Executive Summary
AI is accelerating faster than any previous technology wave and it is reshaping the physical infrastructure beneath it. Across the globe, a new generation of AI factories is rising, purpose-built for training and deploying foundation models at unprecedented scale. These AI Factories represent the new industrial backbone of the digital age: high-performance compute environments optimized for Artificial Intelligence production.
At the same time, traditional HPC environments are rapidly evolving into AI Factories themselves, adapting to workloads that demand new levels of performance, density, and power efficiency. This transformation is driving explosive growth in energy usage and resource demand.
Training the latest models can consume gigawatt-hours of electricity and generate hundreds of tons of carbon emissions per run. The global footprint is expanding quickly. Data centers already account for 1.5% of global electricity use, and the International Energy Agency projects demand will more than double by 2030 to 945 TWh, roughly equal to Japan’s total consumption. If left unchecked, this trend poses a serious threat to climate targets and long-term sustainability.
As AI Factories becomes a core pillar of national strategy and corporate innovation, ESG considerations are no longer optional. Regulators, investors, and the public are watching closely. Organizations must align AI growth with energy efficiency, carbon reduction, and responsible design or face mounting financial, regulatory, and reputational risk.
While most headlines focus on GPUs and cooling, data movement and storage inefficiencies drain power and inflate costs. Every time a GPU sits idle waiting for I/O or checkpointing, it wastes both energy and capital.
This is where DDN’s Data Intelligence platform makes a measurable difference. EXAScaler® was built for performance and efficiency, the foundation of a sustainable AI Factory. It moves data faster, uses less power, and keeps GPUs fully engaged. The result is more work per watt, less idle waste, and a direct path to meeting ESG objectives without compromising scale.
Understanding ESG and Its Importance
ESG stands for Environmental, Social, and Governance. These are the three core pillars used to evaluate a company’s ethical practices and overall impact.
- Environmental: Energy use, carbon emissions, water consumption, e-waste, and the total environmental footprint of the AI Factory.
- Social: Labor practices, community impact, and data privacy.
- Governance: Transparency, regulatory compliance, and ethical oversight.
ESG has evolved from a niche concept to a mainstream business imperative. Nearly 90% of S&P 500 companies now publish ESG reports, many of which emphasize climate-related impacts, and about 89% of investors take ESG factors into account when making investment decisions.
Building AI Factories without sustainability is shortsighted. It damages trust, invites regulation, and alienates investors. Responsible AI leadership now means building efficient, transparent, and climate-aligned AI Factories that demonstrate measurable progress, not just promises.
The Energy and Resource Challenge of AI at Scale
AI Factories are compute-intensive by design. Training foundation models require thousands and soon millions of power-hungry GPUs running continuously. A single training run for a large model like GPT-3 used 1,287 megawatt-hours of electricity and released 552 tons of CO₂ , equivalent to the annual power consumption of 120 U.S. homes.
The demand for more powerful AI will continue growing. Newer models like GPT-5, are even bigger, requiring even more energy, but training is only part of the story. Running these models in production can use even more power over time. Serving users through ChatGPT for example, was estimated to take 564 megawatt-hours per day. That means more energy in a few days than the entire training process.
Global Impact of AI Factories
Globally, the world’s AI Factories now consume over 415 TWh annually (2024), about 1.5% of global electricity use and rising rapidly. If current trends continue, AI-specific data centers could grow tenfold by 2030. Cooling water usage could quadruple to 664 billion liters per year, while e-waste from discarded components could reach 5 million tons.
Without new approaches, the AI Factory boom risks undermining climate progress and depleting natural resources. The challenge is not to slow innovation but to make AI Factories smarter, cleaner, and more efficient.
Why are AI Factories so Power Hungry?
A major contributor to the energy footprint of AI Factories is inefficient data flow. During training and inference, data moves constantly between storage, memory, and GPUs. Every checkpoint or dataset shuffle can involve terabytes of reads and writes. When the storage lags, GPUs become idle and underutilized while still consuming costly power.
This inefficiency compounds at scale. In a 10,000-GPU AI Factory, even a few seconds of I/O wait can translate into megawatt-hours of wasted electricity. Traditional storage architectures were never designed for this level of parallelism and throughput.
That’s where DDN EXAScaler® comes in, the data engine for modern AI Factories. By providing hyperscale-grade performance and intelligent tiering, it minimizes idle GPU time, accelerates checkpointing, and dramatically improves energy efficiency.
The Future of AI Factories
OpenAI’s “Stargate” Initiative exemplifies the scale of future AI Factories. With up to $500 billion in planned investment and millions of GPUs projected by 2030, these mega-Factories will consume gigawatts of power, demanding innovation across every layer of the stack.
Hyperscalers are responding in kind. Microsoft, Google, Amazon, and Meta are all rearchitecting their AI Factories to meet climate goals while expanding capacity. From liquid cooling to renewable integration, these companies recognize that the next generation of AI breakthroughs depends on sustainable AI Factory design.
Even NVIDIA acknowledges that energy access is now a core requirement for scaling AI Factories responsibly. Compute power alone is no longer enough; efficiency and sustainability define leadership.
Sovereign AI and National AI Factories
Governments worldwide are building sovereign AI Factories to ensure data control, digital independence, and innovation leadership. These projects are massive, often involving tens of thousands of GPUs and can strain national power grids if designed inefficiently.
Sovereign AI must mean sovereign efficiency. DDN EXAScaler® enables nations to build AI Factories that are powerful, secure, and energy-conscious. Every watt is used productively; every node optimized for longevity and throughput.
For public agencies and research institutions, that translates to lower operating expenses, fewer emissions, and sustainable digital sovereignty.
Reducing the Environmental Impact of AI Factories
Addressing the AI Factory energy challenge requires system-level innovation, and DDN EXAScaler® targets one of the most critical layers: Data Management.
- Fast NVMe Tiers: Handle massive writes instantly, reducing checkpoint times from minutes to seconds and slashing idle GPU power draw.
- Parallel I/O Pipelines: Streamline throughput across nodes to ensure every GPU remains busy, maximizing utilization and output per watt.
- Intelligent Tiering: Dynamically move cold data to lower-power media while keeping hot data accessible on NVMe, optimizing energy balance.
DDN EXAScaler® turns storage from an energy drain into an efficiency multiplier, the defining characteristic of a truly sustainable AI Factory.
ESG Benefits of AI Factories Powered by DDN
Deploying DDN EXAScaler® inside AI Factories delivers quantifiable ESG benefits:
- Lower carbon emissions: 30–50% energy reduction per AI workload.
- Higher infrastructure utilization: More work per watt, less waste.
- Regulatory readiness: Streamlined compliance with energy disclosure and efficiency mandates.
- Enhanced transparency: Measurable ESG reporting on real energy savings.
- Brand advantage: Positioning as a leader in sustainable AI Factory design.
Future Readiness and Responsible Growth
AI Factories will define the next industrial revolution. To grow responsibly, they must combine compute density with sustainability. DDN EXAScaler® enables that balance, accelerating trillion-parameter workloads while minimizing energy waste.
Sustainable AI Factories are not just a vision. They are the blueprint for how humanity can scale intelligence production without scaling emissions. The organizations that invest in efficiency now will lead the next era of AI, responsibly, profitably, and sustainably.
Conclusion
DDN is committed to powering the future of AI Factories, enabling performance, scalability, and sustainability to coexist. With EXAScaler® at the heart of this transformation, organizations can build intelligent, efficient, and responsible AI Factories that deliver both innovation and impact.
The leaders of tomorrow will be those who not only achieve remarkable AI breakthroughs but do so within sustainable AI Factories that respect planetary limits while expanding human potential.
Ready to lead the way? Let’s talk.
Victor Ghadban
Principal AI Solutions Consultant
victorg@ddn.com
Sustainable AI infrastructure refers to compute, storage, and networking systems designed to minimize environmental impact while supporting scalable AI workloads. This includes energy-efficient AI models, optimized data movement, and carbon-aware design principles.
DDN EXAScaler® accelerates I/O and minimizes GPU idle time, reducing total training time and energy consumption. Its flash-first architecture and intelligent data tiering directly cut the power usage of AI data storage.
AI data storage systems like DDN EXAScaler® directly influence energy use, carbon emissions, and hardware lifespan. Optimizing storage infrastructure helps organizations meet ESG compliance, reduce emissions, and improve ROI on AI investments.
AI factories are large-scale data infrastructure environments purpose-built for training and deploying advanced AI models. They require high-throughput, energy-efficient systems to manage workloads sustainably at scale.
Enterprises can deploy solutions like DDN EXAScaler®, which offer real-time performance, reduced energy consumption, and measurable sustainability metrics. This helps future-proof infrastructure and meet regulatory standards for AI sustainability.
