Arduino VENTUNO Q vs UNO Q: Which Edge-AI Board Should You Use?

The Arduino UNO Q and Arduino VENTUNO Q share the same fundamental idea: combine a Linux-capable application processor with a separate STM32 microcontroller so high-level software and deterministic hardware control can live on the same board.

But they target very different scales of project.

The UNO Q is a compact UNO-format hybrid board built around the Qualcomm Dragonwing QRB2210 and STM32U585. It is designed to make Linux, Python, computer vision and real-time Arduino control accessible without abandoning the familiar UNO ecosystem.

The VENTUNO Q takes that architecture into a much more demanding class. It uses the Qualcomm Dragonwing IQ8 QCS8275, 16 GB of LPDDR5, 64 GB eMMC, expandable NVMe Gen4 storage, a 250 MHz STM32H5F5, triple MIPI CSI camera inputs, 2.5 Gbit Ethernet and a Hexagon Tensor processor rated at up to 40 dense TOPS.

The practical choice is therefore not simply “which is faster?” It is whether your project needs an accessible Linux-plus-Arduino platform or a full physical-AI and robotics computer.

VENTUNO Q vs UNO Q: Quick Comparison

Feature	Arduino UNO Q	Arduino VENTUNO Q
Platform type	Compact Linux SBC + real-time MCU	High-performance physical-AI / robotics computer + real-time MCU
Application processor	Qualcomm Dragonwing QRB2210	Qualcomm Dragonwing IQ8 QCS8275
CPU	Quad-core Cortex-A53 up to 2.0 GHz	Octa-core Kryo Gen 6
GPU	Adreno 702	Adreno 623
AI accelerator	Qualcomm AI-capable platform; no equivalent 40-TOPS figure published for UNO Q	Hexagon Tensor processor, up to 40 dense TOPS
Real-time MCU	STM32U585 Cortex-M33 up to 160 MHz	STM32H5F5 Cortex-M33 up to 250 MHz
MCU Flash	2 MB	4 MB
MCU RAM	786 kB	1.5 MB
System RAM	2 GB or 4 GB LPDDR4	16 GB LPDDR5
Onboard storage	16 GB or 32 GB eMMC	64 GB eMMC
NVMe	No onboard M.2 NVMe connector	M.2 NVMe Gen4
Linux	Debian	Ubuntu; Debian support planned
Wi-Fi	Wi-Fi 5, 2.4/5 GHz	Wi-Fi 6, 2.4/5/6 GHz
Bluetooth	Bluetooth 5.1	Bluetooth 5.3
Wired Ethernet	Via supported USB adapter/hub	Native 2.5 Gbit RJ45
USB	1× USB-C with host/device role switching	USB-C + 2× USB 3.0 Type-A + extra USB on JOMEGA
Camera	USB camera and high-speed camera expansion	3× onboard MIPI CSI + additional high-speed camera expansion
Display	USB-C DisplayPort, MIPI DSI expansion	HDMI + USB-C DisplayPort + MIPI DSI expansion
CAN	CAN controller, external transceiver required on exposed logic-level interface	CAN-FD with onboard PHY plus additional CAN-FD channels
UNO shield headers	Yes	Yes
Qwiic	Yes	Yes
40-pin HAT header	No	Yes
High-density expansion	JMEDIA / JMISC / JCTL and bottom expansion	JMEDIA / JMISC / JOMEGA / JCTL
Board size	68.85 × 53.34 mm UNO form factor	160 × 100 × 25.8 mm
Best fit	Advanced makers, education, compact robotics, edge IoT, lighter AI	Robotics, multi-camera vision, ROS 2, local LLMs, industrial physical AI

The Same Dual-Brain Idea, at Two Different Scales

Both boards separate high-level computing from real-time control.

UNO Q

VENTUNO Q

The development philosophy is therefore familiar across both products. The difference is the amount of compute, memory and I/O available around that architecture.

If you already understand UNO Q App Lab and Bridge/RPC, VENTUNO Q is conceptually familiar even though the hardware is far more powerful.

See our Arduino UNO Q App Lab and Bridge/RPC guide for a detailed explanation of the software model.

CPU: Quad-Core Cortex-A53 vs Octa-Core Kryo

The UNO Q’s QRB2210 uses four 64-bit Arm Cortex-A53 cores running at up to 2.0 GHz.

That is already enough for:

• Python applications;
• web servers;
• computer vision;
• lightweight local AI;
• databases;
• Docker containers;
• general Linux development.

VENTUNO Q uses the much newer Dragonwing IQ8 QCS8275 with an octa-core Kryo CPU.

The extra CPU capacity matters when several demanding Linux processes must run simultaneously: ROS 2 nodes, camera pipelines, AI runtimes, databases, web services and network interfaces.

A simple smart-home gateway may never notice the difference. A robot running three cameras, visual SLAM and a local language model certainly can.

AI Hardware: This Is the Biggest Difference

Both boards are suitable for edge AI, but VENTUNO Q is explicitly built around high-performance physical AI.

UNO Q AI

The QRB2210 includes an Adreno GPU and image signal processing hardware. Arduino positions UNO Q for:

• computer vision;
• smart-camera projects;
• AI learning;
• local agents;
• intelligent automation.

The 4 GB model is especially useful for larger Linux applications and multitasking.

VENTUNO Q AI

VENTUNO Q adds a dedicated Hexagon Tensor AI processor rated at up to 40 dense TOPS.

That puts it in a different category for:

• large object-detection models;
• vision-language models;
• local LLMs;
• speech recognition;
• multimodal AI;
• continuous multi-camera inference;
• industrial machine vision.

Arduino’s current examples demonstrate local language models running through both the Adreno GPU and the Hexagon NPU, with the NPU providing a substantial acceleration advantage for suitable models.

Do You Actually Need 40 TOPS?

Not every AI project benefits from a much larger accelerator.

If the task is:

• classify one image every few seconds;
• detect a simple object from one camera;
• run a compact model;
• perform basic sound recognition;

UNO Q may be entirely sufficient.

The VENTUNO Q makes more sense when inference becomes a continuous system workload rather than an occasional feature.

Examples include:

• several camera feeds;
• simultaneous perception and planning;
• local LLM plus speech recognition;
• large multimodal pipelines;
• real-time industrial inspection.

RAM: 4 GB Maximum vs 16 GB

UNO Q comes in two practical configurations:

• 2 GB RAM + 16 GB eMMC;
• 4 GB RAM + 32 GB eMMC.

For ordinary Linux development, Python, MQTT, dashboards and lighter AI, this is a useful amount of memory.

VENTUNO Q has 16 GB LPDDR5.

That extra memory becomes important when you need to hold:

• large AI models;
• multiple camera buffers;
• ROS 2 processes;
• several Docker containers;
• large datasets;
• complex desktop applications;
• local LLM context and inference buffers.

If you are unsure whether 4 GB will be enough, that is already a sign that VENTUNO Q may belong on the shortlist.

Storage: 16/32 GB vs 64 GB + NVMe

UNO Q uses onboard eMMC rather than a microSD card, which is an excellent design decision for a small Linux SBC.

The available 16 GB and 32 GB capacities are suitable for:

• Debian;
• applications;
• moderate data logging;
• small AI models;
• containers.

VENTUNO Q starts with 64 GB of eMMC and adds an M.2 NVMe Gen4 connector.

This completely changes the possible local-storage workload.

NVMe is useful for:

• ROS bag recordings;
• long camera recordings;
• large model libraries;
• industrial image archives;
• high-rate sensor logging;
• local datasets;
• large databases.

For a robot or vision system that records large quantities of data, the NVMe slot is one of VENTUNO Q’s strongest advantages.

Linux: Debian vs Ubuntu

UNO Q currently runs Debian Linux with upstream support.

VENTUNO Q currently runs Ubuntu Linux, with Arduino stating that Debian support is planned.

Both are familiar mainstream Linux environments with:

• APT package management;
• Python;
• SSH;
• Docker;
• standard Linux development tools.

The practical difference is much smaller than comparing Debian with a specialised embedded distribution such as Yocto.

Developers already comfortable with Ubuntu or Debian should feel at home on either platform.

Real-Time MCU: STM32U585 vs STM32H5F5

UNO Q’s STM32U585 is a Cortex-M33 running at up to 160 MHz with:

• 2 MB Flash;
• 786 kB SRAM.

VENTUNO Q uses the newer STM32H5F5 at up to 250 MHz with:

• 4 MB Flash;
• 1.5 MB RAM.

Both run Arduino Core on Zephyr and serve the same basic purpose: keep time-critical physical interaction away from Linux scheduling.

VENTUNO Q simply provides considerably more real-time MCU headroom.

This is useful when the control side itself is complex, for example:

• many motors;
• several CAN-FD networks;
• high-rate sensors;
• large state machines;
• precision timing;
• local safety logic.

CAN and CAN-FD

UNO Q exposes CAN functionality from the STM32 side but requires an external physical transceiver on the logic-level interface.

VENTUNO Q is much more robotics-oriented.

It includes:

• one CAN-FD interface with an onboard PHY on a screw terminal;
• three additional CAN-FD controller interfaces on JOMEGA without PHY;
• another CAN-FD controller interface on the UNO shield header without PHY.

That means a VENTUNO Q can connect directly to one CAN-FD network without adding a transceiver board, while still offering more CAN channels for expansion.

For robotic joints, distributed motor drives and industrial equipment, this is a major practical advantage.

Networking: Wi-Fi 5 vs Wi-Fi 6 + 2.5 GbE

UNO Q

• Wi-Fi 5;
• 2.4 and 5 GHz;
• Bluetooth 5.1;
• Ethernet available through a supported USB adapter or hub.

VENTUNO Q

• Wi-Fi 6;
• 2.4, 5 and 6 GHz bands;
• Bluetooth 5.3;
• native 2.5 Gbit RJ45 Ethernet.

The wired network difference is particularly important.

2.5 Gb Ethernet gives VENTUNO Q a much stronger foundation for:

• machine vision;
• large model deployment;
• remote NVMe access;
• multi-robot systems;
• ROS 2 traffic;
• industrial integration;
• large data transfers.

UNO Q is perfectly adequate for normal IoT and SBC networking. VENTUNO Q is designed for high-bandwidth edge systems.

Cameras: One of the Clearest Reasons to Move Up

UNO Q supports USB cameras and high-speed camera expansion. Its QRB2210 also contains dual image signal processors.

For:

• one camera;
• facial recognition;
• basic object detection;
• a smart camera prototype;

UNO Q can be a very sensible platform.

VENTUNO Q includes three onboard MIPI CSI camera connectors, with further camera connectivity available through JMEDIA.

That immediately opens the door to:

• stereo vision;
• front/side/rear robot perception;
• multi-angle industrial inspection;
• depth and SLAM systems;
• simultaneous tracking from several cameras.

If the project description includes “multiple cameras,” VENTUNO Q is operating on a much more appropriate hardware scale.

Display Output

UNO Q supports video output through USB-C DisplayPort. With a suitable hub or adapter it can drive a monitor and operate as a standalone Linux computer.

VENTUNO Q provides:

• dedicated HDMI;
• DisplayPort over USB-C;
• MIPI DSI through high-speed expansion.

The dedicated HDMI connector makes standalone development more convenient because the USB-C connection does not need to do everything at once.

USB Expansion

UNO Q has one very capable USB-C port with host/device role switching.

With a powered hub, it can connect:

• keyboard;
• mouse;
• USB camera;
• storage;
• audio devices;
• Ethernet adapter;
• display.

VENTUNO Q includes significantly more physical USB connectivity:

• USB-C;
• two USB 3.0 Type-A ports;
• two additional USB 3.0 interfaces on JOMEGA.

For a robot that already has a camera, LiDAR interface, external drive and USB audio device, avoiding a large external hub is a real benefit.

UNO Headers and Qwiic: Both Retain Arduino Accessibility

Despite its much larger scale, VENTUNO Q still includes UNO shield headers and a 3.3 V Qwiic connector.

That means both boards can use familiar Arduino development workflows for:

• sensors;
• shields;
• SPI devices;
• I2C devices;
• UART modules;
• simple actuators.

The VENTUNO Q does not abandon the Arduino ecosystem just because it adds more advanced connectors.

Our VENTUNO Q pinout and expansion guide covers its connector system in detail.

Raspberry Pi HAT Expansion

VENTUNO Q adds a 40-pin Raspberry Pi HAT-style header.

UNO Q does not include this connector.

This makes VENTUNO Q mechanically compatible with a much larger SBC accessory ecosystem, although every HAT still needs to be checked for:

• logic voltage;
• Linux driver support;
• GPIO mapping;
• I2C/SPI bus assumptions;
• power requirements.

The connector is valuable, but it should not be interpreted as universal plug-and-play compatibility with every Raspberry Pi HAT ever made.

JOMEGA: VENTUNO Q’s Product-Scale Expansion

The VENTUNO Q includes a 100-pin JOMEGA connector for high-density expansion.

It exposes resources such as:

• additional CAN-FD;
• USB 3.0;
• GPIO;
• PWM;
• UART;
• I2C/I3C;
• SPI;
• debug signals;
• power.

This makes it practical to build a custom robot or machine carrier board underneath the VENTUNO Q.

UNO Q has high-speed carrier connectors too, but JOMEGA gives the larger board a more extensive robotics and system-integration path.

Physical Size

UNO Q is a true UNO-format board:

VENTUNO Q is substantially larger:

This difference matters in:

• small robots;
• wearable devices;
• compact enclosures;
• portable test equipment.

UNO Q is much easier to fit where board area matters.

VENTUNO Q uses its extra area for features that would otherwise require several separate boards: Ethernet, USB-A, HDMI, M.2, multiple camera ports, CAN-FD and large expansion headers.

Power: UNO Q Is Much Easier to Feed

UNO Q can be powered from:

• USB-C at 5 V up to 3 A;
• VIN from 7–24 V.

This is relatively manageable for a compact SBC.

VENTUNO Q has a much larger power envelope. Arduino’s SBC setup documentation supports:

• 7–24 V external DC power;
• high-current screw-terminal power;
• USB-C Power Delivery at 9–20 V;
• carrier power through JOMEGA.

Arduino recommends a 65 W-class supply for full standalone operation and heavy AI/USB workloads.

This is an important design distinction.

A battery-powered mobile robot may need a serious DC/DC stage for VENTUNO Q, whereas UNO Q can be much easier to integrate into a smaller electrical system.

Thermal and Mechanical Design

More processing capability normally means more heat.

A VENTUNO Q installation running continuous AI inference, NVMe access and several cameras should be treated as a small high-performance computer rather than as a simple MCU board.

Enclosure design should consider:

• airflow;
• heatsinking;
• ambient temperature;
• power-supply losses;
• camera and NVMe heat;
• mechanical access to connectors.

UNO Q is easier to package in compact prototypes because its performance and power targets are lower.

Arduino App Lab

Both boards use Arduino App Lab to combine:

• Linux applications;
• Python;
• Arduino sketches;
• AI models or Bricks;
• Bridge/RPC communication.

This gives developers a consistent workflow across the two platforms.

You can prototype an application concept on UNO Q and move to VENTUNO Q when the workload eventually needs more RAM, AI acceleration or I/O.

ROS 2

VENTUNO Q has a much stronger robotics emphasis and Arduino provides ROS 2 Jazzy documentation directly for the platform.

The combination of:

• Ubuntu;
• 16 GB RAM;
• 2.5 Gb Ethernet;
• multiple cameras;
• NVMe;
• CAN-FD;
• NPU acceleration;
• a separate real-time MCU;

maps naturally onto ROS 2 robots.

UNO Q can still participate in robotics applications and run Linux middleware, but VENTUNO Q is the obvious step up when ROS 2 becomes the centre of the system rather than one optional component.

Local LLMs

UNO Q can run small local models, particularly on the 4 GB version.

VENTUNO Q is much better suited to this workload because it provides:

• 16 GB RAM;
• 64 GB eMMC;
• NVMe expansion;
• GPU compute;
• Hexagon NPU acceleration.

Arduino’s VENTUNO Q examples explicitly demonstrate local LLM execution through both the GPU and NPU.

If your project genuinely requires an onboard language model rather than a small classification network, VENTUNO Q is the more appropriate hardware class.

Computer Vision

UNO Q is a good choice for:

• single-camera object detection;
• face recognition;
• smart-home vision;
• educational AI;
• visual inspection prototypes.

VENTUNO Q is designed for:

• multi-camera perception;
• continuous high-rate inference;
• robot navigation;
• visual SLAM;
• industrial defect detection;
• complex multimodal processing.

The dividing line is not whether both boards can process an image. They can. The question is how many simultaneous vision workloads must run and how much latency matters.

UNO Q for Compact Robotics

UNO Q is still an excellent robotics board when the project is moderate in scale.

A small mobile robot could use:

• USB camera;
• Python vision application;
• STM32 motor control;
• Qwiic IMU;
• Wi-Fi;
• CAN through an external transceiver.

This keeps cost, power and mechanical size under control.

VENTUNO Q for Serious Robotics

VENTUNO Q becomes compelling when the robot requires:

• multiple cameras;
• ROS 2;
• visual SLAM;
• LiDAR;
• several CAN-FD motor controllers;
• local AI;
• large storage;
• high-speed wired networking;
• USB peripherals;
• complex autonomy.

In that environment, UNO Q may eventually require several external hubs, network adapters, storage devices and carrier boards. VENTUNO Q already integrates much of that infrastructure.

Industrial Edge Applications

VENTUNO Q’s 2.5 Gb Ethernet, screw-terminal CAN-FD, NVMe and multi-camera interfaces make it much easier to use as an industrial physical-AI node.

A machine-vision station could:

1. receive a hardware trigger on the STM32;
2. capture an image from a MIPI camera;
3. run NPU inference;
4. save failed images to NVMe;
5. send results over 2.5 Gb Ethernet;
6. activate a reject mechanism through the real-time MCU.

UNO Q can build a smaller version of the same concept, but VENTUNO Q is better suited when throughput and data volume become production-scale concerns.

Education and Learning

UNO Q is the more approachable platform for learning hybrid embedded computing.

It is smaller, simpler and retains a very clear UNO identity.

Students can begin with:

• normal Arduino sketches;
• Qwiic sensors;
• Python;
• Linux;
• Bridge/RPC;
• one-camera AI.

VENTUNO Q is more appropriate for university robotics labs, advanced AI courses and research projects where students need access to ROS 2, local LLMs and multi-camera systems.

Cost of the Complete System

Even without comparing purchase prices, system cost is worth thinking about.

UNO Q may need external accessories if a project requires:

• Ethernet;
• multiple USB devices;
• large external storage;
• additional CAN transceivers;
• multiple cameras.

VENTUNO Q integrates many of those functions directly.

For a simple project, that integration is unnecessary. For a complex robot, it may reduce the number of separate boards, cables and adapters required.

When UNO Q Is the Better Choice

Choose UNO Q when you value:

• compact size;
• lower system complexity;
• easier power requirements;
• standard UNO form factor;
• Debian Linux;
• 2 GB or 4 GB options;
• normal IoT workloads;
• single-camera vision;
• lighter edge AI;
• education;
• advanced maker projects;
• small robots;
• quick prototypes.

When VENTUNO Q Is the Better Choice

Choose VENTUNO Q when the application needs:

• 40 dense TOPS NPU acceleration;
• 16 GB RAM;
• 64 GB eMMC;
• NVMe storage;
• three onboard MIPI cameras;
• 2.5 Gb Ethernet;
• Wi-Fi 6;
• multiple USB 3.0 ports;
• native HDMI;
• several CAN-FD channels;
• ROS 2;
• local LLMs;
• multi-camera robotics;
• industrial machine vision;
• large physical-AI systems.

Decision Matrix

Requirement	More natural choice
Smallest board	UNO Q
Simpler power design	UNO Q
Traditional UNO-sized project	UNO Q
Lower-complexity Linux + MCU learning	UNO Q
Single-camera AI	UNO Q
Compact smart-home / IoT gateway	UNO Q
16 GB RAM	VENTUNO Q
Large local AI model	VENTUNO Q
40 dense TOPS NPU	VENTUNO Q
Multiple MIPI cameras	VENTUNO Q
NVMe storage	VENTUNO Q
2.5 Gb Ethernet	VENTUNO Q
ROS 2 robot	VENTUNO Q
Multiple CAN-FD buses	VENTUNO Q
Large industrial vision system	VENTUNO Q
Arduino App Lab + Bridge/RPC	Both
UNO shields and Qwiic	Both
Linux + real-time STM32	Both

Can You Start on UNO Q and Move to VENTUNO Q Later?

In many cases, yes.

Because both boards share:

• Arduino App Lab;
• Python;
• Arduino sketches;
• Bridge/RPC concepts;
• UNO-style expansion;
• Linux + STM32 architecture;

it is possible to structure an application so that much of the software design transfers naturally.

The hardware abstraction should be kept clean. Instead of hard-coding specific GPIO assumptions throughout the Linux application, expose meaningful MCU services through Bridge:

That makes moving the high-level application to a more powerful board much easier.

Final Thoughts

The Arduino UNO Q and VENTUNO Q are not really direct substitutes. VENTUNO Q is the logical scale-up of the same dual-brain concept.

The UNO Q is the more accessible choice for developers who want Debian Linux, Python, computer vision and real-time STM32 control in a compact UNO form factor. It is powerful enough for many edge-AI, automation and robotics projects without requiring the power, space and system complexity of a larger computer.

The VENTUNO Q is designed for projects where AI and robotics are no longer side features but the main workload. Its 16 GB LPDDR5, 64 GB eMMC, NVMe, 40-TOPS-class NPU, triple MIPI CSI, 2.5 Gb Ethernet, stronger STM32, expanded USB and CAN-FD connectivity make it suitable for multi-camera robots, industrial vision and local multimodal AI.

The shortest decision is:

If UNO Q can comfortably handle the workload, it is the simpler system. If your design already needs several cameras, a large model, NVMe storage, ROS 2 and multiple CAN-FD networks, VENTUNO Q is the board built for that problem.