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Red Hat today announced Red Hat AI Enterprise, an integrated AI platform designed to unify the full AI lifecycle—from underlying infrastructure to production-ready models, agents, and applications—across hybrid cloud environments. Built on the foundation of Red Hat Enterprise Linux and Red Hat OpenShift, the platform addresses the challenges organizations face when moving beyond AI experimentation to governed, scalable, autonomous operations. Red Hat also introduced Red Hat AI 3.3, delivering major enhancements across its AI portfolio, including expanded model support, hardware optimizations, and operational consistency for frontier AI workloads.
The enterprise AI landscape is shifting toward high-density, agentic workflows that demand deep stack integration. Many organizations remain in pilot phases due to fragmented tools and inconsistent infrastructure. Red Hat AI Enterprise overcomes these barriers by standardizing AI delivery as a reliable enterprise system, enabling IT teams to manage models and agents with the same rigor applied to traditional software.
Red Hat AI Enterprise provides high-performance inference, model tuning, customization, and agent deployment across any model, hardware, or environment. Powered by Red Hat OpenShift, it delivers scalable consistency, stronger security, and familiar tools for hybrid cloud deployments. For NVIDIA-based infrastructure, the co-engineered Red Hat AI Factory with NVIDIA combines Red Hat AI Enterprise and NVIDIA AI Enterprise to accelerate production AI at scale.
Red Hat AI Enterprise enables faster, cost-effective inference using optimized engines like vLLM and llm-d across hybrid hardware. It integrates observability and governance to mitigate risk, providing a tested, interoperable stack that supports consistent deployment and management anywhere business needs require.
Red Hat AI 3.3 extends this foundation with broader model choice—including validated compressed versions and state-of-the-art deployments—along with multimodal improvements (3x Whisper speedup, geospatial support, EAGLE speculative decoding, enhanced tool calling). The Models-as-a-Service preview centralizes private model access, while expanded hardware support lowers costs for small language models and optimizes next-generation accelerators.
The new Red Hat AI Python Index delivers enterprise-grade, security-focused tools for repeatable pipelines, and advanced observability paired with NeMo Guardrails ensures model health, performance, behavior monitoring, and operational safety.
GPU resource access gains intelligence through orchestration, pooled infrastructure, and automatic checkpointing—safeguarding long-running jobs and controlling costs in preemptible or dynamic settings.
Joe Fernandes, vice president and general manager, AI Business Unit, Red Hat: “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.”
About Red Hat
Red Hat is the world’s leading provider of open source solutions, using a community-powered approach to deliver high-performing Linux, hybrid cloud, container, and Kubernetes technologies. Red Hat helps customers integrate new and existing IT applications, develop cloud-native applications, and standardize across environments—physical, virtual, containerized, and across public cloud providers. A subsidiary of IBM, Red Hat is headquartered in Raleigh, North Carolina.
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