Red Hat Unveils AI 3 Platform, Bringing Distributed AI Inference to Enterprise Workloads
Red Hat, the world’s leading provider of open-source solutions, has announced Red Hat AI 3, the latest version of its enterprise-grade AI platform designed to streamline AI workflows and accelerate the transition from experimentation to large-scale production.
The new release integrates Red Hat AI Inference Server, Red Hat Enterprise Linux AI (RHEL AI), and Red Hat OpenShift AI into a single, unified platform that simplifies high-performance AI inference across hybrid cloud environments. With this, Red Hat aims to help enterprises overcome major AI adoption challenges such as cost control, data privacy, and model management.
> “As enterprises scale AI from experimentation to production, they face a new wave of complexity, cost and control challenges,” said Joe Fernandes, Vice President and General Manager of Red Hat’s AI Business Unit. “With Red Hat AI 3, we’re providing an open-source platform that enables IT teams to operationalize next-generation AI confidently across any infrastructure."
From Experimentation to Production: A Shift Toward AI Inference
With enterprises moving beyond model training to real-world deployment, AI inference—the process of running AI models in production—is becoming the new focus. Red Hat AI 3 introduces advanced distributed inference capabilities through the integration of vLLM and llm-d, enabling organizations to scale large language models (LLMs) efficiently across Kubernetes clusters.
These new technologies offer faster response times, reduced latency, and improved cost efficiency by leveraging open-source innovations like NVIDIA Dynamo, NIXL, and DeepEP. This helps organizations run massive AI workloads, including Mixture-of-Experts (MoE) models, with greater predictability and reliability.
A Unified Platform for Collaborative AI
Red Hat AI 3 introduces several new capabilities to foster collaboration and accelerate enterprise AI adoption:
Model as a Service (MaaS): Enables IT teams to host and deliver AI models centrally, ensuring data privacy and cost efficiency.
AI Hub: A curated catalog and management environment for foundational AI models and assets.
Gen AI Studio: A hands-on space for engineers to prototype and test generative AI applications.
These features streamline how organizations manage, deploy, and optimize AI models at scale—bridging the gap between data science and IT operations.
Building the Foundation for Agentic AI
Looking ahead, Red Hat AI 3 also lays the groundwork for agentic AI—the next evolution of autonomous, task-oriented AI systems. The platform integrates Llama Stack and the Model Context Protocol (MCP) to ensure interoperability between models and external tools, empowering developers to build complex AI agents with ease.
A new modular toolkit for model customization and fine-tuning, built on Red Hat’s open-source InstructLab framework, allows enterprises to adapt AI models using their proprietary data while maintaining transparency and control.
Partner Ecosystem and Industry Collaboration
Red Hat’s ecosystem partners, including AMD, NVIDIA, and ARSAT, praised the launch for advancing enterprise-ready AI infrastructure.
Dan McNamara, Senior Vice President at AMD, highlighted the “powerful combination of AMD’s high-performance processors and GPUs with Red Hat’s open-source AI capabilities,” emphasizing the joint effort to operationalize next-generation AI.
Rick Villars, Group VP at IDC, noted that 2026 will mark a turning point where enterprises demand measurable business outcomes from AI investments—an area Red Hat AI 3 directly addresses through scalable, secure, and cost-effective inference solutions.