Red Hat launches AI Enterprise platform to accelerate hybrid cloud AI deployments at scale

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

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 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 latest version of Red Hat AI and a new, co-engineered software platform called the Red Hat AI Factory with Nvidia. The Red Hat AI Enterprise and Red Hat AI platforms form part of a comprehensive new "metal-to-agent" development stack, the company said, while the Red Hat AI factory is all about creating and managing the most efficient environment for deploying AI agents. The IBM Corp. unit said its latest innovations are designed to help 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 due to the use of fragmented tools and inconsistent infrastructure. To get around this, Red Hat AI Enterprise unifies model and application lifecycles so AI can be managed as a regular enterprise system. That way, it said, AI delivery will become as repeatable and reliable as traditional software deployment. The company is positioning Red Hat AI Enterprise as a "foundation for AI production" that provides capabilities including AI inference, model tuning, customization, deployment and management tools in a single package. It's meant to support any kind of AI model in any environment, including the cloud or on-premises. Red Hat's cloud application platform OpenShift sits at the core of Red Hat AI Enterprise, which means developers will be using familiar development and deployment tools and frameworks, it said. Using Red Hat AI Enterprise, organizations will benefit from fast, scalable and cost-effective AI inference powered by Red Hat's vLLM inference engine, integrated observability and lifecycle management tools and flexible deployment options for any environment, Red Hat AI Vice President and General Manager Joe Fernandes said AI needs to be operationalized as a core component of enterprise software stacks, rather 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 are providing the complete stack -- from the GPU-accelerated hardware to the models and agents that drive business logic," he said. Red Hat AI Enterprise will also serve as the hybrid cloud foundation of the new Red Hat AI Factory with Nvidia, which combines Red Hat's model management and deployment tools with Nvidia's accelerated computing software. It's meant to simplify the management of both traditional infrastructure and complex AI computing stacks, Red Hat said, so teams can accelerate their path from pilot to production AI. The new platform takes care of things such as provisioning the underlying infrastructure for AI workloads and optimizing it to enhance 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. Because it's built on Red Hat, users will also benefit from AI that inherits Red Hat's security and compliance capabilities, reducing risk and mitigating downtime. "We're accelerating the path 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 same rigor they apply to their core IT platforms." Somewhat confusingly, Red Hat also offers a popular platform known as Red Hat AI, which is receiving a major upgrade with arrival of version 3.3. Red Hat AI can be considered as the broader portfolio of tools and services used for AI development in hybrid cloud environments, while Red Hat AI Enterprise is the foundation for running models on flexible infrastructure platforms. With Red Hat AI 3.3, developers are getting 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, as well as new foundational models such as 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. Moreover, Red Hat is expanding its hardware support with a new technology preview of generative AI support on Intel Corp.'s central processing units, which can now be used to run more cost-effective small language models. Other new features include the Red Hat AI Python Index, which gives developers the option to use hardened, enterprise-grade versions of tools such as Docling, Training Hub and SDG Hub, on-demand access to GPU resources, and enhanced observability and security features.

Red Hat Launches Red Hat AI Enterprise to Deliver a Unified AI Platform that Spans from Metal to Agents

Red Hat AI Enterprise bridges the gap from AI infrastructure to production-ready agents by unifying the AI lifecycle with the industry-leading foundation of Red Hat Enterprise Linux and Red Hat OpenShift Red Hat today announced Red Hat AI Enterprise, an integrated AI platform for deploying and managing AI models, agents and applications across the hybrid cloud. It joins the Red Hat AI portfolio which includes Red Hat AI Inference Server, Red Hat OpenShift AI and Red Hat Enterprise Linux AI. Red Hat is also introducing Red Hat AI 3.3, bringing significant updates and enhancements across the company's entire AI portfolio. Together, these solutions provide a comprehensive "metal-to-agent" stack, integrating the underlying Linux and Kubernetes infrastructure with advanced inference and agentic capabilities to help organizations move from fragmented experimentation to governed, autonomous operations. The enterprise AI landscape is rapidly evolving from simple chat interfaces toward high-density, autonomous agentic workflows that require deeper integration across the entire technology stack. However, many organizations remain stuck in the "pilot phase" due to fragmented tools and inconsistent infrastructure. Red Hat AI Enterprise addresses this by unifying the model and application lifecycles allowing IT teams to manage AI as a standardized enterprise system rather than a siloed project - making AI delivery as reliable and repeatable as traditional enterprise software. Red Hat AI Enterprise: The foundation for AI production Red Hat AI Enterprise provides core capabilities, including high-performance AI inference, model tuning and customization and agent deployment and management, with the flexibility to support any model and any hardware across any environment. Fueled by Red Hat OpenShift - the industry's leading hybrid cloud application platform powered by Kubernetes - at its core, Red Hat AI Enterprise delivers a highly scalable and more consistent experience with a stronger security footprint, anywhere, using familiar tools and frameworks. For NVIDIA AI infrastructure, NVIDIA and Red Hat co-engineered the new Red Hat AI Factory with NVIDIA, combining Red Hat AI Enterprise and NVIDIA AI Enterprise to help speed and scale production AI for enterprises. Key benefits of Red Hat AI Enterprise include: * Faster, more cost-effective and scalable AI inference using the vLLM inference engine and llm-d distributed inference framework for optimized generative AI model deployments across hybrid hardware environments. * Integrated observability and lifecycle management to help drive AI lifecycle governance and mitigate risk with an integrated, tested and interoperable enterprise-ready AI stack. * Flexibility across the hybrid cloud by empowering organizations to deploy and manage AI models, agents and applications with greater consistency wherever their business needs to run backed by trusted Red Hat platforms. Extending strategic flexibility and full-stack efficiency with Red Hat AI 3.3 Red Hat's strategy centers on bridging the gap between mission-critical stability and frontier innovation through a unified platform. The latest software release expands model choice, deepens full-stack optimization for next-generation silicon and hardens operational consistency for frontier models. New features and enhancements include: * Expanded model ecosystem with validated, production-ready compressed versions of Mistral-Large-3, Nemotron-Nano and Apertus-8B-Instruct, available via the OpenShift AI Catalog. Additionally, the release enables deployment of state-of-the-art models like Ministral 3 and DeepSeek-V3.2 with sparse attention, while delivering multimodal enhancements including 3x Whisper speedup, geospatial support, improved EAGLE speculative decoding and enhanced tool calling for agentic workflows. * Self-service access to AI models with a technology preview of Models-as-a-Service (MaaS). IT teams can provide self-service access to privately hosted models via an API gateway. This centralized approach ensures that AI is available on-demand for internal users, fostering a ready-to-go AI foundation that promotes private and scalable AI adoption within the enterprise. * Expanded hardware support including a technology preview of generative AI support on CPUs, starting with Intel CPUs for more cost-effective small language model (SLM) inference. Additionally, the platform has expanded its hardware certification for NVIDIA's Blackwell Ultra and support for AMD MI325X accelerators. * Unified data-to-model lifecycle secured by the new Red Hat AI Python Index. This trusted repository delivers hardened, enterprise-grade versions of critical tools -- including Docling, SDG Hub, and Training Hub -- enabling teams to move from fragmented experimentation to repeatable, security-focused production pipelines. * Comprehensive AI observability and safety with greater visibility into model health, performance and behavior. This provides real-time telemetry across AI workloads, llm-d deployments and Models-as-a-Service (MaaS) cluster and model usage and is paired with a technology preview of integrated NeMo Guardrails, enabling developers to enforce operational safety and alignment across AI interactions. * Provide on-demand access to GPUs resources by empowering organizations to deploy their own internal GPU-as-a-Service capabilities through intelligent orchestration and pooled hardware access with automatic checkpointing to save the state of long-running training jobs, preventing work loss and maintaining more predictable compute costs, even in highly dynamic or preemptible environments. Supporting Quotes 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." Additional Resources * Learn more about Red Hat AI Enterprise * Learn more about Red Hat AI Factory with NVIDIA * Join the March 3rd roadmap session for a deeper dive on Red Hat AI 3.3

Red Hat AI Factory with Nvidia Accelerates the Path to Scalable Production AI

Red Hat announced the Red Hat AI Factory with NVIDIA, a co-engineered software platform that combines Red Hat AI Enterprise and NVIDIA AI Enterprise to provide an end-to-end AI solution optimized for organizations deploying AI at scale. Red Hat AI Factory with NVIDIA is the latest milestone in the companies' deep collaboration, accelerating the delivery of the newest AI innovations to enterprise customers while also delivering Day 0 support for NVIDIA hardware architectures. With enterprise AI spending expected to reach over $1 trillion by 20291, driven in large part by agentic AI applications, organizations are looking to shift their strategies toward high-density, agentic workflows and address the resulting demands on AI inference and infrastructure. To help organizations keep pace, Red Hat AI Factory with NVIDIA empowers IT operations teams to streamline management of both traditional infrastructure and the evolving demands of the AI stack. Red Hat AI Factory withNVIDIA accelerates the path to production AI and delivers the software platform for AI factories, running on accelerated computing infrastructure that fuels higher performance for the models and NVIDIA GPUs driving the inference stack. The platform is supported on AI factory infrastructure from leading systems manufacturers, including Cisco, Dell Technologies, Lenovo and Supermicro. This empowers IT administrators and operations teams to scale and maintain AI deployments with the same operational rigor and predictability as any enterprise workload. This co-engineered software platform integrates the open source collaboration, engineering and support expertise of both Red Hat and NVIDIA to deliver a trusted, enterprise-grade solution. The Red Hat AI Factory with NVIDIA provides a highly scalable foundation for AI deployments across any environment, whether on-premises, in the cloud or at the edge. It includes core capabilities for high-performance AI inference, model tuning, customization and agent deployment and management, with a focus on security. This allows organizations to maintain architectural control from the datacenter to the public cloud, delivering: Accelerated time-to-value: Advance to production AI with streamlined workflows and instant access to pre-configured models, including the indemnified IBM Granite family, NVIDIA Nemotron, and NVIDIA Cosmos open models, delivered as NVIDIA NIM microservices. Additionally, organizations can further align models to enterprise data using NVIDIA NeMo, reducing tuning time and cost. Optimized performance and cost: Maximize infrastructure usage and bolster inference performance with a unified, high-performance serving stack. Red Hat AI Factory With NVIDIA delivers built-in observability capabilities and taps Red Hat AI inference capabilities powered by vLLM, NVIDIA TensorRT-LLM, and NVIDIA Dynamo to meet strict AI service level objectives. This helps organizations reduce the total cost of ownership (TCO) for AI by optimizing the connection between models and NVIDIA GPUs. Intelligent GPU orchestration: Enable on-demand access to GPU resources through intelligent orchestration and pooled infrastructure, with automatic checkpointing to protect long-running jobs and maintain more predictable compute costs in dynamic environments. Strengthened enterprise posture: Leveraging the flexible and stable foundation of Red Hat Enterprise Linux, organizations benefit from advanced security and compliance capabilities built-in from the start that help to lower risk, save time and mitigate downtime. This delivers a security-hardened foundation for mission-critical AI workloads that require isolation and continuous verification. NVIDIA DOCA microservices build on this foundation, creating a zero-trust architecture and delivering AI runtime security across the infrastructure. Availability: Red Hat AI Factory withNVIDIA is available now.