Qualcomm unveils Arduino Ventuno Q, a powerful AI board designed to bring robotics offline

Qualcomm and Arduino Launch Ventuno Q for Offline AI and Robotics Applications

Qualcomm has introduced the Arduino Ventuno Q, a single-board computer that merges AI processing with robotics control in a compact platform designed to operate entirely without cloud connectivity. Following Qualcomm's acquisition of Arduino last year, this marks the chip designer's first major product combining its processor technology with Arduino's maker-focused philosophy 1

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. Named after the Italian word for "21," the device addresses a growing demand for edge computing solutions that can perceive, decide, and act autonomously in physical environments 2

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The platform represents a shift from cloud-dependent AI systems to fully autonomous edge AI workloads. "With Ventuno Q, AI can finally move from the cloud into the physical world," Qualcomm stated, emphasizing the board's ability to build machines that operate independently on a single device 5

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. This approach eliminates latency issues and data transmission requirements that typically constrain real-time robotics applications.

Dual-Brain Architecture Powers High-Performance Computing

The Arduino Ventuno Q employs what developers call a dual-brain board architecture, combining the Qualcomm Dragonwing IQ8 processor with a dedicated STM32H5 microcontroller for deterministic motor control 3

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. The Dragonwing IQ8 includes an 8-core ARM Cortex CPU, Adreno Arm Cortex A623 GPU, and a Hexagon Tensor NPU capable of delivering up to 40 TOPs of dense AI compute power 1

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. This processing capability enables simultaneous inference and complex AI workloads locally.

The board comes equipped with 16GB of LPDDR5 RAM and 64GB of eMMC storage, expandable through an M.2 NVMe Gen.4 slot 2

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. Connectivity features include Wi-Fi 6, Bluetooth 5.3, 2.5Gbps Ethernet, and USB camera support, providing comprehensive hardware compatibility for diverse robotics applications 1

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. The STM32H5 microcontroller ensures sub-millisecond, deterministic control for motors and sensors, allowing AI-driven decisions to translate into physical action instantly 3

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Ubuntu Partnership Delivers Seamless Development Experience

Through a strategic collaboration with Canonical, the Ventuno Q arrives with Ubuntu pre-installed, providing a production-ready foundation for developers 3

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. This integration enables developers to progress from unboxing to AI inference within minutes, eliminating traditional hurdles associated with complex board support packages and fragmented kernel drivers. The board also supports Debian Linux on its main processor and Arduino Core on Zephyr OS for real-time control 4

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"Our collaboration with Arduino and Qualcomm provides a production-ready starting line for innovators," said Cindy Goldberg, VP of Silicon Alliances at Canonical. "By providing a reliable Ubuntu foundation for VENTUNO Q, we ensure that a successful prototype can scale into a securely-designed industrial solution with 10 years of security maintenance" 3

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. This long-term support makes the platform viable for industrial automation projects that require extended deployment cycles.

Arduino App Lab Simplifies AI Model Deployment

Running natively on Ubuntu, the Arduino App Lab provides a unified development environment that blends Arduino sketches, Python scripts, and advanced AI capabilities into deployable applications 3

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. The platform includes pre-trained AI models for offline AI tasks including LLMs, VLMs, automatic speech recognition (ASR), gesture recognition, pose estimation, and object tracking 1

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. These models run entirely on-device without requiring external servers.

The board supports local LLMs powered by Qualcomm AI Hub, while Edge Impulse Studio allows developers to train custom models for specific use cases 4

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. This flexibility addresses both hobbyist experimentation and professional R&D requirements. The platform is also hardware-compatible with Arduino UNO shields, Modulino nodes, Qwiic sensors, and Raspberry Pi HAT expansions, enabling developers to leverage existing ecosystem components 2

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Target Applications Span Robotics, Smart Kiosks, and Machine Vision

The Ventuno Q targets fully autonomous systems that operate without cloud connectivity, including smart kiosks at transport hubs and healthcare assistants that handle sensitive data locally 4

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. Robotics applications include vision-guided pick-and-place robotic arms, service robots capable of following individuals across dynamic spaces, and autonomous machines navigating complex environments using Visual SLAM combined with path optimization 4

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The board also enables edge AI vision systems for proactive security monitoring, traffic flow analysis, and automated quality inspections with local visual language models 1

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. Fabio Violante, VP and GM at Arduino, emphasized that the platform enables "systems that don't just interpret the world, they interact with it," allowing machines to operate independently in physical environments 4

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Educational and Industrial Pathways Create Long-Term Opportunities

While optimized for the Arduino DIY maker market, the Ventuno Q incorporates industrial-grade specifications that provide a seamless transition path from prototype to production-ready industrial applications 3

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. The platform is designed for education and research in computer vision, generative AI, and prototyping at the edge, making it accessible to students and researchers exploring AI concepts 1

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Qualcomm's goal centers on making advanced robotics and edge AI accessible to every developer, educator, and innovator 5

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. This democratization approach could accelerate adoption of offline AI systems across sectors where cloud connectivity presents security, latency, or cost challenges. The Arduino Ventuno Q will be available in Q2 2026 from the Arduino Store and authorized resellers including DigiKey, Farnell, Mouser Electronics, and RS Components, with pricing expected under $300 4

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. This positions it as a more expensive but significantly more capable alternative to the current $59 Arduino Uno Q model 2

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