Red Hat has officially introduced Red Hat AI Enterprise, a unified AI platform designed to help organizations deploy and manage AI models, agents and applications across hybrid cloud environments. At the same time, the company rolled out Red Hat AI 3.3, delivering major enhancements across its broader AI portfolio, including Red Hat OpenShift and Red Hat Enterprise Linux.

With this launch, Red Hat strengthens its “metal-to-agent” approach by integrating Linux and Kubernetes infrastructure with advanced inference and agentic capabilities. As a result, enterprises can shift from isolated AI experimentation toward governed, scalable and autonomous AI operations.

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Moving Beyond the AI Pilot Phase

As enterprise AI rapidly evolves from basic chat interfaces to complex, high-density autonomous workflows, organizations increasingly demand deeper integration across their technology stacks. However, many IT teams remain stuck in pilot mode due to fragmented tools and inconsistent infrastructure.

Red Hat AI Enterprise directly addresses this challenge. Instead of treating AI as a siloed initiative, the platform unifies model and application lifecycles into a standardized enterprise system. Consequently, organizations can deliver AI with the same reliability and repeatability as traditional enterprise software.

A Production-Ready AI Foundation

Built on Red Hat OpenShift, the industry’s leading hybrid cloud application platform powered by Kubernetes, Red Hat AI Enterprise delivers high-performance AI inference, model tuning, customization and agent management across any hardware and environment. Furthermore, the platform maintains a strong security posture while offering scalability and operational consistency.

In collaboration with NVIDIA, Red Hat also co-engineered the Red Hat AI Factory with NVIDIA. This initiative combines Red Hat AI Enterprise with NVIDIA AI Enterprise to help organizations accelerate and scale production AI workloads.

Key advantages of Red Hat AI Enterprise include optimized generative AI deployments using the vLLM inference engine and llm-d distributed inference framework. Additionally, the platform integrates observability and lifecycle governance tools to reduce operational risk. Organizations also gain hybrid cloud flexibility, enabling them to deploy and manage AI wherever business demands.

Red Hat AI 3.3 Expands Flexibility and Performance

Alongside AI Enterprise, Red Hat AI 3.3 introduces broader model support, full-stack optimization and enhanced operational consistency.

The update expands the validated model ecosystem with compressed versions of Mistral-Large-3, Nemotron-Nano and Apertus-8B-Instruct available via the OpenShift AI Catalog. Moreover, enterprises can now deploy advanced models such as Ministral 3 and DeepSeek-V3.2, benefiting from multimodal enhancements including faster Whisper performance and improved speculative decoding.

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Importantly, Red Hat is previewing Models-as-a-Service (MaaS), allowing IT teams to offer self-service access to privately hosted AI models through an API gateway. This centralized framework promotes scalable and secure AI adoption across the enterprise.

The release also broadens hardware compatibility, introducing generative AI support on Intel CPUs and expanding certification for NVIDIA Blackwell Ultra and AMD MI325X accelerators. Meanwhile, the new Red Hat AI Python Index provides a hardened repository of enterprise-grade tools, enabling secure, repeatable production pipelines.

To enhance operational control, the platform delivers comprehensive AI observability, real-time telemetry and integrated safety mechanisms, including a preview of NeMo Guardrails for enforcing policy and alignment. Additionally, organizations can establish internal GPU-as-a-Service capabilities with intelligent orchestration and automatic checkpointing, ensuring cost predictability and workload continuity.

Executive Perspective

Joe Fernandes, vice president and general manager, AI Business Unit, Red Hat, said: “For AI to deliver true business value, it must be operationalized as a core component of the enterprise software stack, not as a standalone silo. Red Hat AI Enterprise is designed to bridge the gap between infrastructure and innovation by providing a unified metal to agent platform. By integrating advanced tuning and agentic capabilities with the industry-leading foundation of Red Hat Enterprise Linux and Red Hat OpenShift, we are providing the complete stack – from the GPU-accelerated hardware to the models and agents that drive business logic. Additionally, with Red Hat AI 3.3 organizations can move beyond fragmented pilots to governed, repeatable and high-performance AI operations across the hybrid cloud.”

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