IBM Announces Red Hat AI Inference and Red Hat OpenShift Virtualization Service on IBM Cloud

2026-05-12 · Source: IBM - Announcements (Artificial intelligence) · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

IBM announced two new managed services on May 12, 2026: Red Hat AI Inference on IBM Cloud and Red Hat OpenShift Virtualization Service on IBM Cloud. Red Hat AI Inference, generally available May 22, 2026, helps enterprises integrate real-time AI inferencing into production workflows across hybrid cloud environments, offering built-in governance and supporting models like Granite 4.0 H Small, Mistral-Small-3.2-24B-Instruct, Llama 3.3 70B Instruct, GPT-OSS-120B, and Nemotron-3-Nano-30B-FP8. Red Hat OpenShift Virtualization Service, expected generally available in June 2026, provides a managed path for migrating and running virtual machines (VMs) securely and at scale on a Kubernetes-based infrastructure, aiming for operational stability, security, compliance, and predictable costs. These offerings extend IBM's Red Hat managed platform portfolio to accelerate hybrid cloud adoption and AI operationalization.

Key takeaway

For CTOs and VPs of Engineering seeking to scale AI and modernize infrastructure, these new IBM Cloud services offer a clear path. Red Hat AI Inference provides a managed platform for production-grade AI without GPU management complexity, while Red Hat OpenShift Virtualization Service simplifies VM migration to a Kubernetes-based environment. You can offload operational burdens and accelerate time-to-value for both AI and virtualization initiatives.

Key insights

IBM's new managed services enable enterprises to operationalize AI inference and modernize virtualized workloads on a hybrid cloud.

Principles

• Standardize AI orchestration and governance.
• Optimize virtualization for stability and cost.
• Leverage managed services for operational burden.

Method

Migrate VMs to a Kubernetes-based environment using integrated tooling like Migration Toolkit for Virtualization, then deploy AI models via OpenAI compatible APIs for production-grade inference.

In practice

• Deploy AI models via API-accessible shared resources.
• Migrate legacy VMs to OpenShift with Kubernetes.
• Utilize IBM Cloud IAM for AI model governance.

Topics

• Red Hat AI Inference
• Red Hat OpenShift Virtualization
• IBM Cloud
• AI Operationalization
• Virtual Machine Migration

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM - Announcements (Artificial intelligence).