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Arduino Ventuno Q
Manufacturer:
Arduino
Part Number: ABX00181
Description:
VENTUNO Q is the edge-AI powerhouse that senses, decides, ...
Datasheet:
Not Available
Quantity: 20
In Stock
Ship Date : 7-10 working days
In stock
Ext. Price:
₹28,700.00
VENTUNO Q is the edge-AI powerhouse that senses, decides, and acts – on a single board.
Running LLMs and agentic AI offline, with 40 dense TOPS of NPU acceleration. Real-time
MCU control. Your gateway to physical AI.
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Arduino® VENTUNO™ Q Board
Arduino VENTUNO Q is an edge-AI powerhouse designed to sense, decide, and act on a single board. With up to 40 Dense TOPS of NPU acceleration, offline AI capabilities, real-time microcontroller control, and advanced connectivity, VENTUNO Q provides a powerful platform for robotics, industrial automation, computer vision, and physical AI applications.
Overview
Arduino VENTUNO Q features a dual-brain architecture combining a Qualcomm Dragonwing™ IQ8 MPU with a dedicated STM32H5 real-time microcontroller. This architecture enables sensing, AI decision-making, and real-time control on a single platform.
The AI processing system delivers up to 40 Dense TOPS of NPU acceleration for vision models, Large Language Models (LLMs), and multimodal AI inference. The real-time control system runs the Arduino Core on Zephyr RTOS, enabling deterministic control of motors, CAN-FD, PWM, and GPIO.
The two processing systems communicate through an RPC bridge, reducing the complexity associated with using multiple devices.
- 16 GB LPDDR5 RAM
- 64 GB industrial-grade eMMC storage
- Standard upstream Ubuntu Linux support
- Debian support coming soon
- Docker and apt pre-installed
- Compatible with VS Code, PyCharm, and Jupyter
- No proprietary SDK or vendor lock-in
Key Features
40 Dense TOPS NPU
Qualcomm Hexagon™ Tensor AI Processor provides up to 40 Dense TOPS of NPU acceleration for AI workloads.
Dual-Brain Architecture
Combines a Qualcomm Dragonwing™ IQ8 MPU with an STM32H5 real-time MCU for AI processing and deterministic control.
16 GB LPDDR5 RAM
High-performance memory for demanding AI, vision, robotics, and edge computing workloads.
64 GB eMMC Storage
Industrial-grade onboard storage with support for external NVMe Gen.4 storage through the M.2 connector.
Real-Time Control
STM32H5 MCU enables deterministic control of motors, CAN-FD, PWM, GPIO, and other embedded interfaces.
Advanced Connectivity
Includes Wi-Fi 6, Bluetooth 5.3 and 2.5 Gigabit Ethernet for high-speed connected applications.
Purpose-Built for Robotics and Industrial Edge AI
VENTUNO Q is designed for systems that need to move, manipulate, sense, and respond with precision.
- Autonomous mobile robots
- Drones and robotic systems
- Pick-and-place robotic arms
- Vision-guided manipulation
- Visual SLAM applications
- Predictive maintenance
- Industrial process automation
- Energy monitoring
- Smart city traffic monitoring
- Connected and responsive environments
- Automated quality inspection
- On-premises defect detection
- Computer vision research
- Generative AI development
- Embedded systems education and research
One Board. Three Ways to Build with AI.
1. Ready to Run
VENTUNO Q provides access to a curated library of NPU-optimized AI models through Arduino App Lab and Qualcomm® AI Hub.
Supported AI workloads include:
- Qwen and Gemma 4 LLMs and VLMs
- Whisper ASR
- Melo TTS
- Piper
- MediaPipe gesture recognition
- YOLO-X object tracking
- Keyword spotting
2. Bring Your Own Model (BYOM)
Upload GGUF-format models from Hugging Face or Qualcomm AI Hub through the llama.cpp brick in Arduino App Lab and start building immediately.
3. Train Your Own Model (TYOM)
Use the integrated Edge Impulse Studio to train and quantize custom models optimized for the Dragonwing IQ8 NPU and deploy them directly into Arduino App Lab.
Arduino App Lab
Arduino App Lab provides an integrated development experience combining Linux applications, real-time operating system development, Python scripts, Arduino Sketches, and containerized AI models.
- Arduino Sketches
- Python scripts
- Containerized AI models
- Ready-to-use Arduino Apps
- Plug-and-play Bricks
- Pre-loaded AI models
Pre-loaded AI capabilities include object and human detection, anomaly detection, image classification, sound recognition, and keyword spotting.
From Prototype to Production
VENTUNO Q is built for industrial applications and is designed to move beyond prototyping. When a VENTUNO Q prototype is ready for commercial deployment, the same Dragonwing IQ8 architecture can be carried forward to production-certified System-on-Modules from select Dragonwing IQ8 certified partners.
The software stack, AI models, and application logic developed on VENTUNO Q can carry forward without requiring hardware re-architecture.
Technical Specifications
| Processor | Qualcomm Dragonwing™ IQ8 (QCS8275)
|
|---|---|
| Microcontroller | STM32H5F5
|
| RAM | 2 × 8 GB LPDDR5 |
| Storage | 64 GB eMMC M.2 connector for NVMe Gen.4 external storage |
| Wireless Connectivity |
|
| Ethernet | 1 × 2.5 Gbit RJ45 Ethernet |
| USB |
|
| Camera |
|
| Video |
|
| Audio | 2 × Microphone IN / Headphone OUT / Ear OUT / Line OUT on JMISC header |
| CAN |
|
| Interfaces |
|
| Power Supply |
|
| Operating Temperature | Commercial Temperature Range: -10 °C to +60 °C (14 °F to 140 °F) |
| Dimensions | 160 × 100 × 25.8 mm |
Power Requirements
Use a PD-capable power supply of at least 50 W to start the board, or up to 65 W for full AI workloads and peripherals.
Power can be supplied through USB-C PD, barrel jack, or screw terminal within the specified voltage limits.
Why Choose Arduino VENTUNO Q?
- High-performance edge AI processing
- Up to 40 Dense TOPS NPU acceleration
- AI and real-time control on a single board
- 16 GB LPDDR5 RAM
- 64 GB industrial-grade eMMC storage
- Wi-Fi 6 and Bluetooth 5.3
- 2.5 Gigabit Ethernet
- Multiple camera interfaces for computer vision
- CAN-FD for industrial and automotive applications
- ROS 2 compatibility
- Linux and real-time OS capabilities
- Suitable for prototype-to-production development
Product Resources
For detailed technical information, refer to the official Arduino VENTUNO Q datasheet.
Need Help?
If you need assistance selecting the Arduino VENTUNO Q for your project or require technical information, please contact the Indus Technologies team.
Ideal for: Robotics | Industrial Automation | Edge AI | Computer Vision | IoT | Embedded Systems | Research & Development
| Manufacturer | Arduino |
|---|---|
| Microprocessor (MPU) | Qualcomm Dragonwing™ IQ8 (QCS8275): Octa-core Qualcomm®️Kryo™ Gen 6 CPU Qualcomm®️ Adreno™ 623 GPU Qualcomm®️ Hexagon™ Tensor AI Processor (NPU): up to 40 Dense TOPS Qualcomm Spectra™ 690 ISP |
| Microcontroller (MCU) | STM32H5F5: Arm® Cortex® M33 at 250MHz 4MB flash 1.5MB RAM |
| RAM | 2x8GB LPDDR5 |
| Storage | 64GB eMMC , M.2 connector for NVME Gen.4 external storage |
| USB | 1× USB-C port with host/device role switching, power role switch and video output 2x USB 3.0 Type A 2x USB 3.0 on JOMEGA header |
| Power Supply | ∙From USB-C connector 5 VDC max 5.5x2.1 mm Power Jack 12-24 VDC ∙Screw Terminal 7-24 VDC ∙7-24 V on JOMEGA Note: Use a PD-capable power supply of at least 50 W to start, or up to 65 W for full AI workloads and peripherals, through USB-C PD, barrel jack, or screw terminal within stated limits. |
| Connectivity | Wi-Fi® 6 2.4/5/6 GHz with onboard antenna Bluetooth® 5.3 with onboard antenna 1x 2.5Gbit RJ45 Ethernet |
| Interfaces | I2C/I3C SPI PWM UART 4× RGB user-controllable LEDs 8x13 Blue LED Matrix 1x Qwiic connector voltage 3V3, I2C 1x User push-button 1x reset button JCTL: MPU Remote Debug connector JTAG port on JOMEGA |
| Video | 1x HDMI muxed with MIPI DSI on JMEDIA header , Video output (DP Alt mode) support via USB-C , MIPI DSI pins on JMEDIA header |
| Audio | 2x Microphone IN / Headphone OUT / Ear OUT / Line OUT on JMISC header |
| Extra | Commercial Temperature Range: -10 °C to +60 °C (14 °F to 140 °F) |
| Dimensions | 160x100x25.8 mm |
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