Red Hat readies its metal-to-agent AI infrastructure stack for hybrid cloud deployments

Red Hat Inc. said today it’s gearing up its artificial intelligence ambitions with the launch of a brand new platform called Red Hat AI Enterprise that’s meant to make it easier to deploy and manage models, AI agents and applications in hybrid cloud environments.

It debuts alongside the most recent version of Red Hat AI and a brand new, co-engineered software platform called the Red Hat AI Factory with Nvidia.

The Red Hat AI Enterprise and Red Hat AI platforms form a part of a comprehensive recent “metal-to-agent” development stack, the corporate said, while the Red Hat AI factory is all about creating and managing essentially the most efficient environment for deploying AI agents.

The IBM Corp. unit said its latest innovations are designed to assist enterprises move their AI projects past the “pilot phase.” It said far too many enterprises get stuck, unable to deploy and scale up their AI projects on account of the usage of fragmented tools and inconsistent infrastructure. To get around this, Red Hat AI Enterprise unifies model and application lifecycles so AI might be managed as a daily enterprise system. That way, it said, AI delivery will change into as repeatable and reliable as traditional software deployment.

The corporate is positioning Red Hat AI Enterprise as a “foundation for AI production” that gives capabilities including AI inference, model tuning, customization, deployment and management tools in a single package. It’s meant to support any type of AI model in any environment, including the cloud or on-premises. Red Hat’s cloud application platform OpenShift sits on the core of Red Hat AI Enterprise, which suggests developers can be using familiar development and deployment tools and frameworks, it said.

Using Red Hat AI Enterprise, organizations will profit from fast, scalable and cost-effective AI inference powered by Red Hat’s vLLM inference engine, integrated observability and lifecycle management tools and versatile deployment options for any environment,

Red Hat AI Vice President and General Manager Joe Fernandes said AI must be operationalized as a core component of enterprise software stacks, somewhat than a standalone silo. “By integrating advanced tuning and agentic capabilities with the industry-leading foundation of Red Hat Enterprise Linux and Red Hat OpenShift, we’re providing the entire stack — from the GPU-accelerated hardware to the models and agents that drive business logic,” he said.

Architecting AI factories

Red Hat AI Enterprise can even serve because the hybrid cloud foundation of the brand new Red Hat AI Factory with Nvidia, which mixes Red Hat’s model management and deployment tools with Nvidia’s accelerated computing software. It’s meant to simplify the management of each traditional infrastructure and complicated AI computing stacks, Red Hat said, so teams can speed up their path from pilot to production AI.

The brand new platform takes care of things corresponding to provisioning the underlying infrastructure for AI workloads and optimizing it to reinforce its performance. It provides access to dozens of preconfigured AI models, including IBM’s Granite family and Nvidia’s Nemotron and Nvidia Cosmos models, enhancing flexibility for developers. Since it’s built on Red Hat, users can even profit from AI that inherits Red Hat’s security and compliance capabilities, reducing risk and mitigating downtime.

“We’re accelerating the trail to deploy AI and move quickly to production using Red Hat AI Factory with Nvidia,” said Red Hat Chief Technology Officer Chris Wright. “With a stable, high-performance foundation driven by our proven hybrid cloud offerings, we’re enabling our customers to own their AI strategy and scale with the identical rigor they apply to their core IT platforms.”

More models and Model-as-a-Service access

Somewhat confusingly, Red Hat also offers a well-liked platform generally known as Red Hat AI, which is receiving a significant upgrade with arrival of version 3.3.

Red Hat AI might be regarded as the broader portfolio of tools and services used for AI development in hybrid cloud environments, while Red Hat AI Enterprise is the inspiration for running models on flexible infrastructure platforms.

With Red Hat AI 3.3, developers are gaining access to an expanded library of AI models to work with, including compressed, production-ready versions of Mistral-Large-3, Nemotron-Nano and Apertus-8B-Instruct, in addition to recent foundational models corresponding to Ministral 3 and DeepSeek-V3.2 with sparse attention. There’s also a technology preview of Model-as-a-Service that’s meant to facilitate self-service access to privately-hosted models through an application programming interface gateway. Furthermore, Red Hat is expanding its hardware support with a brand new technology preview of generative AI support on Intel Corp.’s central processing units, which might now be used to run less expensive small language models.

Other recent features include the Red Hat AI Python Index, which provides developers the choice to make use of hardened, enterprise-grade versions of tools corresponding to Docling, Training Hub and SDG Hub, on-demand access to GPU resources, and enhanced observability and safety features.

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