Build the right AI factory for your needs: partner for success

AI ambition is easy to describe - using data and models to improve decisions, automate work, accelerate discovery, or create new services. Delivering those outcomes is harder and takes a significant amount of experience to be successful. Training a frontier model, fine-tuning an industry model, running high-volume inference and supporting agentic AI place different demands on infrastructure, processes and people. Implementing AI may include a single-purpose turnkey configuration that will accommodate one line of business, or the business may demand a more strategic approach to capitalize on the economies of scale and create AI as a multi-tenant service, designed to accommodate the multitude of mainstream business requirements. That is the value of an AI factory, bringing the complete AI lifecycle together so a cost-effective infrastructure can be designed around the mission, scaled with demand, secured as needed and operated as a dependable source of intelligence. The AI factory is designed to deliver the capacity, performance, utilization and business value customers expect. From AI workload to business outcome An AI factory is an integrated solution for data ingestion, model development, training, fine-tuning, inference, monitoring and continuous improvement. Its purpose is to turn data into intelligence repeatedly and efficiently-whether that intelligence supports a clinician, an engineer, a researcher, a public service or an enterprise application. Achieving that goal requires more than choosing an accelerated processor. Compute must match the business model and expected workloads. Networking and data pipelines must keep accelerators supplied. Storage must support the volume and velocity of data. Software must provision resources and orchestrate jobs. Security, governance and multi-tenancy must reflect who will use the environment and what data they can access. Power and cooling must support the system's density today and as it grows. Without proper architecture, governance and operational expertise, organizations can't safely leverage their data, take AI into core processes, or turn innovation into durable competitive advantage," says Thierry Pienaar, HPE Fellow, Vice President and CTO for HPC and AI Sales. Customers are realizing the fact that to derive value to its utmost extent they need an end-to-end infrastructure that's purpose-designed and purpose-built for AI." Choice starts with the workload The HPE AI Factory with NVIDIA portfolio brings together NVIDIA accelerated computing, networking and AI software with HPE infrastructure, software, services and expertise at deploying complex systems. Rather than forcing every customer into a single configuration, the portfolio provides three paths for different ambitions, operating models and requirements. HPE Private Cloud AI, the turnkey AI factory solution, is an enterprise-ready, on-premises AI platform for running fine tuning, RAG & inferencing workload environments that need up to 256 GPUs. HPE AI Factory at-scale supports model builders, service providers and large enterprises that operate across many users, workloads and GPU resources (using 100s to 10s of thousands of GPUs) with centralized control, operational visibility, and multi-tenancy over the entire AI lifecycle HPE Sovereign AI Factory is an HPE AI factory at-scale that adds a deep level of operational control, data security and residency, sovereign management (including optional air-gapped configurations), and built-in compliance frameworks. It is designed for large enterprises, and any other organizations with sensitive information that require strict control and compliance across data, infrastructure, models and operations within defined legal, regulatory or geographic boundaries. Each option starts with the same principle: define the workloads and desired outcomes first, then select the right technologies to support them and finally identify the required resources needed to implement such an infrastructure. A hospital deploying clinical assistants will make different choices from a service provider offering GPU capacity, a manufacturer training vision models or a government operating sensitive national workloads. The HPE AI Factory model gives each a way to build for its mission without losing sight of performance, control or future growth - and HPE partners with these organizations to dramatically increase the likelihood of success. Operate the environment as one system As the use of AI expands across an organization, the operational challenges mature. Multiple teams may need different resource profiles, application stacks, service levels and data boundaries. Platform teams need to see utilization, allocate capacity, apply policy and understand consumption without creating a separate infrastructure island for every workload. The management of these differences make time-to-production an increasingly useful way to think about AI infrastructure: How quickly can an organization cost effectively move from investment vision to an operational environment generating useful intelligence? Many enterprises initially try to answer that question by extending their existing IT expertise. But building a DIY production AI environment from individual components requires skill sets that many enterprise IT organizations have never needed or required at this scale. A poorly implemented AI system may technically operate while still failing economically or operationally. GPUs can sit underutilized. Data pipelines can create bottlenecks. Cooling or power constraints can limit operation and/or expansion. Security policies can prevent sensitive data and workloads from being included. Separate less understood management systems can make AI factory infrastructure difficult to operate. That is why the AI factory challenge is fundamentally a strategic, systems integration and operations problem, not simply a stream of hardware purchasing transactions. "The HPE AI Factory with NVIDIA portfolio gives enterprises a range of AI solutions co-developed with NVIDIA, backed by HPE's engineering expertise and technical capabilities to design an AI factory around their specific needs and optimize it for performance at scale." Pienaar explains. That distinction matters; customers can choose an architecture suited to their current mission and expand it as models, users and operational requirements change. The HPE AI Factory is designed to help operators provision and govern resources, observe infrastructure, track usage and support secure multi-tenant operations. That control helps customers align capacity with workload priorities while keeping the environment easier to manage as it grows. Make sovereignty a design requirement Cloud services, private environments and hybrid approaches can all play important roles in an AI strategy. For organizations with sovereignty requirements, the decision is defined by costs and the level of sovereignty and control they need: where data and models reside, who can administer the environment, which jurisdiction applies, data residency, how policies are enforced and what level of isolation various workloads require. Sovereign AI tools from HPE and NVIDIA give an enterprise, or even a nation state, complete control over how its AI systems are built, deployed, operated and governed," says Kaushik Shirhatti, Vice President, AI Factory at NVIDIA. For some, that means keeping sensitive data in-country. For others, it means controlling who can access systems, where workloads run, how models are governed, and which local laws apply." HPE and NVIDIA engineer for the complete outcome HPE and NVIDIA co-engineer AI factory solutions to reduce the integration work required to deploy and operate a high efficiency enterprise AI environment. By combining NVIDIA accelerated computing, networking, and AI software with HPE infrastructure, cloud operations, services, and support, the joint solution helps data scientists and developers spend more time building and improving AI applications while platform teams maintain operational production and control. NVIDIA provides accelerated computing platforms, networking, and the NVIDIA AI Enterprise software suite to power modern training, fine-tuning, inference, and agentic workloads. HPE contributes its expertise in enterprise systems engineering, high-performance computing, management and observability software, services, global support, financing, and years of experience in the power and cooling requirements of dense computing environments. Together, the companies can optimize AI computing solutions beyond any single component. The objective is to select the right GPU architecture and system design for the workload, keep accelerators productive with high-speed data movement, provide the software and operational controls teams need, and create a path to scale without unnecessarily redesigning the environment. HPE AI Services support that path from business planning, AI strategy, workload characterization and facility planning through deployment, integration, support and ongoing operations. HPE Financial Services can help with purchasing, accelerated depreciation schedules and lifecycle flexibility. These capabilities help customers make economically sound technology choices in the context of the business outcome, the operating model and the pace at which the environment needs to evolve. Deployment speed matters, but it is not the final measure of success. Customers need to consider workload readiness, model performance, accelerator utilization, developer productivity, governance, availability, economics and the ability to expand. Those measures connect the infrastructure decision to the outcomes the organization set out to achieve. The case for HPE AI Factory with NVIDIA is not that every customer needs the same stack. It is that every customer needs an AI environment intentionally matched to its workloads, data, operating requirements and goals. By combining NVIDIA's accelerated computing leadership with HPE's infrastructure, software, services and operating expertise, organizations can choose the right path-and move from AI investment to meaningful business outcomes faster. Recent deployments of the HPE AI Factory with NVIDIA - TELUS Sovereign AI Factory in Canada and the sovereign AI factory at the University of Utah in the US - are helping with overcoming engineering challenges and driving scientific advances. In conclusion, start by identifying the workloads that would benefit from AI, define the relevant data boundaries and residency requirements, estimate the expected scale over a reasonable timeframe, and determine the operating model, resources, and skills needed to support the AI infrastructure. Then work with HPE and NVIDIA to evaluate which path-turnkey, at-scale, or sovereign-best meets those requirements. To learn more visit HPE AI Factory | AI Infrastructure for Enterprises | HPE Sponsored by HPE and NVIDIA