The industrialization of AI: 8 critical considerations for building an AI gigafactory
The rapid rise of artificial intelligence has transcended the boundaries of corporate software innovation, sparking a foundational transformation in global infrastructure. Across the globe, nations, research institutions, and large enterprises are shifting away from traditional data center models toward massive, centralized hubs designed explicitly to industrialize AI workloads at an unprecedented scale.
As the European Commission works to establish multiple facilities dedicated to training next-generation AI models with trillions of parameters, a new term has emerged to describe these monumental engineering feats: the AI gigafactory.
While a standard corporate AI setup might utilize a few hundred or thousand GPUs, an AI gigafactory scales up to tens or hundreds of thousands of nodes. Power requirements shift dramatically from standard line-item budgeting to a fundamental physical constraint measured in megawatts or even gigawatts.
For CIOs and technology leaders tasked with planning these multi-million-dollar investments, success requires designing the infrastructure from the ground up to support a decade-long operational lifespan.
Based on an architectural white paper from HPE and AMD, here are the eight critical considerations that technology leaders must address when planning a giga-scale AI facility.
- Start with power and land
At the gigafactory scale, raw electrical power dictates the viability of the entire project. Many organizations discover late in the planning cycle that the existing power grid cannot support their computational roadmap, requiring years of utility redesigns, substation overhauls, and transmission line upgrades. Furthermore, because these facilities represent critical infrastructure for national sovereignty and data security, they require physical placement near both primary power grids and robust, independent backup generation sources. - Design for liquid cooling
Modern high-performance AI chips generate intense thermal output, and compounding this heat across high-density server racks makes traditional air cooling obsolete. Liquid cooling architectures absorb heat significantly faster and can transport up to 3,000 times more heat than standard air systems while utilizing less energy to operate. However, implementing liquid cooling introduces architectural complexities including leak detection systems, precise water-use management, and strict regulatory and insurance sign-offs that must be integrated early in the design phase. - Overbuild your networking capability
While compute power scales linearly, the communication network must scale exponentially. Large language models are split across thousands of interconnected nodes, and every training iteration requires synchronized communication. Network bottlenecks cause expensive GPUs to sit idle, wasting millions in operational overhead. To future-proof a giga-scale facility, experts advise over-engineering network bandwidth and latency upfront rather than attempting costly retrofits later. - Plan for hardware and software failure
In a cluster operating with tens of thousands of compute nodes, hardware components will inevitably fail. Gigafactory resilience requires a shift away from standard troubleshooting toward autonomous, self-healing infrastructures. System architects must design independent fault domains, allowing specific operational units to be swapped out or repaired without disrupting the broader, ongoing training workloads. - Deploy orchestration and governance before hardware
Orchestration software cannot be treated as an afterthought or bolted on after the physical servers are installed. Technology leaders must deploy governance, scheduling, and resource allocation layers first, as these software frameworks directly influence physical hardware requirements, network segmentation, and systemic security boundaries. Pre-defining these parameters ensures that workloads can begin executing safely the moment the physical infrastructure goes live. - Choose your suppliers wisely
Building at this extreme scale demands technology partners with verified, proven experience in high-performance computing (HPC). For instance, HPE currently powers some of the world’s fastest supercomputers and offers standardized, engineering-validated AI factories to streamline deployment. Concurrently, selecting hardware built on open ecosystems—such as AMD EPYC™ CPUs, AMD Instinct™ GPUs, and AMD Pensando™ DPUs—mitigates vendor lock-in risk and ensures long-term flexibility, high energy efficiency, and sustained return on investment across decades of operation. - Maximize utilization
Due to the substantial capital expenditure required to establish an AI gigafactory, maximizing utilization is a financial necessity. Interestingly, giga-scale systems operate more efficiently and reliably under heavy, continuous workloads, as stable utilization allows hardware to fail more predictably and exposes systemic bugs early. Cultivating this environment requires fine-grained job scheduling, multi-tenant isolation, and intelligent queuing built directly into the technology stack. - Establish a comprehensive communications plan
The final consideration extends beyond technical architecture into public policy and stakeholder communication. Because many large-scale AI hubs leverage public funds or impact localized utility resources, management teams must maintain a proactive public relations strategy. Establishing reporting transparency for fund allocation, sustainability metrics, and milestones—while preparing contingency communication strategies for timeline adjustments—helps build lasting community and institutional support.
Building for the future
An AI gigafactory represents far more than a standard data center expansion; it is a generational investment that acts as a launchpad for economic progress, scientific discovery, and national competitiveness. By addressing these core engineering, architectural, and operational constraints early, technology leaders can ensure their high-performance computing initiatives deliver sustained value, resilience, and operational sovereignty in an increasingly interconnected global economy.
For more information, read 8 Considerations for Building an AI Gigafactory.
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HPE and AMD are partnering to deliver the next generation of AI and supercomputing infrastructure. Combining HPE’s deep expertise in large-scale system design with AMD EPYC™ processors and AMD Instinct™ accelerators, the two companies help customers build platforms capable of supporting the most demanding AI, modeling, simulation, and analytics workloads. Whether accelerating scientific research, enabling digital engineering, or training advanced AI models, HPE and AMD focus on what matters most: faster insights, greater efficiency, and the flexibility to scale. Together, they provide the technology foundation organizations need to solve complex problems, drive discovery, and create lasting business and societal impact.