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Don't forget to register for Red Hat SummitRegistration is now open for Red Hat Summit 2026 in Atlanta! Register by February 23 for the lowest rates, or save further with group discounts for three or more attendees from your organization. Secure your spot now for the best value. Learn more Red Hat AI tops MLPerf Inference v6.0 with vLLM on Qwen3-VL, Whisper, and GPT-OSS-120BEnterprises use MLPerf to evaluate AI workload performance by comparing hardware and software stacks in a standardized environment. These results illustrate Red Hat's ability to match or outperform other inference engines

In a previous article, we focused on the capability that turns large language models (LLMs) from general-purpose tools into instruments of research through domain-specific customization. Fine-tuned models are how research teams encode domain expertise, institutional research, and reasoning patterns into systems that can help accelerate discovery rather than simply assist it.But customized models are only one half of the equation. For those models to become useful at institutional scale, they need a platform that can be used to train, serve, govern access to, and integrate them into the broader

The release of Red Hat Ansible Automation Platform 2.6 marks a pivotal milestone. Before you begin your upgrade, there are 3 key things you need to know to make your transition smoother:This is the last version with an RPM-based installer. Red Hat Ansible Automation Platform 2.6 using the RPM method is only available for Red Hat Enterprise Linux (RHEL) 9, and the RPM installer will be retired after this release. Ansible Automation Platform 2.7 will only support a containerized install method, the Red Hat OpenShift operator, or our cloud services, so now is the time to begin the transition.Ansi

This year, Red Hat Summit 2026 is headed to Atlanta for an experience that’s more hands-on and interactive than ever! Whether you’re a long-time attendee or joining us for the first time, this year’s event promises new ways to move from “what if” to “what’s next.” We’ve designed 2026’s program to help every attendee—from IT leaders to hands-on practitioners—sharpen their skills and bring open source innovation to life.Explore the expo hall The expo hall is your hub for 1-on-1 access to the experts building the future of IT. This year, we’re introducing lightning labs:

The preview release of Claude Mythos presents a massive challenge for IT security experts, as well as an opportunity (at least for the organizations that can afford it). Mythos represents a new category of frontier model that can not only identify complex memory safety issues and logic flaws hidden in legacy code but also exploit them in increasingly sophisticated ways. This dramatically compounds and expands the outsize role currently played by AI-driven vulnerability scanning both in corporate IT security teams and open source communities. Mythos, however, represents more than a deluge of AI

Model Context Protocol (MCP) servers often execute code or commands as instructed by an AI agent, exposing them to various risks. To help mitigate these risks, you should implement strict runtime security measures to contain what the server can do and to sanitize what it processes.As discussed in our previous blog post, MCP security: Implementing robust authentication and authorization, an important aspect of MCP security is the ability to monitor autonomous agent behaviour and identify potential threats in real-time. By maintaining a detailed audit trail of tool invocations, authentication ev

Once organizations move beyond experimenting with a small handful of large language models (LLMs), the limits of manual model deployment become clear. What may work for early testing and development quickly turns inefficient, expensive, and difficult to scale. As the number of models, variants, and versions grow, teams are left not only managing increasing operational complexity, but also determining which GPU resources are the best fit for each workload.This challenge often turns into a kind of hardware-model Tetris. Most enterprises operate with a diverse mix of GPU infrastructure, from cutt

We recently announced the general availability (GA) of managed identity and workload identity for Microsoft Azure Red Hat OpenShift clusters. With this, users benefit from short-lived, limited permission credentials that enhance security and reduce operational overhead that may otherwise come with longer lived credentials such as service principals.Now, we’d like to call attention to a significant enhancement to the cluster creation process. A fully integrated portal experience for deploying managed identity-based Azure Red Hat OpenShift clusters is now available.Simplicity and speed: Deploy

Scientific research has a compute problem. Not a shortage of ideas, but a shortage of infrastructure that can keep up with them. It also has a platform problem—how can we deliver generative AI (gen AI) capabilities through the right architecture and operating model so institutions can provide access without handing every user unrestricted access to expensive models and runaway spend.What are small language models?Small language models (SLMs) are usually domain-specific AI models developed with significantly fewer parameters than their massive counterparts, typically ranging from 1 billion to

Legacy software doesn't retire itself. It sits in production, accumulating technical debt, resisting change, and quietly becoming a risk—not because of what it does, but because of what it can no longer support.That's the challenge facing leading systems integrators (SIs) working in support of government and industry. Across a portfolio of mission-critical applications, SIs and aerospace companies are managing aging Python and Java codebases that need to move to a modern, security-focused, and supportable foundation—specifically Red Hat Enterprise Linux 10 (RHEL 10).The goal isn't just a s

Many SAP environments are deployed in a hybrid landscape where applications may run on-premise, or on Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), or Software-as-a-Service (SaaS). Red Hat has three product solutions to support SAP workloads including Red Hat Enterprise Linux (RHEL), Red Hat Ansible Automation Platform, and Red Hat OpenShift. In this blog I demonstrate how these solutions are used in a hybrid landscape to run, simplify, and expand SAP capabilities.The following diagram depicts an example hybrid SAP landscape, with each number marker explained in following s

Red Hat Enhances Enterprise Stability with Red Hat Enterprise Linux Extended Life Cycle, PremiumRed Hat launched Red Hat Enterprise Linux Extended Life Cycle Premium, offering a predictable 14-year life cycle for major releases. This new subscription simplifies support for mission-critical, change-averse workloads, providing organizations in highly regulated industries with greater operational certainty and long-term infrastructure stability. Learn more Red Hat and Google Cloud Expand Collaboration to Accelerate Application Modernization and Cloud Migration with Red Hat OpenShiftRed Hat and

One of the strengths of Red Hat Ansible Automation Platform is its flexible automation of an array of use cases across ITOps. It includes multiple options to help you jumpstart new automation projects, using Ansible Content Collections. With Ansible Content Collections, you can access more than 200 Red Hat Ansible-certified and validated collections, built and delivered by partners and Red Hat so you can automate more quickly. In this blog post, you’ll learn about new and updated content for some of the most common use cases. So, let’s jump into it!Comprehensive Microsoft Windows automatio

The promise of large language models (LLMs) is clear. From code generation to customer support, from document analysis to creative workflows, organizations everywhere are racing to integrate LLMs into their products and operations. The enterprise LLM market is projected to grow from $6 billion in 2025 to over $50 billion by 2035. But behind the excitement lies a practical challenge—serving LLMs in production can be expensive, inefficient, and operationally complex.The production scale challengesInference cost is the real billThere's a common misconception that training is where most of the m

Red Hat is proud to announce industry-leading results from the latest MLPerf Inference v6.0 benchmarks, achieved through deep engineering co-design with NVIDIA. These results demonstrate that when you combine Red Hat’s open-source leadership with NVIDIA’s leading AI infrastructure, the result is a versatile, proven platform ready for any enterprise inference workload—from vision and speech to complex reasoning.Our latest submissions focused on maximizing the potential of the NVIDIA HGX H200 and NVIDIA HGX B200 systems, proving that software optimization is just as critical as raw horsepo

For organizations managing mission-critical workloads, long-term stability and predictability are not just preferences—they are necessities. Recognizing this need, Red Hat is proud to introduce Red Hat Enterprise Linux (RHEL) Extended Life Cycle, Premium, a new, stand-alone offering designed to provide easily consumable, significantly longer life cycles for your most vital systems.This premium offering builds upon the solid foundation of the RHEL Premium subscription, extending maintenance beyond the traditional ten year timeline. It is an indispensable solution for highly regulated industri

As the first quarter of 2026 comes to a close, we have seen a shift in the role of network automation. It's no longer a "nice to have" and instead is a critical support for AI-driven workloads, edge computing, and hybrid cloud environments. For network operations (NetOps) teams, there has been a transition from managing individual devices to orchestrating entire service delivery frameworks.The momentum we've seen this past quarter highlights that Red Hat Ansible Automation Platform is the standard trusted execution layer for this transformation. Organizations are moving away from isolated team

Most leaders I speak with are well past the hype cycle of AI. The question is no longer whether AI matters. The question is how to move from experimentation to production in a way that is security-focused, supportable, and repeatable across teams.From where I sit—leading strategy and operations for AI Platform Core Components (AIPCC), an engineering function within Red Hat’s AI Engineering organization—that shift changes everything. The conversation moves from a tooling decision to an operating model decision. A strong AI platform is the foundation that helps teams ship AI-enabled capabi

Red Hat is proud to announce our strong results from the latest industry-standard MLPerf Inference v6.0 benchmark. Our submission includes four AI workloads (Whisper-Large-v3, GPT-OSS-120B, Qwen3-VL-235B-A22B, and Llama-2-70b) on NVIDIA (H200, B200, L40S) and AMD (MI350X) GPUs, running on Red Hat Enterprise Linux (RHEL) and Red Hat OpenShift AI with our open source inference stack: vLLM, and llm-d. We achieved top scores across several configurations, including the highest offline throughput on B200 for GPT-OSS-120B, the leading H200 result on Whisper, and the top B200 submission on Qwen3-VL,

As AI workloads move from experimental prototypes into production environments, enterprises face a familiar challenge—how do you protect, manage, and govern these new components with the same rigor you apply to traditional software applications? A key piece of the puzzle lies in something your organization likely already uses extensively—containers, specifically Open Container Initiative (OCI) containers.What is the Open Container Initiative?The Open Container Initiative defines open specifications for image formats, container runtimes, and distribution, helping organizations avoid vendor