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Red Hat AI 3 is launched

Red Hat AI 3 is launched: Accelerating AI from concept to production 

Red Hat has announced Red Hat AI 3, the latest version of its AI platform designed to simplify the deployment of artificial intelligence in production environments. The new release integrates multiple AI solutions across Red Hat Enterprise Linux (RHEL) and OpenShift, helping organizations scale AI inference in hybrid infrastructures.

Built on trusted Red Hat technologies, it combines several specialized components: Red Hat AI Inference Server, RHEL AI, and OpenShift AI. Together, these tools aim to help enterprises move AI workloads from proof-of-concept to full-scale production more efficiently.

Key innovations in Red Hat AI 3

Red Hat highlights four major innovations in this release:

  • Model as a Service (MaaS): Provides centralized management and on-demand access to AI models for developers and applications. This gives organizations full control over their data and infrastructure while streamlining model delivery.
  • AI Hub: Offers a curated catalog of models and tools for managing the entire model lifecycle. It includes an environment for deploying and managing models through OpenShift AI.
  • Gen AI Studio: A development environment where users can experiment with large language models (LLMs) and build applications leveraging techniques like retrieval-augmented generation (RAG).
  • New Models: The release also includes support for specific LLMs such as OpenAI’s gpt-oss, DeepSeek-R1, and speech models like Whisper and Voxtral Mini.

Advancements in OpenShift AI 3.0

Red Hat OpenShift AI 3.0 plays a central role in this update. It now supports agent-based AI applications and introduces a Unified API based on the Llama Stack, alongside adoption of the Model Context Protocol (MCP) — a standard increasingly embraced by industry leaders like Snowflake and Salesforce. MCP enables seamless communication between models, tools, and external data sources.

Red Hat AI 3 also integrates a model adaptation toolkit built on InstructLab technology. This allows developers to process proprietary data, generate synthetic datasets, and train models efficiently.

Optimized for scalable inference

A key focus of Red Hat AI 3 is improving inference performance. The platform now includes llm-d, an extension of the vLLM project, enabling distributed inference on Kubernetes. This enhancement delivers lower latency, reduced costs, and better utilization of GPU accelerators from NVIDIA and AMD.

Recognizing the challenges enterprises face in bringing AI to production, Red Hat aims to make the process more accessible and scalable. By combining open-source reliability with a unified AI ecosystem, Red Hat AI 3 helps organizations transform innovative ideas into production-ready applications that deliver real business value.

A step toward Red Hat’s AI future

The launch of Red Hat AI 3 marks a strategic move in Red Hat’s evolution, positioning the company not only as a leader in open-source technologies but also as a major player in enterprise AI enablement.

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