Inside the AI Factory

Buidling Sustainable AI Infrastructure for Scalable AI

AI is accelerating faster than any previous technological wave, and it is reshaping the physical infrastructure beneath it.

The Strategic Imperative for AI Factories

AI Factories — whether inside enterprises, sovereign initiatives, or NVIDIA Cloud Partner (NCP) deployments — exist for one purpose:

To convert massive GPU investments into business results at the fastest speed, lowest cost, and lowest risk. Today’s GPU clusters can consume $100M+ of capital per deployment. But unless they’re continuously and efficiently fed with data, they underperform — often by 40– 60%. Many existing solutions struggle to support the full demands of end-to-end AI pipelines. This often results in underutilized GPU infrastructure and lower returns on AI investments.

Across the globe, a new generation of AI factories is rising – purpose-built for training and deploying foundation models at unprecedented scale. At the same time, traditional HPC environments are rapidly shifting toward AI workloads, demanding new levels of performance, density, and power consumption. 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 that 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 infrastructure becomes a core pillar of national strategy and corporate innovation, ESG (Environmental, Social, and Governance) 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 risks.

While most headlines focus on GPUs and cooling, storage inefficiencies drain power and inflate costs. Every time a GPU sits idle waiting for slow I/O or checkpointing, it wastes energy and a company’s budget.

This is where DDN’s Data Intelligence platform makes a difference. DDN is built for performance and efficiency, allowing it to transform the economics of AI infrastructure. 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.

DDN: The Core of Sustainable AI Factories

DDN is not just storage — it is the data intelligence platform that ensures GPUs, the engines of the AI factory, are continuously fed and fully utilized.

• 90–95% GPU Utilization
  DDN consistently achieves near-full GPU usage, unlocking up to 2× ROI over competitors.
• Complete Pipeline Coverage
  From ingestion to training, inference, and multi-cloud movement — DDN handles it all without compromise.
• Lowest TCO
  DDN enables AI factories to scale with less infrastructure, lower power and cooling requirements, and a smaller carbon footprint.

Why ESG Matters for AI Infrastructure

ESG stands for Environmental, Social, and Governance – three core pillars used to evaluate a company’s ethical practices and overall impact:

• ENVIRONMENTAL
  Includes energy use, carbon emissions, water consumption, electronic waste, and the total environmental footprint of the infrastructure.
• GOVERNANCE
  Includes energy use, carbon emissions, water consumption, electronic waste, and the total environmental footprint of the infrastructure.
• SOCIAL
  Covers labor practices, community impact, and data privacy.

ESG has evolved from a niche concept to a mainstream business imperative. Nearly 90% of S&P 500 organizations now publish ESG reports, many of which emphasize climate-related impacts. About 89% of investors take ESG factors into account when making investment decisions.

Investors are backing organizations that take environmental and social responsibility seriously. They see them as better prepared for the future. Meanwhile, 76% of consumers say they would walk away from a brand that ignores climate or social issues.

Pursuing AI dominance without sustainability planning undermines long-term success. It damages trust, invites regulation, and turns off investors. Customers walk away from organizations that don’t take responsibility. Following ESG shows people you mean business – it builds credibility and adds long-term value. In today’s world of AI, this is what leadership looks like.

The Energy Footprint of AI at Scale

GPU Underutilization is a Silent Drain

In traditional infrastructure, storage bottlenecks lead to 40–60% GPU idleness — wasting both energy and budget. DDN eliminates this inefficiency through optimized I/O and orchestration.

Training AI models takes thousands of GPUs and is scaling up to millions of power-hungry GPUs running nonstop for days or even weeks. A single training run for a large model like GPT-3 used an estimated 1,287 megawatt-hours of electricity and released 552 tons of carbon dioxide – the same as what 120 U.S. homes use in a year or the emissions from 110 cars.

The demand for more powerful AI will continue growing. Newer models like GPT-4, Google’s PaLM, and Meta’s LLaMA 2 are even larger and require even more energy. But training is only part of the story. Running these models in production can use even more power over time. For example, serving users through GPT-3 was estimated to take 564 megawatt-hours per day – that’s more energy in a few days than the entire training process.

Global Impact

Today, the world’s data centers consume an estimated 415 TWh annually (as of 2024) – about 1.5% of global electricity use. That share is rising fast as AI adoption accelerates.

As global demand for energy to power AI grows, the environmental stakes will go far beyond electricity. A recent analysis by Greenpeace warned that AI-specific data centers could drive a sharp increase in resource consumption by 2030. In one scenario, global electricity use from AI data centers could grow 10X from around 50 terawatt-hours in 2023 to approximately 550 terawatt-hours in 2030.

When combined with other servers in data centers, total data center consumption could reach 1,400 terawatt-hours by the end of the decade. Cooling water usage will nearly quadruple, reaching 664 billion liters per year by 2030. During that same period, discarded hardware could create up to 5 million tons of electronic waste.

Each of these factors carries serious environmental and social consequences. If energy and resource use are not brought under control as AI infrastructure expands, the damage to the planet could be severe. The result may be an AI boom that undermines climate progress and drains natural resources. This is the future responsible leaders are working hard to prevent.

The Hidden Energy Cost of AI Data Movement

A key contributor to AI’s energy footprint is the inefficiency in how data is moved and stored during computation.

Training modern AI models involves iterating over massive datasets and frequently writing checkpoints for fault-tolerance. Each checkpoint can be hundreds of gigabytes to multiple terabytes of data being written out. Traditional storage systems struggle to handle this torrent of data quickly. GPUs end up sitting idle, waiting for checkpoints to write or data to stream from storage. During those idle moments, the GPUs still draw power but contribute no useful work, effectively wasting energy.

In large distributed AI jobs, these I/O bottlenecks can significantly extend total training time, meaning tens of thousands of GPUs running longer and burning more electricity than necessary. Inefficient data movement directly inflates the power consumption and carbon cost of AI training. Many organizations focus on the power of GPUs or cooling systems when thinking about efficiency but overlook the critical role of storage I/O.

Google and OpenAI have spent heavily on custom filesystems and faster data pipelines to avoid costly GPU idle time during training, but not every organization can build custom infrastructure like the giant hyperscalers. This is where solutions like DDN EXAScaler® come into play, bringing hyperscale-grade efficiency in data handling to a broader market.

Unlike general-purpose storage from vendors like VAST and Pure, DDN is purpose-built for AI. It removes I/O bottlenecks that leave expensive GPUs idle. The result: faster AI cycles, lower emissions, and more value from every infrastructure dollar.

The Future of AI Infrastructure and Sustainability

Only DDN is Ready for the Scale of What’s Coming

As hyperscalers and NCPs race to build next-gen AI data centers, they face three barriers: underutilized GPUs, bloated costs, and energy risk. DDN solves all three. With seamless NVIDIA integration and proven production deployments, it’s the trusted platform for the world’s most demanding AI factories.

OpenAI’s “Stargate” Initiative

OpenAI, together with partners like Microsoft, Oracle, and others, has announced a plan to invest up to $500 billion in new AI-dedicated data centers over the next four years. This Stargate project could deploy one to five million GPUs by 2030 to support OpenAI’s research and services. Such an immense compute footprint would require several gigawatts of power – prompting discussions around building dedicated off-grid power plants like small modular nuclear reactors to supply these AI farms. It’s an example of how AI at scale becomes an energy infrastructure concern. If those millions of GPUs spend significant time waiting on slow storage or networks, the energy waste and cost would be astronomical. Optimizing every part of the stack – from chips to cooling to storage – is vital for projects like Stargate to be viable and responsible.

Hyperscalers Ramping Up

Microsoft’s CEO revealed the company is spending $80 billion in a single year on data center infrastructure to meet AI demand. Google and Amazon are making similar investments. Each of these organizations has public climate pledges (e.g. Google aims for 24/7 carbon-free energy by 2030, and Microsoft aims to be carbon-negative by 2030, etc.) Achieving these goals while adding tens of new AI data centers will require extreme efficiency and innovation. Meta claims its data centers run on 100% renewable energy and are among the world’s most efficient, with all new builds aiming for LEED Gold or better in sustainability certifications. Google has been carbon-neutral since 2007 and is investing in novel cooling like liquid immersion and outside air cooling to reduce data center power use. NVIDIA, whose GPUs power most of these AI clusters, is also aware of the power dilemma. The company noted that building AI infrastructure now requires not just advanced chips and land, but “sustainable energy access” as a top concern. The industry recognizes that tomorrow’s AI breakthroughs depend on responsible energy use just as much as raw compute power.

Sovereign AI and the Need for Energy-Efficient Infrastructure

As governments invest heavily in sovereign AI to secure digital autonomy and national innovation, the infrastructure behind it must be both powerful and sustainable.

These national-scale initiatives often involve massive compute clusters, sensitive data, and long-term commitments. But without careful planning, they can create a heavy environmental and energy burden.

Sovereign AI is not just about control over models and data. It’s about controlling the energy footprint and long-term costs of national infrastructure. Countries that build these systems without efficiency in mind may find themselves facing soaring energy bills, strained power grids, and difficulty meeting their own climate targets.

DDN EXAScaler® is ideal for sovereign AI programs. It provides a way to deliver scalable AI capabilities while reducing total cost of ownership, improving power efficiency, and supporting ESG alignment. It ensures that every watt is used productively, reducing idle compute, shrinking energy usage, and extending infrastructure life.

For public agencies and national labs, this translates to lower operating expenses, fewer emissions, and infrastructure that is future-ready. Efficient design becomes a strategic asset – one that helps governments lead in AI innovation without compromising their environmental responsibilities.

National and sovereign AI programs trust DDN because it uniquely combines data performance, cost-efficiency, and deployment expertise. This is not theoretical — DDN is already powering secure, sustainable AI factories across leading national labs and hyperscalers.

DDN EXAScaler®. Smarter Storage That Powers the Entire AI Factory.

Addressing the AI energy challenge requires an integrated approach. DDN EXAScaler® tackles a crucial piece of that puzzle in the storage and data pipeline.

DDN EXAScaler® is a high-performance parallel file system and data intelligence solution built for data-intensive workloads like AI training. Its architecture is engineered for speed and efficiency, completing data operations faster and with less energy. Here’s how it curbs energy consumption in AI data centers:

• Blazing Fast NVMe Storage
  DDN uses modern NVMe flash tiers to handle large writes at high speed. Tasks like model checkpointing that once took minutes now complete in seconds. This reduces GPU idle time and shortens training cycles, leading to lower energy use and higher efficiency. Internal tests show up to 90% less power consumption during checkpoints compared to traditional disk systems. Writing a multi-terabyte snapshot in 20 seconds instead of 5 minutes adds up to massive energy savings at scale.
• Parallel I/O Pipelines
  DDN orchestrates simultaneous read and write streams across the entire storage cluster. By striping data across servers and using RDMA, it delivers full-speed throughput to every GPU. This keeps storage from becoming a bottleneck, maximizes utilization, and reduces idle time. Compared to legacy NAS or SAN systems, it can cut I/O wait time by up to 20%, leading to faster jobs and more output per watt.
• Intelligent Data Tiering
  DDN keeps frequently used data on fast NVMe flash and automatically moves cold data to lower-power media. This reduces idle energy draw without sacrificing access – optimizing performance while cutting unnecessary power use.
• Optimized Hardware Utilization
  DDN delivers high capacity in a compact, energy-efficient footprint. It replaces siloed systems, lowers power per terabyte, and offloads I/O tasks like compression to reduce the need for extra servers and their energy costs. DDN’s platform turns storage from a cost center into a performance engine — enabling AI factories to scale sustainably while achieving 2× the business output compared to traditional systems.

DDN EXAScaler® speeds up AI workflows and cuts energy waste. In one benchmark, it used ten times less storage power than a traditional HDD system by finishing I/O faster and reducing total run time. That frees up energy for more experiments and training within the same budget. It also improves resilience. With built-in data protection and recovery, DDN EXAScaler® avoids costly reruns and extends hardware life. Fewer failures mean less e-waste and fewer replacements.

Fast I/O, smart tiering, and efficient design let you run more AI with less power. You get better performance, lower costs, and real progress on sustainability.

ESG Gains from Energy-Efficient AI Infrastructure

Implementing DDN EXAScaler® as part of an AI infrastructure can yield numerous ESG-related benefits that are both quantifiable and strategic for an organization:

• LOWER CARBON EMISSIONS
  The most direct environmental benefit is reduced energy consumption for AI workflows, which in turn cuts greenhouse gas emissions. DDN shortens training times and eliminates idle power, allowing a company to run the same AI workload with 30–50% less total energy. If the electricity comes from the grid, that difference could mean tens of metric tons of CO₂ avoided per training run.
• HIGHER INFRASTRUCTURE UTILIZATION WITH LESS WASTE
  DDN enables higher utilization of expensive compute resources (GPUs, TPUs, DPUs, etc.). In ESG terms, this is an efficiency gain with more output per unit of input. From a business perspective, it also improves ROI on capital, with GPUs spending more time producing results and less time waiting. By eliminating idle GPU time, DDN delivers not just sustainability wins — but direct business value. Every $100M in GPU spend delivers $100M+ in useful output with DDN. Other platforms leave tens of millions stranded in underused hardware.
• REGULATORY COMPLIANCE AND FUTUREPROOFING
  As mentioned, regulators are moving toward requiring disclosures of data center energy usage, efficiency metrics, and even imposing limits or efficiency standards. Using a data intelligence solution like DDN can make it easier to comply with current and future regulations.
• REDUCED CARBON FOOTPRINT OF STORAGE
  DDN’s flash-first design uses less energy and completes jobs faster, which means less time keeping drives active. That lowers the carbon footprint of AI workloads and helps hit efficiency targets like watts per terabyte.
• ENHANCED REPORTING AND ACCOUNTABILITY
  DDN gives organizations measurable proof of sustainability. Its metrics – like energy savings and reduced idle time – can be used directly in ESG reports to show real operational improvements, not just promises. That kind of transparency builds trust with regulators, investors, and customers.
• BRAND AND INVESTOR ADVANTAGE
  Sustainable AI is becoming a market differentiator. With DDN, organizations can promote their AI services as energy-efficient and lower-carbon. This resonates with clients who have their own ESG goals and helps improve ESG scores that influence investment decisions.

In summary, adopting DDN EXAScaler® yields measurable ESG wins: less energy, better resource efficiency, easier compliance, and a story of innovation in sustainability.

Scaling AI Responsibly. Infrastructure for the Future.

AI is growing fast, models are getting larger, workloads heavier, and sustainability pressures higher.

Scaling AI without scaling emissions requires infrastructure built for efficiency from the start. DDN EXAScaler® is designed for that future. It handles massive I/O loads without draining energy, helping organizations run trillion-parameter models without wasting power. As regulations tighten and energy costs rise, efficient infrastructure becomes a strategic advantage.

Sustainable AI is not a contradiction. With the right design choices – like fast and efficient storage – organizations can drive innovation while meeting ESG goals. Fewer devices, longer lifespans, and less cooling demand all contribute to a lower environmental impact.

The organizations that lead the next era of AI will be the ones that scale responsibly. Investing in efficient infrastructure today means staying competitive, compliant, and resilient tomorrow.

DDN is committed to building the future of AI. With DDN and our broader sustainable infrastructure efforts, we aim to support the AI revolution in a way that honors environmental and governance commitments at every step. The coming years will be defining for AI and for climate action. By tying them together through responsible infrastructure, we can drive positive outcomes on both fronts.