Page 18 - Red Hat PR REPORT - MAY-JUNE 2025

Press Release

access inference with vLLM, retrieval-augmented generation (RAG), model evaluation, guardrails
and agents, across any gen AI model. MCP enables models to integrate with external tools by
providing a standardized interface for connecting APIs, plugins and data sources in agent
workflows.

The latest release of Red Hat OpenShift AI (v2.20) delivers additional enhancements for
building, training, deploying and monitoring both gen AI and predictive AI models at scale. These
include:
• Optimized model catalog (technology preview) provides easy access to validated Red
Hat and third party models, enables the deployment of these models on Red Hat OpenShift
AI clusters through the web console interface and manages the lifecycle of those models
leveraging Red Hat OpenShift AI’s integrated registry.
• Distributed training through the KubeFlow Training Operator enables the scheduling
and execution of InstructLab model tuning and other PyTorch-based training and tuning
workloads, distributed across multiple Red Hat OpenShift nodes and GPUs and includes
distributed RDMA networking–acceleration and optimized GPU utilization to reduce
costs.
• Feature store (technology preview), based on the upstream Kubeflow Feast project,
provides a centralized repository for managing and serving data for both model training
and inference, streamlining data workflows to improve model accuracy and reusability.

Red Hat Enterprise Linux AI 1.5 brings new updates to Red Hat’s foundation model platform
for developing, testing and running large language models (LLMs). Key features in version 1.5
include:
• Google Cloud Marketplace availability, expanding the customer choice for running Red
Hat Enterprise Linux AI in public cloud environments–along with AWS and Azure–to help
simplify the deployment and management of AI workloads on Google Cloud.
• Enhanced multi-language capabilities for Spanish, German, French and Italian via
InstructLab, allowing for model customization using native scripts and unlocking new
possibilities for multilingual AI applications. Users can also bring their own teacher models
for greater control over model customization and testing for specific use cases and
languages, with future support planned for Japanese, Hindi and Korean.

The Red Hat AI InstructLab on IBM Cloud service is also now generally available. This new
cloud service further streamlines the model customization process, improving scalability and user
experience, empowering enterprises to make use of their unique data with greater ease and control.

Red Hat’s vision: Any model, any accelerator, any cloud.
The future of AI must be defined by limitless opportunity, not constrained by infrastructure silos.
Red Hat sees a horizon where organizations can deploy any model, on any accelerator, across any
cloud, delivering an exceptional, more consistent user experience without exorbitant costs. To
unlock the true potential of gen AI investments, enterprises require a universal inference platform–
a standard for more seamless, high-performance AI innovation, both today and in the years to
come.