Source Summit. Learn more about Avocado OS or follow our progress.