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Raleigh (North Carolina), USA — On March 27, Red Hat, Inc., which stands as the global leader in providing open-source solutions, unveiled new enhancements to their product line called Red Hat AI. This suite is tailored to expedite both the creation and implementation of artificial intelligence technologies throughout various hybrid cloud settings. The updated offering from Red Hat includes an advanced AI platform aimed at boosting productivity during model training and inference stages. It promises greater operational efficiency, user-friendly processes, and adaptability to be deployed seamlessly across multiple hybrid cloud infrastructures.

Despite efforts from companies to minimize expenses associated with implementing extensive large language models (LLMs) to handle an increasing range of applications, these organizations continue to grapple with the difficulty of merging such models with their exclusive datasets that fuel these applications. They must ensure accessibility to this information regardless of where it resides—be it within a data center, through multiple public cloud platforms, or out at the network’s periphery.

Red Hat AI encompasses both Red Hat OpenShift AI and Red Hat Enterprise Linux AI (RHEL AI). It tackles these issues by offering an enterprise-level AI platform designed to help users implement more effective and streamlined models. These models are tailored using specific business-related data and can subsequently be deployed across various hybrid clouds for extensive use in training as well as inference processes on multiple high-performance computing setups.

Red Hat OpenShift AI

Red Hat OpenShift AI offers a comprehensive AI platform designed to handle the entire lifecycle of both predictive and generative AI (Gen AI) processes across various hybrid clouds. This includes functionalities like machine learning operations (MLOps) and LLMOps features. It equips you with the means to develop predictive models as well as fine-tune Gen AI models. Additionally, it comes equipped with user-friendly tools aimed at streamlining all aspects of AI model management—from initial data science tasks through pipeline development and ongoing model monitoring right up to regulatory compliance and beyond.

The newest version of Red Hat OpenShift AI, Release 2.18, introduces various enhancements and functionalities aimed at bolstering Red Hat AI’s objective to deliver highly refined and resource-efficient AI models across hybrid clouds. Notable additions encompass:

* Distributed Serving: Offered via the vLLM inference server, this approach allows IT teams to divide model serving tasks among several graphics processing units (GPUs). By doing so, it reduces strain on individual servers, accelerates both training and tuning processes, optimizes computational resource usage, and facilitates service distribution across various nodes for AI models.

• A comprehensive model refinement process: This latest addition leverages InstructLab along with Red Hat OpenShift AI data science workflows to ease the adjustment of Large Language Models (LLMs). It enhances scalability, efficiency, and auditability within extensive operational settings and provides control via the Red Hat OpenShift AI interface.

* AI Safety Measures: The latest version of Red Hat OpenShift AI 2.18 enhances LLM precision, efficiency, response time, and clarity with a new feature called AI Guardrails. This tech-preview allows monitoring and bolstering protection for user inputs as well as model outcomes. AI Guardrails provides extra checkpoints to assist IT staff in recognizing and addressing possibly offensive, harmful, or inappropriate content, sensitive personal details, proprietary info, or other restricted material according to company guidelines.

* Model Evaluation: Leveraging the language model evaluation (lm-eval) tool offers crucial insights into the model’s overall effectiveness. This feature allows data scientists to assess how well their large language models perform across multiple tasks such as logical reasoning, mathematical problem-solving, and handling adversarial inputs in natural language processing. In turn, this aids in developing smarter, more interactive, and personalized artificial intelligence systems.

RHEL AI

As part of the Red Hat AI suite, RHEL AI serves as a foundational platform for developing, testing, and deploying large language models (LLMs) to support business applications. It offers customers access to Granite LLMs along with InstructLab model alignment tools. These resources come bundled as a bootable Red Hat Enterprise Linux server image, enabling deployment throughout various environments within the hybrid cloud infrastructure.

Introduced in February 2025, RHEL version 1.4 brought multiple new improvements such as:

* Support has been added for the Granite 3.1 8B model as part of the newest inclusion into the open-source licensed Granite model series. This edition introduces multilingual capabilities for inference and allows for tailored taxonomies and knowledge personalization (in developer preview). Additionally, it features a 128K token context window aimed at enhancing summary outcomes and performance in retrieval-augmented generation (RAG) processes.

* A fresh graphical user interface designed for skill and knowledge submissions, offered as part of a developer preview, aimed at streamlining data intake and segmentation along with enabling users to incorporate their personal skills and inputs into an AI model.

* Document Knowledge-bench (DK-bench) designed to facilitate simpler comparisons between AI models that have been fine-tuned using pertinent private data and their respective base models without tuning.

Red Hat AI InstructLab on IBM Cloud

More businesses are seeking AI solutions that emphasize precision and data protection, all while minimizing expenses and intricacies. The Red Hat AI InstructLab offered as a cloud-based service via IBM Cloud aims to streamline processes, expand capabilities, and enhance security measures for both training and deploying AI models. This makes it easier to fine-tune the InstructLab models, enabling companies to create highly effective systems customized to their specific requirements without relinquishing control over their information.

No-cost AI Foundations training

Artificial intelligence represents a revolutionary chance that is reshaping enterprise functioning and competition methods. In order to assist businesses navigate this ever-changing environment, Red Hat has introduced free-of-charge online training courses under their AI Foundations program. They offer two distinct AI certification programs tailored for seasoned executives as well as those new to AI, aimed at educating individuals across various skill levels about leveraging AI to revolutionize operational processes, expedite decision making, and foster innovation. These educational resources instruct participants on effectively utilizing their understanding within the scope of Red Hat’s AI solutions.

Availability

Red Hat OpenShift AI 2.18 and Red Hat Enterprise Linux AI 1.4 are now widely accessible. For details about extra functionalities, enhancements, bug corrections, and instructions on upgrading to the most recent release of Red Hat OpenShift AI, please visit their website for more insights.
here
The most recent version of RHEL AI can be located.
here.

The upcoming release of Red Hat AI InstructLab on IBM Cloud is scheduled for shortly. Customers can currently access AI Foundations training provided by Red Hat.

Supporting Quotes

Joe Fernandes, Vice President and General Manager of the AI Business Unit at Red Hat, stated, “As organizations increasingly adopt generative AI solutions into production and operate them at larger scales, managing costs becomes critical. Additionally, companies must tackle issues related to merging AI models with internal corporate information and ensure deployment flexibility irrespective of where their data resides. To help businesses overcome these hurdles, Red Hat’s AI platform supports deploying optimized, custom-trained models directly onto company infrastructure—whether on-site, in public clouds, or at the network edges.”

Regis Lesbarreres, who serves as the advanced analytics and AI innovation manager at Airbus Helicopters within their digital innovation division, stated: “When we embarked on our AI initiative, Airbus Helicopters aimed to incorporate AI into our current infrastructure, minimize unauthorized IT activities, bring together our data science team onto one unified AI platform, and streamline expenses efficiently across various operations. By utilizing Red Hat OpenShift AI, we have successfully achieved each of these targets, leading to our initial practical application of AI technology. The strategic outlook of Red Hat regarding AI matches perfectly with our corporate aims, enabling us to reach our objectives while ensuring adaptability, ease of access, and clear visibility.”

Javier Olaizola Casin, Worldwide Managing Partner,
Hybrid Cloud
And Data, IBM Consulting states, “Enterprises are progressively leveraging AI to revamp fundamental business operations, requiring adaptable, budget-friendly, and reliable AI solutions aligned with trusted corporate data to address specific demands. Red Hat AI offers the uniformity, dependability, and efficiency needed for enterprises to develop and release AI models and apps throughout mixed-cloud settings. By integrating IBM Consulting’s sector-specific knowledge and technological proficiency with Red Hat’s AI tools, we assist our clients in maximizing returns on their tech expenditures.”

Torsten Volks, Principal Analyst of Application Modernization at ESG, states: “Top-performing companies utilize AI-powered, data-focused decision-making across various teams and departments. Consequently, the capacity to swiftly and economically create, launch, incorporate, expand, and oversee AI functionalities throughout the organization emerges as a vital element for success. Developing such proficiency necessitates an adaptable and extensive AI infrastructure that guarantees smooth interaction with current systems and procedures, enhances operational flexibility, and maintains ongoing oversight. Empowering employees and clients to leverage AI benefits promptly and extensively remains essential for sustained corporate achievement.”

Anand Swamy, Executive Vice President and Global Head of Ecosystems at HCLTech, states, “To fully harness the power of generative AI, companies must focus on setting up adaptable and nimble infrastructures. Through integrating the comprehensive features of Red Hat AI—which includes both RHEL AI and Red Hat OpenShift AI—to provide a complete AI app platform, along with HCLTech’s advanced AI knowledge and cognitive infrastructure services—a component of HCLTech AI Foundry—clients gain a seamless route for tapping into AI-driven advancements. This helps address typical hurdles like ensuring data safety, expanding AI tasks efficiently, and reducing infrastructure expenses.”

Additional Resources

* Learn more about
Red Hat AI

* Explore
Red Hat’s AI portfolio

* Read more about
Red Hat AI training

Red Hat stands as the premier supplier of open-source enterprise software solutions globally, leveraging a collaborative community model to offer robust and high-performance Linux systems, hybrid clouds, containers, and Kubernetes technologies. They assist clients in merging new and current IT applications, developing cloud-based programs, adopting their top-tier operating system, and streamlining, securing, and managing intricate ecosystems. Their acclaimed support, education, and advisory services further cement Red Hat’s position.
reliable consultant for top corporations
As a strategic collaborator with cloud service providers, systems integrators, software suppliers, clients, and open-source groups, Red Hat assists enterprises in getting ready for the digital future.

Forward-Looking Statements

Apart from the historical data and discussions included here, this press release might include forward-looking statements under the Private Securities Litigation Reform Act of 1995. Such predictions rely on the company’s present expectations about its future operational and economic outcomes. Various elements can lead to real outcomes differing significantly. Each forward-looking statement presented today reflects solely the views at the time it was shared; however, unless mandated by legislation, the firm does not commit itself to updating or modifying these projections in light of new developments.

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