Authors: Jehlum Vitasta Pandit – Product Management, Red Hat AI and Matthew Kelm – Product Management, IBM Fusion

Enterprises today demand AI that delivers tangible business results rather than stalling in the pilot phase, long integration projects, unpredictable costs, or fragile pipelines. The fastest path to production AI is to run inference where the data lives, eliminate data copies, and simplify operations so teams can move from pilot to impact quickly.

IBM Fusion for Red Hat AI does exactly this. It unifies data, inference, and operations into a single, sovereign on‑premises platform. With a system that is preconfigured out of the box, run AI workloads quickly while maintaining strict data controls and predictable compute expenses.

Why change now

Recent industry research shows that 88%–95% of AI pilots never reach production, with IDC and MIT pointing to gaps in organizational readiness, operational complexity, and data management as primary causes. These challenges are amplified today by data movement, fragmented infrastructure, and rising inference costs. Cloud‑only strategies introduce unpredictable token billing and egress fees, while DIY GPU clusters impose an engineering burden most teams can’t absorb. Given these challenges, the advantage belongs to organizations that can use governed data in place and run inference at a predictable cost per token.

Three barriers to enterprise AI

• The semantic gap (dark data): Most enterprise data such as documents, PDFs, emails, images, logs, contracts lives in systems that simply store it but don’t understand it. To make this unstructured data usable for AI or RAG, teams copy, clean and re-index it into vector databases, creating duplicate pipelines that introduce freshness, lineage, and compliance risks while adding permanent operational overhead. The “semantic gap” is the distance between where enterprise data lives and what AI systems need to consume it.

• The multi‑generation infrastructure reality: Mission‑critical VM estates won’t be refactored overnight. Meanwhile, AI runs best in containers on Kubernetes. The result is a split architecture where AI workloads and the data they need live apart, driving latency, cost, and risk—becoming a permanent drag on modernization.

• Inference economics and lock‑in: Rigid cloud instances and siloed on-premises infrastructure traps AI workloads to heavily constrained hardware, that holds back ROI on GPU investments made. Without flexibility to intelligently route jobs to the most effective compute resources available, scaling AI becomes financially unsustainable with runaway costs.

Introducing IBM Fusion for Red Hat AI

IBM Fusion for Red Hat AI is a fully integrated, turnkey platform that brings inference to your data and abstracts away the complexity of operational AI.

The value at a glance

• AI‑ready on Day One: Red Hat OpenShift + Red Hat AI pre‑integrated on IBM Fusion HCI, with builtin security, resiliency, and lifecycle automation.

• Inference at the data: Fusion Content‑Aware Storage (CAS) makes enterprise content more easily accessible to AI — with zero copies, instant semantic context.

• Unified operations: Run VMs and containerized AI workloads together on a single platform—modernize without disruption.

• Open accelerator choice: Land each workload on the GPU/CPU of choice; to avoid lock‑in with lower cost‑per‑token and optimized inference runtimes (vLLM, llm‑d).

• Sovereign by design: Keep data, models, and operations under your control to meet governance and regulatory requirements confidently.

• Agentic‑AI ready: Unified APIs, high‑throughput inference, and resilient automation to support enterprise agent workflows.

How it works

1/ Connect models to data — without moving the data

Fusion Content‑Aware Storage (CAS) turns storage from unstructured into an intelligent data layer. CAS indexes and vectorizes unstructured content in place, so RAG can query governed data without massive pipelines or duplication. A global namespace provides zero‑copy access across sites and systems, improving accuracy and trust while reducing risk and cost. This turns RAG from a months‑long integration effort into a capability teams can use immediately.

2/ Deliver high‑performance inference — at predictable cost

On top of Fusion’s governed data layer sits Red Hat AI the model‑serving, tuning, and agentic AI workbench. Optimized runtimes like vLLM and the distributed inference framework, llm‑d, maximize throughput, minimize latency, and improve GPU utilization. With open accelerator choice (e.g., NVIDIA or AMD today, room to evolve tomorrow), teams place each workload on the best‑fit resource to reduce cost‑per‑token, improve GPU ROI, and scale with confidence.

3/ Unify operations for VMs and containers — modernize at your pace

IBM Fusion, preconfigured with Red Hat OpenShift, lets you run VMs and AI workloads together under a single operational model. Platform engineering teams get one control plane for deployment, policy, monitoring, backup/DR, and upgrades—so AI can move forward without destabilizing the mission‑critical apps the business depends on.

Why it matters: less operational overhead, faster adoption, modernization without rewriting everything.

Business value delivered — Start → Scale

Start — Deploy (AI‑ready in days): Deploy a pre‑integrated IBM Fusion + Red Hat AI stack to start fast, not months from now.

Crawl — Consolidate (Unified operations): Run VMs and containers together to reduce complexity and eliminate the operational split.

Walk — Activate data (Zero‑copy RAG): Use CAS to unlock dark data in place and build zero‑copy RAG pipelines for trusted, context‑rich answers.

Run — Accelerate (High‑performance inference): Scale high‑throughput, low‑latency inference with vLLM and llm‑d, landing jobs on the most cost‑effective accelerators.

Scale — Govern (Sovereign AI foundation): Operate a governed, enterprise‑wide AI platform with consistent policy, multi‑cluster automation, and full data control—ready for agentic AI.

Closing

IBM Fusion for Red Hat AI isn’t a toolkit with ‘assembly required.’ It’s a sovereign AI foundation that makes enterprise data usable, inference affordable, and AI deployment predictable. Fusion for Red Hat AI gives enterprises a self-governed, on-premises, future‑proof AI architecture that removes the cost and complexity barriers preventing AI from scaling.

Join us in this coming executive fireside chat webinar. Hear firsthand from business leaders as they share their perspectives about the future of enterprise AI, and what it takes to move ahead in this “token economy”.