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What did Red Hat announce with the general availability of Red Hat AI 3.5 on September 11, 2026, and how does the release—including its integration with Red Hat AI Factory with NVIDIA—help organizations move AI from pilot projects into trusted, production scale hybrid cloud infra
Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi tenant production service across hybrid environments—not merely a collection of pilots. Its central promise is to combine pre deployment trust validation, efficient shar...
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Key takeaways
- Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi tenant production service across hybrid environments—not merely a collection of pilots.
- Its central promise is to combine pre deployment trust validation, efficient shared GPU operations, and repeatable application/inference deployment.
- [16][8] Trust and governance: EvalHub is generally available for automated, risk focused safety benchmarking and auditable compliance certifications for customer built or customized models, RAG pipelines, and AI agents.
- This helps teams test for issues such as prompt injection and jailbreak exposure before deployment.
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openai.comCreate a landscape editorial hero image for this Studio Global article: What did Red Hat announce with the general availability of Red Hat AI 3.5 on September 11, 2026, and how does the release—including its inte. Article summary: Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi tenant production service across hybrid environments—not merely a collec. Topic tags: general web, ai safety, llm, agents, ai. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with
Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi-tenant production service across hybrid environments—not merely a collection of pilots. Its central promise is to combine pre-deployment trust validation, efficient shared-GPU operations, and repeatable application/inference deployment. 16
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Trust and governance: EvalHub is generally available for automated, risk-focused safety benchmarking and auditable compliance certifications for customer-built or customized models, RAG pipelines, and AI agents. This helps teams test for issues such as prompt injection and jailbreak exposure before deployment.
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Validated-model choice: Red Hat expanded its catalog with more than 20 newly validated models from providers including Google, NVIDIA, and Alibaba Cloud, with reported safety, PII-exposure, and toxicity-risk information. The practical aim is to give platform teams evidence for model selection rather than requiring every team to devise its own evaluation process.
Shared GPU production controls: Fair-share scheduling, priority-aware serving, admission control, and priority-based request routing are intended to prevent one workload or team from monopolizing expensive GPU capacity. Together with per-user token metering, they support service-level prioritization and chargeback/showback by team.
Operations visibility: New observability dashboards expose inference health, GPU utilization, and model-performance signals, allowing platform operators to identify capacity, availability, and quality problems while AI services are running.
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RAG and agent development: AutoRAG adds multilingual support for non-English and mixed-language document corpora, including language detection and experiment configuration. Red Hat also positioned it for enterprise-data retrieval, conversational testing, and agentic applications; AI Hub templates provide starting architectures for code review, document processing, and research workflows.
Hybrid and third-party Kubernetes inference: Red Hat AI Inference 3.5 supports optimized inference images across NVIDIA CUDA, AMD ROCm, Google TPU, Intel Gaudi, and IBM Spyre accelerators. Distributed Inference with llm-d is supported on Azure Kubernetes Service and CoreWeave Kubernetes Service, while Amazon EKS inference-aware scheduling remains a Technology Preview.
NVIDIA integration: Within Red Hat AI Factory with NVIDIA, these controls form a standardized operational layer over NVIDIA-accelerated infrastructure: organizations can validate models, allocate GPU capacity fairly, monitor serving, and deploy across hybrid infrastructure using a common Kubernetes-oriented approach. This is the mechanism by which Red Hat is trying to reduce the handoff gap between data-science experimentation and enterprise operations.
Broader IBM/Red Hat position: This release reinforces IBM and Red Hat’s strategy of pairing open, hybrid-cloud infrastructure with governed AI operations. Separately, LTM collaborated with IBM and Red Hat on Lightwell to help enterprises identify, prioritize, and remediate open-source software vulnerabilities; Lightwell is a joint IBM–Red Hat initiative for reducing risk from vulnerable third-party dependencies.
A key qualification: “generally available” does not apply uniformly to every feature—Amazon EKS inference-aware scheduling is explicitly a Technology Preview, whereas the CoreWeave and Azure paths are supported deployments. 8
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What is the short answer to "What did Red Hat announce with the general availability of Red Hat AI 3.5 on September 11, 2026, and how does the release—including its integration with Red Hat AI Factory with NVIDIA—help organizations move AI from pilot projects into trusted, production scale hybrid cloud infra"?
Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi tenant production service across hybrid environments—not merely a collection of pilots.
What are the key points to validate first?
Red Hat announced the general availability of Red Hat AI 3.5 as an enterprise AI platform update focused on making AI a governed, observable, multi tenant production service across hybrid environments—not merely a collection of pilots. Its central promise is to combine pre deployment trust validation, efficient shared GPU operations, and repeatable application/inference deployment.
What should I do next in practice?
[16][8] Trust and governance: EvalHub is generally available for automated, risk focused safety benchmarking and auditable compliance certifications for customer built or customized models, RAG pipelines, and AI agents.