Red Hat News

The complex risks facing modern IT environments make IT security a strategic imperative, not a back-end detail. Furthering this is cloud computing, which serves as the foundation of the AI economy, meaning that enterprises and nations require greater control, transparency, and assurance over data location and protection. Trust has become not just a technical question, but a matter of national policy, corporate strategy, and even societal resilience.At the same time, the explosion of AI and machine learning (ML) workloads is reshaping infrastructure requirements. But these shifts pose a complex

Red Hat Enterprise Linux 10.1 (RHEL) features some significant updates to RPM and DNF, two technologies designed to help you manage software installs and updates. The RPM package manager (RPM) creates installation files used to install and uninstall an application, and that can be queried for information about what libraries and binaries the application contains. The dnf command is the tool used on RHEL to search for available applications, and then to install, update, or uninstall them. These are important components of a computer system, so we've worked hard to improve them.RPM signature imp

Imagine that after 60 hours of training, a large language model (LLM) on an 8x NVIDIA H100 GPU cluster costing $55 an hour, your job fails at 90% completion. You must restart from your last checkpoint, which was saved 3 hours ago, wasting $165 in compute costs, and delaying model deployment. This kind of scenario isn't hypothetical, it's a daily reality for organizations running distributed AI training workloads in production environments. LLM training represents one of the most compute-intensive workloads in modern AI infrastructure. With GPU clusters costing thousands of dollars and training

The final quarter of the year is marked by accelerated innovation. Red Hat is leading the way by integrating AI into management, fortifying our security foundations, and readying your infrastructure for both present and future demands like quantum computing and highly scaled virtualization. This month brought significant platform updates across Red Hat Enterprise Linux (RHEL) and Red Hat OpenShift, along with key insights from our performance engineering teams. The following articles provide the essential technical and strategic information you need to make the most of your hybrid cloud journe

A lot of teams we work with are juggling 2 worlds at once: the Red Hat OpenShift projects that keep accelerating, and the previous hypervisor platforms or physical infrastructure that need to be kept to run the traffic management and security policies of the BIG-IP appliances.This gets the job done, but nobody enjoys maintaining 2 sets of infrastructure with 2 different lifecycles. It’s a tax we all pay because there hasn’t been a clean alternative.Now there is.F5 BIG-IP Virtual Edition (VE) is officially validated by F5 to support Red Hat OpenShift Virtualization and listed in the Red Hat

The open source AI ecosystem has matured quickly, and many developers start by using tools such as Ollama or LM Studio to run large language models (LLMs) on their laptops. This works well for quickly testing out a model and prototyping, but things become complicated when you need to manage dependencies, support different accelerators, or move workloads to Kubernetes.Thankfully, just as containers solved development problems like portability and environment isolation for applications, the same applies to AI models too! RamaLama is an open source project that makes running AI models in containe

This series takes a look at the people and planning that went into building and releasing Red Hat Enterprise Linux 10. From the earliest conceptual stages to the launch at Red Hat Summit 2025, we’ll hear firsthand accounts of how RHEL 10 came into being.Part 1 | Part 2 | Part 3In our previous installment of the story of how Red Hat Enterprise Linux (RHEL) 10 came to be, we got insights into the testing process and how the headline features (and the stories around those features) started coming together. In part 4, those stories come into clearer focus as the team works to put the finishing t

In today's modern technological world, IT infrastructure must deliver uncompromising speed and reliability to meet the critical demands of the business. Centralized automated platforms, such as Red Hat Ansible Automation Platform, are essential for managing critical IT infrastructure. These platforms streamline operations and increase efficiency by standardizing and scaling automation across the entire organization, transforming complex, repetitive tasks into a governed, security-focused, and repeatable enterprise strategy.The platform’s fundamental value lies in its resilience. For mission-

Scott and I talk to a lot of customers, and one theme that comes up over and over is that it’s difficult to plan for future releases of Linux. Sometimes, support drops for a feature or capability on which they rely. Other times, it’s that they can’t wait for a new feature, bug fix, and so on. Planning for Red Hat Enterprise Linux (RHEL) with its built-in digital roadmap, powered by Red Hat Lightspeed, is changing that. Product managers within RHEL now communicate directly with you about upcoming changes in the code base. This gives you more time to plan for changes, as well as clearer in

On December 16, 2025, Red Hat announced the acquisition of Chatterbox Labs, a pioneer in model-agnostic AI safety and generative AI (gen AI) guardrails.The following questions and answers provide additional context on the acquisition and what it means for Red Hat customers and partners.The AnnouncementWhat is the news? Red Hat announced on December 16, 2025, that it has acquired Chatterbox Labs, a private company founded in 2011 that provides model testing and gen AI guardrails. Chatterbox Labs technology, known as the AIMI platform, delivers quantitative AI risk metrics to help organizations

2025 proved to be dynamic in the world of enterprise IT - from the full-on emergence of AI agents to the renewed demands of sovereignty, there was no shortage of storylines throughout the year. But that’s looking behind us; when we look ahead, what do we expect for 2026?Several of Red Hat’s executives have taken on this question to provide their views into the new year, emphasizing the shifting mindsets and priorities that IT leaders are facing come January. So let’s hear from them!Ashesh Badani, senior vice president and chief product officer“Technology leaders are torn between many p

2025 was an incredible year for network operations (NetOps) and Red Hat Ansible Automation Platform. To address growing network complexity, Red Hat provided the tools NetOps teams needed to implement a strategic automation approach. Through major platform releases, new partner integrations, and global events, Red Hat helped customers achieve enterprise-wide network automation with Ansible Automation Platform. Let’s take a look back at the major network automation milestones that defined the year.Key platform innovations In October, Ansible Automation Platform 2.6 launched, introducing new ca

In the last article, we discussed how integrating AI into business-critical systems opens up enterprises to a new set of risks with AI security and AI safety. Rather than reinventing the wheel or relying on fragmented, improvised approaches, organizations should build on established standards and best practices to stay ahead of cybercriminals and other adversaries.To manage these challenges, enterprises need to adopt a formal approach by using a set of frameworks that map AI threats, define controls, and guide responsible adoption. In this article, we’ll explore the evolving AI security and

The adoption of Ray for scalable AI and ML workloads has skyrocketed. The Ray framework is powerful, but as the official documentation emphasizes, developers or platform providers are responsible for their own security.With Red Hat OpenShift AI, we are committed to providing a production-ready environment for complex AI workloads, and we recognize that robust security is important. That's why we're enhancing the existing controlled network environment (CNE) for Ray Clusters in OpenShift AI 3.0 and delivering that natively with KubeRay. CNE is an opinionated, platform-enforced policy that strea

Demystifying llm-d and vLLM: The race to productionAs organizations accelerate the journey to production for large language model (LLM) workloads, the ecosystem of open source tools is growing fast. Two powerful projects—vLLM and llm-d—have recently emerged to tackle the complexity of inference at scale. Learn more Forbes - Open Source And Orchestration Will Define Enterprise AI Leadership In 2026Red Hat’s CEO Matt Hicks discusses how enterprise AI leadership in 2026 will hinge on open source adoption and orchestration capabilities. This shifts the focus from model performance to contr

Every team that starts experimenting with generative AI (gen AI) eventually runs into the same wall: scaling it. Running 1 or 2 models is simple enough. Running dozens, supporting hundreds of users, and keeping GPU costs under control, is something else entirely.Teams often find themselves juggling hardware requests, managing multiple versions of the same model, and trying to deliver performance that actually holds up in production. These are the same kinds of infrastructure and operations challenges we have seen in other workloads, but now applied to AI systems that demand far more resources

NOTE: This blog has been updated to announce support for additional supported third-party model providers for the Red Hat Ansible Lightspeed intelligent assistant. Additional testing and validation of new model providers is ongoing. For the most recent list of supported model providers, please refer to Red Hat's official documentation. Earlier this year, we released the Red Hat Ansible Lightspeed intelligent assistant, a generative AI service which delivers an intuitive chat assistant embedded within Ansible Automation Platform. The Ansible Lightspeed intelligent assistant is like having an An

For years, my career in cybersecurity was defined by a sense of urgency and criticality. As a leader of incident response teams, I lived on the front lines, constantly reacting to the latest software vulnerabilities, cyberattacks, and anomalies. My days were a blur of alerts, patch deployments, and the relentless pressure to mitigate risk and restore operations. It was a challenging, high-stakes environment where every vulnerability felt like a direct threat.Now, I've traded the immediate firefight for a more proactive battlefield as a manager within Red Hat Product Security. This has given me

Developers are accustomed to the cloud, where a virtual machine (VM) can be launched in seconds. But in many enterprises, especially in regulated industries, requesting and receiving a VM can take a staggering 60 to 90 days. This kind of delay can stifle innovation and slow down critical projects. But what if you could provide your developers with a more seamless, self-service experience that delivers a fully configured VM in under an hour, with automated lifecycle management? This isn't a far-off dream, it's a reality you can build with Red Hat's integrated toolset.3 pillars of a modern IT se

Successfully delivering automation demands technical excellence, quality code, and reliable execution. However, scaling this success requires translating those technical wins into measurable business impact that can be communicated to leadership or integrated into business strategy. Red Hat Ansible Automation Platform offers two different capabilities that help practitioners and leaders turn operational metrics into a clear, auditable business case for expansion: Automation dashboard and automation analytics.Automation dashboard: On-premise visibilityThe automation dashboard provides a compreh