Ubuntu on Qualcomm® IoT Platforms
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Scale AI at the edge faster with Ubuntu on Qualcomm® IoT Platforms

Ubuntu on Qualcomm® IoT Platforms emerges from a strategic collaboration between Qualcomm Technologies and Canonical, delivering optimized Ubuntu images specifically for Qualcomm IoT platforms. This initiative is designed to cater to a wide array of device applications at the intelligent edge.
 

This product delivers an Ubuntu OS experience, optimized for the IoT market and intelligent edge applications. It provides developers with a robust, user-friendly environment supported commercially by Canonical, ensuring seamless integration and enhanced performance for a wide range of device applications. This offering is an enterprise-grade release with regular security updates and maintained by Canonical with backing from Qualcomm Technologies.
 

Benefit from seamless scalability and enhanced functionality with access to the comprehensive Ubuntu ecosystem, from edge computing solutions to cloud services.

Empowering developers with Qualcomm SoCs & Ubuntu

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Empowering developers with Qualcomm SoCs & Ubuntu

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Discover how Ubuntu on Qualcomm® IoT Platforms can transform your development process and optimize your edge solutions.

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Accelerate time-to-market
Gain from not having to create and support your own OS, reducing engineering efforts and costs, having access to open-source frameworks and allowing you to channel your investments into enhancing your unique value proposition.
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Leading edge AI
Utilize optimized software to leverage hardware accelerators, quantized AI models with Qualcomm® AI Hub, and leverage advanced features on the Qualcomm® IoT platforms. Select between upstream open source or optimized drivers based on your requirements.
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Meet cybersecurity regulatory requirements
Providing multiple OS updates to fulfill a 15-year product lifespan expectation. Over-the-air (OTA) updates include regular security enhancements to comply with regulatory standards.
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Utilize the extensive Canonical developer’s ecosystem
Leverage a vast developer ecosystem built over decades of collaboration with developers. Utilize tools and services from Canonical to orchestrate and manage deployments from the edge to the cloud."

Software Block Diagram

Software Roadmap**

 

  Q4 25 Q1 26 Q2 26
  Oct '25 - Dec '25 Jan '26 - Mar '26 Apr '26 - Jun '26
Dev Board Support
SoC
OS
Ubuntu 24.04 LTS (QCOM optimized)
Ubuntu 24.04 LTS (QCOM optimized) | 25.10 nightly* Ubuntu 24.04 LTS (QCOM optimized) | 26.04 LTS*
Details
  • Updates to supported HW and SoCs
    • AI SDK | Robotics SDK | Real Time Optimization
  • Oct: Available Real Time(RT) kernel for QCS6490

  • Dec: Certified IQ-9075 EVK
    • Ubuntu Desktop | Server (Core Kit, GMSL Mezz)
    • AI SDK Ubuntu Server | Robotics SDK
  • Updates to supported HW and SoCs

  • AI SDK | Robotics SDK | Real Time Optimization

  • Feb: AI SDK Ubuntu Desktop

  • Feb: Available Real Time(RT) kernel for QCS6490, IQ9075

  • Feb : Early Release* IQ-X7181
    • Ubuntu Desktop | Ubuntu Server
    • AI SDK Ubuntu
  • Updates to supported HW and SoCs
    • AI SDK | Robotics SDK | Real Time Optimization
  • Mar: Public Beta IQ8275
    • Ubuntu Desktop | Ubuntu Server (Core Kit)
    • AI SDK
  • Apr: Beta* IQ-X7181
    • Ubuntu Desktop | Ubuntu Server
    • AI SDK Ubuntu Server
  • May: Early Release* IQ-X5121
    • Ubuntu Desktop | Ubuntu Server
    • AI SDK Ubuntu Server 

*Future mass production rate

**As of January 2026 and subject to change without notice

Software Roadmap

*See full list of Development Kits and Hardware SoCs

Verticals

Industrial

Robotics

Retail

Healthcare

Energy

Smart Cities

Use Cases

IoT endpoints and management

Robotics fleet telemetry

Secure and scale on device AI deployments

Edge AI Box

Discover Canonical key related articles

Embedded AI workflow

Dragonwing RB3 Gen 2 development kits are supported by platforms and SDKs such as Edge Impulse, Qualcomm AI Hub, and Foundries.io, for a streamlined AI model workflow, and Qualcomm Intelligent Multimedia (IM) SDK for a simplified integration of video and audio streams. The AI workflow includes AI model selection, raw data acquisition, model training/refining and benchmarking, and deployment onto multiple edge devices in production.

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