Build AI at Scale: WWT Brings the Red Hat AI Factory with NVIDIA to the Enterprise

From experiment to enterprise

Nearly every major enterprise today is on an exciting journey of planning, building and, eventually, operationalizing an AI footprint that meets a variety of needs while keeping cost and complexity in check. This journey often begins with what amounts to a proof-of-concept – essentially a small-scale experiment for how these new technologies can be leveraged. 

But as organizations move beyond experimentation and toward production, the infrastructure stakes get much higher. It's no secret that AI workloads have special considerations from an infrastructure perspective.

In fact, the needs of AI workloads necessitate a rethinking of traditional data center design paradigms. To address this, the market presents an abundance of choice including software platforms, high-performance computing, hardware accelerators, GPUs and so on. The challenge isn't a lack of options; it's knowing how to bring the right ones together.

That's where the concept of an AI factory comes in. An AI factory is a specialized computing infrastructure designed to manage the entire lifecycle of artificial intelligence systems, from data ingestion and model training to inference and ongoing operations.

Today, many organizations are on a journey to plan and eventually build their own AI factory, but going the do-it-yourself route can lead to integration challenges that can be magnified when you try to operationalize the solution.

Building AI at scale is harder than it looks

For many enterprises, the leap from proof-of-concept to production AI is where the real difficulty begins. Without a deliberate, well-architected approach, AI infrastructure quickly becomes disjointed, manual and difficult to reproduce and maintain reliably. What works in a small-scale experiment rarely scales cleanly to an enterprise-wide deployment.

The compute demands only amplify the challenge. Existing platforms often fail to efficiently handle the intensive processing requirements of production-grade AI workloads, leaving organizations with performance bottlenecks that slow model training, increase inference latency and ultimately limit the business value AI can deliver.

Security and compliance add yet another layer of complexity. Fragmented environments lack the rigorous controls needed to protect sensitive enterprise data, creating risk that is difficult to quantify and even harder to remediate at scale.

Perhaps the most frustrating challenge is the purchasing dilemma. Enterprises face an overwhelming number of platform choices, many of which overlap in capabilities, yet none of which can fully address every need on their own. The result is often a patchwork of tools and technologies that introduces integration challenges rather than solving them.

The good news? These challenges aren't unsolvable. They simply require the right combination of purpose-built technology and deep integration expertise, which is exactly what brings Red Hat and NVIDIA together.

Red Hat + NVIDIA: A better together story

Recall that an AI factory is built around three core principles: performance, scalability and energy efficiency — achieved through the tight integration of hardware, software and automation to streamline AI development and deployment. Both Red Hat and NVIDIA® have independently developed solutions to address the challenges outlined above, and each brings formidable capabilities to the table.

But here's where it gets interesting. While having multiple best-in-class solutions is generally a good thing, it can also put organizations in a difficult position; ideally, they'd want a platform that brings the best of both worlds together rather than forcing them to choose between them. 

Recognizing this, Red Hat and NVIDIA chose collaboration over competition, forming a deeper collaboration to co-engineer joint AI solutions with a unified go-to-market strategy.

The result is something greater than the sum of its parts. Red Hat AI Factory with NVIDIA — a validated AI platform combining Red Hat OpenShift AI with NVIDIA accelerated computing to design, build and run enterprise AI — transforms what is often a disjointed, one-off process of creating, customizing and deploying AI models into a repeatable, scalable factory process, complete with the guardrails and safeguards enterprises need to protect their workloads and data. 

And critically, organizations no longer have to choose between a robust enterprise AI platform and the best-in-class hardware accelerators for their use case. With Red Hat AI Factory with NVIDIA, they get both.

The power of co-engineering

Red Hat AI Factory with NVIDIA brings together the best of both industry leaders into a single, cohesive offering. By bundling Red Hat AI Enterprise and NVIDIA AI Enterprise — an end-to-end AI software platform that accelerates data preparation, model training, fine-tuning and inference deployment on NVIDIA-accelerated infrastructure — and offering simplified node-based pricing for NVIDIA hardware, the solution removes the complexity of procuring, integrating and managing two separate platforms.

Each partner contributes what they do best. NVIDIA delivers specialized AI software, including NVIDIA NIM™ — containerized AI inference microservices that allow developers to deploy optimized AI models anywhere, from cloud to data center to workstation — NVIDIA NeMo™, an end-to-end platform for developing, customizing, and deploying large language models from data curation through training, fine-tuning, and alignment, and NVIDIA Nemotron™ models. 

Red Hat contributes the open hybrid cloud foundation via OpenShift, highly efficient vLLM inference, AgentOps and the enterprise stability organizations depend on. Together, these capabilities form a unified offering for building, deploying and scaling production-ready AI. The diagram below illustrates how these components come together across the solution stack.

Overlap is a feature, not a bug

Not every component in the Red Hat AI Factory with NVIDIA solution is unique to one vendor, and that's okay. There are areas where Red Hat AI Enterprise and NVIDIA AI Enterprise overlap or compete, such as NVIDIA Dynamo (an open-source inference framework for serving large language models at scale, with disaggregated prefill and decode for maximum GPU utilization), TRT-LLM, NVIDIA Run:ai (an AI compute management platform for orchestrating and optimizing GPU workloads, maximizing utilization and accelerating AI development across any infrastructure) and BCM. Rather than being a drawback, this overlap is actually a feature of the solution's flexibility.

Where duplicate technologies exist, the question of which to use becomes an implementation decision rather than a purchasing decision. Every organization brings its own priorities, existing investments and operational requirements to the table — and Red Hat AI Factory with NVIDIA is designed to accommodate that. Organizations are free to leverage the technologies from either vendor that best align with their AI factory's patterns and goals.

Built for performance, designed for business

Red Hat AI Factory with NVIDIA is designed with business outcomes in mind, not just technical ones. Beyond the architectural advantages, the solution delivers tangible benefits that matter to the organizations deploying it.

Accelerate time to value

Building an AI factory from scratch is a time-consuming endeavor. Evaluating platforms, procuring hardware, integrating components and validating the stack can take months before a single model ever reaches production. Red Hat AI Factory with NVIDIA significantly shortens that timeline by delivering a pre-integrated, co-engineered solution ready to deploy on validated NVIDIA hardware. Organizations can spend less time building the foundation and more time realizing the business value of their AI investments.

Increase operational efficiency

Scaling enterprise AI capabilities often introduces unpredictable costs and operational friction. Left unchecked, those inefficiencies compound quickly. Red Hat AI Factory with NVIDIA helps organizations maximize their infrastructure investments and lower total cost of ownership through a highly optimized, fully integrated stack.

Intelligent resource management is equally critical. Red Hat AI Factory with NVIDIA optimizes GPU utilization through advanced quota management and efficient workload scheduling, ensuring that the most expensive component of any AI factory is never left idle or poorly allocated. Organizations can take efficiency even further by incorporating NVIDIA BlueField® DPUs, which offload, accelerate, and isolate networking, storage, and security workloads — freeing GPU and CPU resources to focus on what they do best.

Reduce risk

Every DIY AI infrastructure build carries inherent risk; integration challenges, security gaps and unvalidated component combinations can all surface at the worst possible time. Red Hat AI Factory with NVIDIA mitigates that risk by delivering a solution co-engineered and validated by two of the most trusted names in enterprise technology. Enterprise-grade security and compliance controls are built into the platform from the ground up, giving organizations the confidence to deploy production AI at scale without compromising on governance or data protection.

WWT: Your guide from blueprint to production

Understanding the value of Red Hat AI Factory with NVIDIA is one thing, but successfully deploying and operationalizing it is another. That's where World Wide Technology comes in. As a trusted partner to both Red Hat and NVIDIA, WWT brings the deep technical expertise, integration experience and professional services capabilities needed to help organizations plan, architect and implement their AI factory with confidence. 
 Whether you're just beginning your AI journey or looking to accelerate an existing initiative, WWT bridges the gap between a powerful co-engineered solution and a production-ready deployment tailored to your organization's unique needs.

Red Hat AI Factory with NVIDIA is available today on the NVIDIA Hopper™ and NVIDIA Blackwell architectures, giving enterprises access to the solution across the current and most recent generations of NVIDIA's industry-leading GPU platforms. Support for the NVIDIA Vera Rubin architecture is on the roadmap, with availability expected in the second half of 2026, ensuring that organizations investing in the solution today are well positioned for the next generation of NVIDIA hardware.