Test out Arduino's Uno Q - The new Single-Board Computer!

View table of contents ...  

In this RoadTest, testers will explore the Arduino Uno Q, a single-board computer that "unlocks a new level of performance for the Arduino ecosystem". This hardware uses Qualcomm's DragonwingTm QRB2210 MPU running a full Debian Linux operating system, and the real-time responsiveness of a dedicated STM32u585 MCU running Arduino sketches over Zephyr OS, all on one board.

Arduino Uno QArduino Uno Q

What’s the UNO Q?

  • UNO Q is a hybrid single-board computer + microcontroller platform. It combines a powerful application processor (Qualcomm DragonwingTm QRB2210) running a full Debian Linux OS with a real-time micro-controller (STM32U585, Arm Cortex-M33) running Arduino-style code under Zephyr OS. 

  • The board retains an UNO-form factor and includes both classic UNO headers and additional high-speed connectors, making it compatible with standard shields and also able to support cameras (MIPI-CSI), displays (MIPI-DSI or USB-C video), audio I/O, and more. 

  • Onboard memory/storage options: either 2 GB LPDDR4 + 16 GB eMMC, or a 4 GB + 32 GB variant. For this RoadTest we intend to provide the 4GB + 32GB variant.

  • Connectivity includes dual-band Wi-Fi 5 and Bluetooth 5.1.

Why This May Be Interesting To You

  • Hybrid architecture: Real-time + high-level computing. Run Linux applications (Python, AI, web stack, etc.) on the MPU while using the MCU for deterministic real-time I/O, sensors, motors, or actuators.

  • Edge AI, multimedia, vision & IoT ready. GPU, ISP(s), camera/display/audio interfaces, plus Wi-Fi/Bluetooth — great for vision, voice, AI-powered embedded systems, smart home, robotics, etc.

  • Shield and ecosystem compatibility. Classic UNO headers let you reuse existing shields. Additional high-speed connectors and Qwiic support make sensor/module expansion easy.

  • Storage & memory for heavier workloads. On-board eMMC and LPDDR4 mean you can run more complex software stacks, store data locally, or do machine-learning inference without external modules.

  • Unified development workflow. Use the classic Arduino IDE (for MCU sketches), or leverage the new Arduino App Lab — combining Linux apps, Arduino sketches, Python, and AI models in a single environment

Key Specifications

Parameter / Feature Details
Application Processor (MPU) Qualcomm DragonwingTm QRB2210 — 4× Arm Cortex-A53 @ 2.0 GHz, Adreno GPU, dual ISPs (13 MP + 13 MP or 25 MP) 
Real-time MCU STM32U585 (Arm Cortex-M33 up to 160 MHz), running Arduino Core on Zephyr OS 
RAM / Storage Options 2 GB LPDDR4 + 16 GB eMMC, or 4 GB + 32 GB variant 
Wireless Connectivity Wi-Fi 5 (2.4 / 5 GHz), Bluetooth 5.1 
USB / I/O Ports USB-C (power / device / host / video), classic UNO headers, high-speed carriers (MIPI-CSI, MIPI-DSI, audio, etc.), Qwiic connector 
Expansion & Peripherals Camera, display, audio via high-speed connectors; shields via UNO headers; sensors/modules via Qwiic 
Operating Systems Debian-based Linux (on MPU) + Arduino Core on Zephyr OS (on MCU) 
Board Form Factor / Size Standard UNO form factor (≈ 68.85 × 53.34 mm) 
Power Input USB-C (5 V up to 3 A) or VIN 7–24 V (per official specs) 

Supporting Links

- Official Arduino Documentation on Arduino Uno Q

- Arduino Uno Q Datasheet

Possible Applications

Here are some project types or scenarios where UNO Q could shine — and which would make attractive RoadTests:

  • Edge-AI vision systems: Use MIPI-CSI cameras + the MPU’s GPU and ISP + Linux-based ML frameworks, e.g. object detection, surveillance, smart cameras.

  • Robotics with high-level control + real-time motor/sensor I/O: Let the MCU handle the real-time signals (motors, servos, sensors), while the MPU runs high-level planning, vision, or network connectivity.

  • Smart home / IoT hubs: Combine Wi-Fi/Bluetooth, sensors (I²C / Qwiic), cameras/displays, local storage, ideal for gateways, smart appliances, local data logging or automation systems.

  • Multimedia or kiosk applications: Use USB-C video output, audio I/O, and Linux-capable software to build media players, interactive kiosks, control panels, or small-form-factor PCs.

  • Hybrid projects blending high-level tasks and embedded I/O: Data logging + real-time data acquisition + Linux-based analysis; sensor fusion + local ML + actuator control; AI-enabled embedded devices.

What You’d Need to Try It

To fully explore the capabilities of UNO Q in a RoadTest, you might want to have / plan for:

  • A USB-C cable & power supply (capable of delivering 5 V / ~3 A) or suitable VIN power source

  • Optional display (via USB-C video output or MIPI-DSI carrier) and input devices (keyboard, mouse) if testing Linux desktop-like usage

  • Camera modules (MIPI-CSI or USB-camera) if testing vision or multimedia functionality

  • Sensors or modules for I²C / Qwiic, or other I/O (GPIO, ADC, etc.) to test MCU side real-time capabilities

  • Sample peripherals, audio devices, external storage (USB), SPI/I²C sensors, shields, to test expansion, compatibility, and real-world integration

  • Software tools: Arduino IDE (for MCU sketches), or Arduino App Lab / Linux development environment for MPU workflows

Your Potential Tasks and Instructions

What you could choose to do as a part of your RoadTest application to explore UNO Q:

  • Boot & setup: First power-up, confirm Linux boots, check MCU side works, test connectivity (Wi-Fi, Bluetooth), ensure board is stable under load.

  • Hybrid application test: Write a test project that uses Linux for data processing/storage (e.g. image capture + ML inference + saving), while MCU manages real-time sensor reading or actuator control, document the workflow, performance, CPU/memory usage, latencies, and any integration challenges.

  • Peripheral & expansion test: Connect camera or display, test audio I/O, Qwiic sensors/modules, UNO shields, verify compatibility and performance across different use cases.

  • Networking & edge-IoT test: Set up a networked service (e.g. MQTT, HTTP server), gather sensor data, push to cloud or local storage, test reliability of Wi-Fi and system resources under sustained workloads.

  • Real-world use case demo: Build a small but practical project , e.g. a smart camera + sensor hub, a robot with vision, a data logger with local processing, and document steps, code, performance, and user-experience (setup, ease of use, limitations).

  • Development workflow evaluation: Use both Linux side (App Lab / Python / shell) and MCU side (Arduino IDE) — note ease of switching between environments, tool-chain stability, resource constraints, documentation, and perceived developer experience.

When Applying

When the RoadTest is open, tell us:

  1. Your background and experience (especially with Linux, embedded systems, Arduino, or SBCs).

  2. What kind of project or test you plan to build — and why UNO Q is appropriate.

  3. Which aspects you want to evaluate: raw performance, integration complexity (sensors/peripherals), hybrid MCU/MPU workflows, expansion flexibility, power consumption, networking reliability, user-experience, etc.

Important Dates

  • Begin enrolment: 22nd of December 2025

  • End enrolment: 25th January 2026

  • RoadTester selection: Within 2 weeks after enrolment ends

  • Kit shipment: Within 2 weeks after RoadTester selection subject to compliance checks.

  • Start of RoadTesting: On receipt of kit

  • Review deadline: 2 to 3 months after kit receipt

Terms and Conditions

Are you interested?

About the Sponsor

Arduino is a company that develops open-source hardware and software designed to make embedded electronics accessible for engineers, educators, and makers, offering a wide range of microcontroller boards, development tools, and a global ecosystem that supports rapid prototyping and product development. For more information click here.

RoadTest Reviews
Comment List
Anonymous
  • IDK why the LEDs irk me so.
    a snapdragon with an arduino sounds great

  • # df -h /
    Filesystem              Size   Used   Avail   Use% Mounted on
    /dev/mmcblk0p72   9.3G  7.8G    1.1G   89%     /

    /dev/mmcblk0p73   3.6G  1.2G    2.2G   36%    /home/arduino
    /dev/mmcblk0p71   511M 262M   250M  52%   /boot/efi

  • Thanks for the update. I row realize the 4GB ones are still unavailable, but it looks like the Arduino site is taking orders now!

  • How much available space do you have left in the "/" partition.  I have read that using App Lab will consume up to 4GB for the Docker containers which leaves very little space on a 2GB/16GB version.  It may depend on how many different Bricks (resources) that you used.

  • I have the 2G version, it took several weeks to get it.

  • Hi  Thanks again for this feedback. What version of the Q did you purchase? The 2 GB LPDDR4 + 16 GB eMMC, or 4 GB + 32 GB variant?
    Steve K

  • Thanks   for sharing such clear, hands-on notes about getting the Uno Q and App Lab up and running; that kind of detail is really helpful when planning first experiments. I am especially excited now to see how well App Lab actually handles the dual-brain programming model and all the plumbing between sketches, Python, and AI models on this new platform.

  • Note to Sponsor,

    I put my application in last month when I first heard about it. With 8 days left till road testers are selected, I'm really hoping I get selected to test this cutting-edge development board, and the launch of the cool new Arduino App Lab just doubles that excitement. This isn't just a minor tweak; it's a huge step forward, promising a totally unified and smooth development system.

    From my research on the Uno Q, it seems like the App Lab's main advantage is its seamless integration of three distinct, yet related, elements: traditional Arduino sketches, versatile Python scripts, and cutting-edge AI models. This cohesive system is invaluable because it eliminates the typical difficulties and constant switching between tools that plague multi-language, multi-platform projects.

    Plus, this powerful software platform is a perfect match for the new UNO Q's hardware. Its advanced "dual-brain" setup—I'm guessing a high-speed main chip and a dedicated secondary one for specialized stuff like machine learning or real-time control—makes this a genuinely appealing and high-value testing opportunity. The combo of the App Lab's software unity and the UNO Q's sophisticated hardware creates an amazing foundation for quickly building and deploying complex, smart embedded systems.

  • Yep, figured it out.  Thanks