Red Hat unveils enhanced AI tools for hybrid cloud use

Red Hat has announced enhancements to its AI portfolio, which are designed to address enterprise needs in reducing AI costs, integrating private data, and scaling AI deployments across hybrid cloud environments.

The updated AI portfolio now includes advancements in Red Hat OpenShift AI 2.18, Red Hat Enterprise Linux AI 1.4, and the introduction of Red Hat AI InstructLab on IBM Cloud. These updates aim to streamline the operationalisation of AI strategies within enterprises.

Joe Fernandes, Vice President and General Manager of the AI Business Unit at Red Hat, highlighted the enterprise challenges in AI deployment. "Red Hat knows that enterprises will need ways to manage the rising cost of their generative AI deployments, as they bring more use cases to production and run at scale. They also need to address the challenge of integrating AI models with private enterprise data and be able to deploy these models wherever their data may live. Red Hat AI helps enterprises address these challenges by enabling them to leverage more efficient, purpose-built models, trained on their data and enable flexible inference across on-premises, cloud, and edge environments."

The latest release of Red Hat OpenShift AI, version 2.18, includes several updates. Distributed serving through the vLLM inference server allows IT teams to distribute model serving across multiple GPUs, enhancing resource efficiency. It also offers an end-to-end model tuning experience, AI Guardrails for improving LLM accuracy, and model evaluation support through the lm-eval component.

The Red Hat Enterprise Linux AI 1.4 update introduces Granite 3.1 with multilingual support, a new interface for knowledge contributions, and Document Knowledge-bench (DK-bench) to simplify AI model comparisons. Régis Lesbarreres of Airbus Helicopters shared the value of these enhancements, stating, "At the outset of our AI journey, Airbus Helicopters was looking to integrate AI into our existing architecture, reduce shadow IT, unite our data scientists under a singular AI platform and optimise costs at scale. With Red Hat OpenShift AI, we've been able to accomplish all of these goals, which led to our first business use case for AI. Red Hat's vision for AI aligns with our business objectives and allows us to meet them while maintaining flexibility, accessibility and transparency."

The introduction of Red Hat AI InstructLab on IBM Cloud is aimed at simplifying AI model training and deployment while enhancing data security. Javier Olaizola Casin, Global Managing Partner of Hybrid Cloud and Data at IBM Consulting, remarked, "Businesses are increasingly applying AI to transform core business processes, and they need AI solutions that are flexible, cost-effective and tuned with trusted enterprise data to meet their unique needs. Red Hat AI brings the consistency, reliability and speed that organisations need to build and deploy AI models and applications across hybrid cloud scenarios. Combining IBM Consulting's domain, technology and industry expertise with Red Hat's AI technologies, we are helping our clients drive ROI from their technology investments."

In addition to product updates, Red Hat is now offering AI Foundations online training courses at no cost. These are intended to educate both experienced leaders and AI newcomers on how AI can transform business operations. Anand Swamy, Executive Vice President and Global Head of Ecosystems at HCLTech, expressed the importance of flexible infrastructure in AI development, "In order to realise the full potential of generative AI, organisations need to prioritise agile and flexible infrastructure. By combining the capabilities of Red Hat AI, encompassing RHEL AI and Red Hat OpenShift AI to deliver an end-to-end AI application platform, with HCLTech's leading AI expertise and cognitive infrastructure services, a part of HCLTech AI Foundry solution, customers get a streamlined path to unlocking AI innovation, helping them overcome common challenges such as data security, scaling AI workloads and minimising infrastructure costs."