Ubuntu Blog

As designers working at Canonical, we’re always thinking about open source. We believe that encouraging more designers to contribute to open source  benefits everyone, from the project maintainers to the end users themselves. In the 2025 edition of FOSSBackstage conference, we presented our research findings on  why designers don’t get involved in open source projects and found a particular breakdown between designers and project maintainers.

Canonical’s security philosophy has always been built on the premise that vulnerabilities exist and will be discovered. Our response relies on defense-in-depth architecture, rapid patch deployment, and strict adherence to Coordinated Vulnerability Disclosure (CVD). AI changes vulnerability discovery volume and speed. We have a robust vulnerability management process that is backed by rigorous compliance certifications.

Welcome to this blog series which explores innovative uses of Ubuntu Core. Throughout this series, Canonical’s Engineers will show what you can build with this Core 26 release, highlighting the features and tools available to you. In this first blog, Farshid Tavakolizadeh, Engineer Manager for Canonical’s Industrial team, will show you how to try Ubuntu Core 26 inside a virtual machine and turn it into a local AI inference appliance using Multipass and the gemma4 snap.

This year we celebrate a decade of Ubuntu Server support on the s390x architecture: marking a long-standing collaboration between Canonical and IBM that began at LinuxCon 2015. The first release happened on April 21, 2016, bringing Ubuntu 16.04 LTS (Xenial Xerus) to IBM Z and IBM LinuxONE platforms. That release was a significant milestone: for the first time, enterprises could deploy the vast Ubuntu ecosystem directly on IBM hardware.

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

Previous articles walked through RDMA (Remote Direct Memory Access) as a programming model and InfiniBand as the fabric that was built around it. Both led to the same conclusion, even if it was never stated outright: moving data, not compute, becomes the bottleneck once systems scale. So what happens when you want RDMA, but you’re already running an Ethernet network you’re not keen to replace? That’s usually where RDMA over Converged Ethernet (RoCE) enters the conversation.

Launch sandboxed development environments that are composable and repeatable with a single command. Innovate with agentic workflows while keeping your host system isolated. Repeatable environments, from a single file Workshops are defined through YAML files, allowing them to be recreated on different machines, without spending time on complex configurations. Add in SDKs to your workshop with only a few lines, including Ollama, OpenCode, NVIDIA CUDA, AMD ROCm, Intel OpenVino, and ROS 2.

June 5, 2026, 2:53pm 1 Hello Ubuntu Desktop community, With Ubuntu 26.04 LTS “Resolute Raccoon” now shipped and settling in, the Desktop Engineering team is already deep into the next development cycle. Ubuntu 26.10 “Stonking Stingray” arrives in October 2026. While non-LTS releases often focus on delivering new capabilities, they also play another important role: laying the foundation for the next LTS release.

Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike. With the tremendous cost of GPU clusters, extracting as much value as possible from the expense is critical. But in the pursuit of tokens, it’s important to remember that hardware efficiency isn’t the only factor influencing data center operating costs, or the output of useful, revenue-generating AI work.