Red Hat, Inc., a leading provider of open source solutions, has announced updates to its Red Hat AI portfolio. These enhancements aim to support the development and deployment of AI solutions across hybrid cloud environments. The updated offerings include improvements to Red Hat OpenShift AI and Red Hat Enterprise Linux AI (RHEL AI), designed to help enterprises manage the costs and integration challenges associated with generative AI deployments.
Joe Fernandes, vice president and general manager of the AI Business Unit at Red Hat, emphasized the importance of efficient model management: “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.”
The updated Red Hat OpenShift AI platform offers new features such as distributed serving through vLLM inference server, an end-to-end model tuning experience using InstructLab, and a technology preview of AI Guardrails for improved model accuracy and safety. Additionally, it includes model evaluation tools for benchmarking language models.
RHEL AI provides a foundation for developing large language models (LLMs) tailored for enterprise applications. Recent enhancements include support for Granite 3.1 8B model with multilingual capabilities and a new graphical user interface for skill contributions.
Red Hat’s collaboration with IBM Cloud aims to simplify the training and deployment of secure AI models through Red Hat AI InstructLab. The company is also offering no-cost online training courses under its AI Foundations program.
Industry leaders have expressed support for these advancements. Régis Lesbarreres from Airbus Helicopters noted how Red Hat OpenShift AI helped integrate AI into their existing architecture while optimizing costs. Javier Olaizola Casin from IBM Consulting highlighted the consistency and reliability brought by Red Hat’s technologies in building flexible and cost-effective solutions.
Torsten Volks from ESG emphasized the importance of rapid development and deployment capabilities provided by an open and extensible foundation like Red Hat’s. Anand Swamy from HCLTech pointed out that combining Red Hat’s infrastructure with HCLTech’s expertise facilitates unlocking innovation while addressing common challenges like data security.
These updates reflect Red Hat’s commitment to enhancing enterprise capabilities in deploying efficient, scalable, and secure AI solutions across various environments.
