The 600kW Blueprint: Powering the Next Generation of Hyperscale AI Infrastructure

Your Blueprint to Building AI Infrastructure for 600kW+ Workloads and Beyond

Artificial intelligence (AI) is totally changing how we build and think about digital systems. What used to be regular data centres are now becoming AI Factories. These are massive, software-controlled computing hubs that mainly produce AI, a bit like how traditional factories make goods.

NVIDIA CEO Jensen Huang puts it really well: "AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories... They're not data centres of the past... They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable... called tokens."

These new facilities take in power and data and then churn out machine learning models, predictions, and digital agents on an amazing scale. Unlike older computer setups, these huge AI factories have:

• Super dense power delivery: They pack a lot of power into a small space.
• Advanced cooling systems: They use clever cooling methods, like liquid directly on chips or even submerging parts in liquid, to stop things getting too hot.
• Specialised networking: They have unique network systems designed to quickly move information between lots of graphics processing units (GPUs).

These AI factories are a new type of digital infrastructure built specifically to handle very powerful GPU clusters, using 600kW or more of power per server rack. Beyond needing dense power and cooling, they also require:

• Really fast networking: To keep communication delays to a minimum.
• AI-optimised layouts: The physical arrangement is designed perfectly for AI workloads.
• Close to data and users: To make sure information is accessed quickly and users are served efficiently.

As AI transforms industries and economies, building the physical foundation for this change isn't just an option anymore; it's absolutely crucial. This plan explains what's involved in engineering these next-generation, hyperscale AI factories from the ground up.

Why Traditional Data Centres Can't Meet AI's Demands

The computing needs of modern AI, especially deep learning, aren't just demanding; they're fundamentally different and growing at an incredible pace. Traditional data centres, built for an older era of computing, simply can't scale to meet these unique requirements due to several built-in limitations.

Modern AI, especially something called deep learning, needs a massive amount of computing power. It's not just a bit more power; it's a completely different kind of demand that's growing incredibly fast. Think of it this way: old data centres were built for a different time, like an old-fashioned factory. They were designed for everyday computer tasks, like running websites or office programmes. They simply can't cope with the unique needs of AI. AI needs things to be tightly linked, process huge amounts of data quickly, and have almost no delays – something old data centres just weren't built for.

You see, traditional data centres were designed for distributed, general-purpose computing, like running websites and business applications. They weren't made for the tightly connected, high-speed, low-delay processing that AI supercomputing clusters require.

• Extreme Computational Intensity: AI workloads, particularly model training, involve quadrillions of calculations. This necessitates specialised hardware accelerators like Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs), which are far more efficient than general-purpose Central Processing Units (CPUs) for parallel processing.
• Phased Workload Demands:
  • Pre-training: The initial phase of teaching large AI models from colossal datasets is extraordinarily compute-intensive, requiring immense clusters of accelerators.
  • Fine-tuning: Subsequent customisation of models for specific tasks still demands significant computational resources, although typically less than pre-training.
  • Inference (Test-time Scaling): Deploying trained models to generate real-time predictions requires low-latency, high-throughput capabilities to serve numerous concurrent requests, crucial for applications like autonomous vehicles or real-time language translation.
• Power and Thermal Density: AI accelerators consume significantly more power and generate far more heat than traditional servers. Conventional air-cooling systems and power delivery infrastructure are insufficient to manage racks running at 50kW, 100kW, or even 1MW per rack. This intense energy consumption not only presents a significant thermal challenge but also places immense pressure on existing power grids and sustainability commitments. Hyperscalers must therefore prioritise locations with access to substantial, reliable, and ideally, renewable energy sources, while also implementing advanced power management techniques to minimise environmental impact and operational costs. In addition to power and cooling, the physical form factor of AI clusters introduces new challenges, high rack weight, cooling manifold integration, and airflow zoning, none of which align with traditional raised-floor or hot-aisle/cold-aisle layouts.
• Interconnectivity Requirements: The massive datasets and complex models require ultra-high-speed, low-latency communication between GPUs within a server and between servers in a cluster. To avoid I/O bottlenecks, AI Factories rely on high-bandwidth, low-latency topologies such as NVIDIA NVLink, NVSwitch, and RoCE fabricsfar beyond what standard Ethernet networks in enterprise data centres can support.
• Operational Complexity: Operationally, AI clusters require specialised orchestration, thermal telemetry, and availability strategies—very different from the virtual machine-based management stacks common in traditional IT deployments.

Ultimately, traditional data centres fall short not just in capacity, but in capability. They were not built to sustain the thermal loads, floor densities, or real-time data exchange requirements of modern AI workloads. Meeting these demands requires a wholesale reinvention of digital infrastructure, one optimised for AI’s computational intensity, energy consumption, physical footprint, and operational complexity. The AI Factory emerges from this need: a purpose-built architecture that reimagines everything from the rack to the grid.

AI Token Throughput: The New Measure of Success

In the realm of AI Factories, traditional metrics such as storage capacity or standalone network bandwidth are insufficient to gauge performance. A more pertinent metric has emerged: AI token throughput, the rate at which an AI system generates output tokens during inference. This metric encapsulates the system's ability to deliver real-time predictions and content generation, serving as a direct indicator of its intelligence production capacity.

What is a Token?

In AI, particularly with large language models (LLMs) like ChatGPT, a token is a fundamental unit of text or code that the model processes. It's often a word, but it can also be a part of a word, a punctuation mark, or even a space. For example, if you make a request to ChatGPT, such as "Tell me a story about a brave knight," the words "Tell," "me," "a," "story," "about," "a," "brave," and "knight" would likely each be treated as individual tokens. However, tokenisation isn't always one-to-one with words; a word like "running" might be broken into "run" and "##ning" (two tokens), or a common phrase might be represented by a single token.

To provide a comprehensive view of AI system performance, token throughput is often considered alongside other key indicators:

• Time to First Token (TTFT): Measures the latency between input submission and the generation of the first output token, crucial for responsiveness.
• Tokens Per Second (TPS): Indicates the rate of token generation, reflecting the system's overall throughput and processing speed.
• Time Per Output Token (TPOT): Represents the average time taken to generate each token, impacting the user experience.
• Goodput: Focuses on the volume of useful output delivered within acceptable latency thresholds, ensuring quality and efficiency.

Elevated token throughput directly correlates with an AI Factory's capacity to handle extensive, concurrent inference workloads efficiently. Achieving this necessitates optimised hardware configurations, such as high-performance GPUs or TPUs, and advanced software strategies, including model parallelism and efficient batching techniques.

While factors like energy efficiency, cost management, and scalability are crucial, AI token throughput stands out as the definitive measure of an AI Factory's effectiveness. It encapsulates the facility's core mission: transforming data into actionable intelligence at scale, thereby driving innovation and competitive advantage across industries.

New Priorities for Data Centres: The 5S of AI Factories

The rise of AI Factories is making IT and data centre leaders completely rethink their priorities. In the past, data centres were planned around things like overall size (square metres, total power) and how cheap they were to run. Today, five key priorities, which we call the 5S, have become crucial for the large cloud providers (hyperscalers) building and running AI infrastructure:

• Speed: In the world of AI Factories, 'speed' means several things. First, it's about time-to-value – how quickly can you train a new AI model or add more capacity when demand suddenly increases? Hyperscalers now compete on how fast they can set up new GPU clusters or launch AI services. Cloud-native AI platforms focus on quick setup and minimal hassle; for example, offering GPU capacity by the hour, ready with AI frameworks, so development teams can innovate rapidly. Executives need to ensure their infrastructure (and partners) can deploy at "hyperspeed" both in getting hardware ready and moving data quickly. High-performance connections (low-delay networks, locations close to users) are also vital, as model training and AI predictions happen in real-time. Simply put, if your AI Factory can't keep up with the speed of experimentation and user demand, innovation will move elsewhere.

• Scale: AI workloads that used to run on a few servers now need thousands of GPUs working at the same time. 'Scale' isn't just about having big data centres; it's about smoothly expanding within and across different facilities. Hyperscale AI Factories must support huge amounts of computing power (petaflops to exaflops), millions of simultaneous AI model queries, and training runs involving trillions of parameters. This requires designs that are modular and can be easily copied. For instance, NVIDIA’s reference AI Factories are built from "pods" or blocks of GPUs that can be cloned and connected by the hundred. Cloud providers talk about "availability zones" dedicated to AI, and "AI regions" appearing where there's plenty of power. The goal is to expand AI computing almost like a utility, adding more AI Factory space with minimal disruption. Scale also means having a global presence: hyperscalers like AWS, Google, and Alibaba are expanding AI infrastructure to more regions to serve local needs while balancing workloads worldwide. If an AI service suddenly needs ten times more capacity due to a popular app or a breakthrough model, the infrastructure should be able to expand within days, not months. As Huang revealed, NVIDIA even provided a five-year roadmap visibility to partners because building AI-ready power and space takes a long time. Leading data centre operators are now planning for 100+ MW expansions proactively to ensure scale never slows down innovation.

• Sovereignty: Data sovereignty and infrastructure sovereignty have become critical in the age of AI. As AI systems are used in sensitive areas, from healthcare diagnoses to national security, where data and models are stored, and under whose laws, is a major concern. Hyperscalers must navigate a complex set of regulations that increasingly demand certain data remains within national borders, or that AI workloads are processed in locally controlled facilities for privacy and strategic reasons. The recent push for "sovereign cloud" offerings in Europe and elsewhere reflects this trend. For AI Factories, sovereignty can mean choosing data centre locations to meet legal requirements and customer trust. It's no longer just about technical specifications, but also about geopolitical and compliance positioning. For example, European cloud users might prefer (or be required by law) to use AI infrastructure hosted in the EU by EU-based providers. In China, AI infrastructure must be locally hosted due to strict data laws. Even within countries, some government or enterprise workloads demand sovereign-certified facilities, those checked for handling classified data or critical infrastructure roles. Where your AI infrastructure lives isn't just a technical choice, it's a competitive one. Latency, compliance, and sustainability are all shaped by location. Leading data centre operators choose sites based on a strategic mix of low latency, data sovereignty, and energy resilience. In practice, this means hyperscalers are investing in regions they previously left to partners and partnering with local data centre specialists to ensure sovereign coverage. The AI Factory revolution won't be a one-size-fits-all global solution; it will be a network of regionally tailored hubs that balance global scale with local control.

• Sustainability: The power-hungry nature of AI has put sustainability at the heart of the conversation. Company boards and governments are increasingly scrutinising the energy and carbon footprint of AI operations. A single large AI training run can use as much electricity as hundreds of homes; scaled across many runs, AI could significantly impact company and national energy goals. Hyperscalers are acutely aware that any perception of AI as "wasteful" or environmentally harmful could lead to regulatory or public backlash, not to mention the direct impact on energy costs. Therefore, the new mantra is "performance per watt" and designing for efficiency from scratch. Leading cloud data centres are committing to 100% renewable energy (through solar, wind, hydro, or even emerging nuclear partnerships) to power AI Factories. They're also adopting advanced cooling to reduce waste; for example, liquid cooling can drastically cut cooling power overhead and even allow heat reuse, improving PUE (Power Usage Effectiveness) dramatically. Every aspect of facility design is under the microscope for sustainability, from using sustainable building materials to implementing circular economy principles for hardware (recycling and reusing components). Importantly, hyperscalers are now reporting metrics like "carbon per AI inference" or "energy per training run" as key performance indicators. The next generation of data centres will be judged not just on capacity, but on efficiency. As a recent report put it, "the next generation of data centres won't just be measured by performance alone; they'll be judged by efficiency… boards, regulators and customers are asking: Where is the energy coming from? How efficient is your data centre? What is the carbon impact per GPU-hour?". To remain competitive (and compliant), AI Factories must be sustainable by design, aligning with global net-zero ambitions and corporate ESG commitments. Sustainability is no longer a nice-to-have Corporate Social Responsibility (CSR) item; it's a core design principle and differentiator in the AI era.

• Security: With AI becoming a backbone for everything from financial services to autonomous vehicles, the security of AI infrastructure is paramount. Here we mean both cybersecurity and physical security/resilience. On the cyber side, AI workloads often involve valuable training data (which could include personal data or proprietary information) and models that are intellectual property worth billions. Protecting these from breaches is critical; a compromised AI model or a disrupted AI service can cause immense damage. Hyperscale AI Factories are targets for attackers ranging from lone hackers to state-sponsored groups, all seeking to steal AI technology or sabotage services. This means investing in robust encryption (for data when it's stored and when it's moving), secure access controls, continuous monitoring powered by AI itself, and isolated compute environments (to prevent one client’s AI environment from affecting another’s in multi-tenant clouds). On the physical side, downtime is unacceptable; an AI Factory outage could halt operations for a business or even knock out critical infrastructure (imagine if an AI-driven power grid or hospital network fails). Therefore, AI data centres are built with extreme redundancy and hardened against threats. Many pursue Tier IV certifications for fault tolerance, and features like on-site backup power for days, multi-factor access controls, and even EMP or natural disaster protection in some cases. Additionally, supply chain security has emerged as a concern: ensuring that the chips and software powering AI are free from backdoors or vulnerabilities (which also links back to sovereignty). Security by design is a must. As one NEXTDC customer put it, their clients "rely on the ability to run AI-powered applications without interruption, for as long as they need," so having a partner that can guarantee uptime and flexibility is crucial. In practice, hyperscalers are choosing colocation providers and designs that emphasise robust risk management – from certified physical security controls to comprehensive compliance with standards (ISO 27001, SOC 2, etc.). In the AI Factory age, a security breach or prolonged outage isn't just an IT issue; it's a business-critical incident. Therefore, security and resilience permeate every layer of the 5S model, underpinning speed, scale, sovereignty, and sustainability goals with a foundation of trust and reliability.

In summary, these 5S priorities are shaping decisions at the highest levels. Hyperscaler CIOs and CTOs are now asking:

• Can our infrastructure deploy new AI capacity fast enough (Speed)?
• Can it grow to the scale we’ll need next year and five years from now (Scale)?
• Do we have the right locations and partnerships to meet data jurisdiction and governance needs (Sovereignty)?
• Are we minimising our environmental impact and energy risk even as we expand (Sustainability)?
• And can we guarantee security and resilience end-to-end so that our AI services never falter (Security)?

The AI Factory era demands a holistic approach. Success will come from excelling across all five dimensions, rather than optimising for just one. In practice, this means designing data centre solutions that are agile and fast, massively scalable, locally available and compliant, green and efficient, and rock-solid secure. That’s a tall order – but it’s exactly what the leading innovators are now building.

The Foundational Components of an AI Factory

Building an AI Factory requires a holistic rethinking of digital infrastructure, focusing on highly specialised components:

• The Compute Layer:
  • GPUs and Accelerators: These are the engines of the AI factory. Rack-scale architectures with dense, multi-GPU configurations are optimised for both training and inference.
  • AI-Specific Processors: Beyond GPUs, integrating AI-specific processors such as Google's Tensor Processing Units (TPUs), optimised for machine learning tasks, can offer significant performance and efficiency benefits.
  • Modular Architectures: Incorporating modular systems like NVIDIA's MGX platform provides flexibility and scalability, allowing for tailored configurations that meet specific AI workload requirements.
  • Advanced Liquid Cooling: Given the extreme power consumption and heat generation of AI accelerators, sophisticated liquid cooling systems (e.g., direct-to-chip, immersion cooling) are essential for thermal management, allowing for higher density and sustained performance.
  • Reference Architectures: Blueprints like NVIDIA DGX POD or SuperPOD provide validated designs for on-premises AI infrastructure deployment, streamlining the construction of dedicated AI factories.
• Networking Architecture:
  • Advanced Interconnects: Implementing high-speed interconnects like NVIDIA's NVLink and InfiniBand facilitates low-latency, high-bandwidth communication between compute nodes, essential for efficient AI model training and inference.
  • High-Performance Ethernet Fabrics: Specialised Ethernet fabrics provide the backbone for large-scale data transfer and communication between compute nodes, ensuring high throughput and minimal latency.
  • Data Processing Units (DPUs): These specialised processors offload networking, storage, and security tasks from GPUs, freeing up valuable compute resources to focus solely on AI workloads.
  • Software-Defined Networking (SDN): Adopting SDN provides dynamic network management, allowing for optimised data flow and resource allocation tailored to AI workloads.
• Storage Architecture:
  • Optimised for High-Speed Data Ingestion: AI models require access to vast datasets for training and inference, demanding storage systems capable of ultra-fast data ingestion to prevent bottlenecks.
  • Tiered Storage Solutions: Implementing a tiered approach, combining high-speed NVMe storage for active datasets with scalable object storage for archival data, can optimise performance and cost-efficiency.
  • Distributed Storage Systems: Scalable and reliable distributed file systems or object storage are essential for managing immense volumes of AI data and models, facilitating efficient data sharing and serving.
  • Data Versioning and Lineage: Incorporating data versioning and lineage tracking ensures reproducibility and accountability in AI model development, facilitating better model management and compliance.
  • Data Reuse and Feedback Loops: The architecture should support continuously feeding data generated by AI applications back into the system to refine and improve model performance, creating a virtuous cycle of intelligence.
  • Integration with Enterprise Storage: Seamless integration with existing enterprise data lakes and storage systems allows organisations to leverage their current data assets effectively.
• Security and Compliance:
  • Integrated Security Frameworks: Embedding security at every layer of the AI Factory, from hardware to application, is crucial. Solutions like Cisco's Secure AI Factory with NVIDIA emphasise the importance of integrated security measures to protect data and AI models.
  • Compliance and Governance: Establishing robust compliance and governance protocols ensures that AI operations adhere to regulatory standards and ethical guidelines, fostering trust and reliability.

Key Characteristics Defining an AI Factory

Beyond its components, an AI Factory possesses distinct characteristics that enable accelerated AI development:

• Specialised Hardware: Purpose-built with powerful accelerators (GPUs, TPUs) designed specifically for AI computations, drastically speeding up model training and inference.
• Scalable and Resilient Infrastructure: Designed to handle massive datasets and complex AI models with elasticity, ensuring solutions can be developed and deployed rapidly and reliably.
• Modular and Composable Infrastructure: Adopting a modular approach allows for flexibility and scalability, building infrastructure that can be easily reconfigured to accommodate different AI workloads, from training to inference. Components can be scaled independently, optimising resource utilisation and cost-efficiency.
• Advanced Software Stack: Provides access to a comprehensive suite of tools, including machine learning libraries, MLOps platforms, data visualisation, and model deployment pipelines, streamlining the entire AI lifecycle.
• Operational Sophistication & MLOps: Beyond hardware, AI Factories are defined by their advanced operational frameworks. They integrate Machine Learning Operations (MLOps) platforms to automate and manage the entire AI lifecycle, from data ingestion and model training pipelines to deployment, monitoring, and continuous retraining. This demands highly automated processes, sophisticated telemetry, and real-time observability, alongside a unique blend of engineering talent skilled in both infrastructure and AI workflows.
• Continuous Learning and Feedback Loops: AI Factories are designed to support continuous learning cycles. This involves implementing systems that feed real-time data back into the AI models to refine and improve their performance over time and establishing automated processes for retraining models as new data becomes available, ensuring AI systems evolve with changing data patterns.
• Sustainability and Energy Efficiency: Given the significant energy demands of AI workloads, sustainability is a key consideration. This involves selecting hardware components that offer high performance per watt to reduce overall energy consumption and implementing advanced cooling technologies, such as liquid cooling, to manage heat effectively and further improve energy efficiency.
• Robust Security & Data Governance: Ensuring the security and compliance of AI operations is paramount. Given that AI models are trained on and process vast, often sensitive datasets, AI Factories require exceptional security protocols and stringent data governance. This includes implementing comprehensive data privacy measures (like encryption and access controls), protecting intellectual property embedded within AI models, and establishing robust compliance and governance protocols to ensure adherence to relevant regulatory standards (e.g., GDPR) and ethical guidelines, fostering trust and reliability.
• Integration with Enterprise Systems: For AI Factories to deliver maximum value, seamless integration with existing enterprise systems is crucial. This is achieved by utilising API-driven architecture to connect AI capabilities with business applications, facilitating real-time decision-making and process automation, and ensuring AI systems can access and process data from enterprise data lakes, enhancing the breadth and depth of insights generated.
• Concentrated Expertise: Fosters innovation by bringing together multidisciplinary teams of data scientists, machine learning engineers, and domain experts who collaborate to develop and deploy AI solutions.

Unleashing Potential: The Benefits of AI Factories

Investing in AI Factories unlocks significant strategic advantages for organisations:

• Faster Time-to-Market: Accelerates the development and deployment of AI solutions, crucial for staying competitive and responsive to market changes.
• Improved Efficiency & Cost-Effectiveness: Optimises the AI development process through specialised infrastructure, reducing total cost of ownership for AI-intensive workloads.
• Enhanced Innovation: Creates a dedicated environment where experimentation and creativity thrive, enabling the rapid development of breakthrough AI capabilities.
• Increased Agility: Empowers organisations to pivot quickly by rapidly deploying AI-driven strategies in response to emerging opportunities or threats.
• Sustainable Competitive Edge: Establishes long-term differentiation by building proprietary AI capabilities in-house.
• Resilience & Availability: Delivers mission-critical reliability with high availability, fault tolerance, and workload flexibility.
• Greener AI at Scale: Enables energy- and cooling-efficient AI operations that align with enterprise sustainability and ESG goals.
• Data Sovereignty & Compliance: Maintains full control over sensitive data and ensures compliance with complex cross-border regulatory frameworks.
• Talent Magnetism: Serves as a centre of excellence that attracts world-class AI and engineering talent.

AI Factories in Action: Leading the Charge

The blueprint for today's AI Factory was largely created by pioneers in large-scale internet services like Google, AWS, Alibaba, Tencent, and ByteDance. Their huge investments and innovative methods have turned traditional data centres into powerhouses of intelligence, setting the standard for others to follow.

Beyond these tech giants, the influence of AI Factories is expanding, with various organisations showing the power of specially built AI infrastructure:

• Uber – Michelangelo: Uber's ML platform, Michelangelo, is integral to its operations, enabling real-time predictions and optimizations across the platform. It supports over 10 million predictions per second, facilitating tasks such as:

  • Intelligent Dispatching: Matching riders with drivers efficiently by analyzing thousands of features in real-time.
  • Dynamic Pricing: Adjusting fares based on supply and demand dynamics to balance the marketplace.
  • Real-Time Route Optimisation: Providing drivers with optimal routes by considering traffic conditions and other variables. Michelangelo's scalability and integration into Uber's infrastructure allow for seamless deployment and management of ML models across various services, including Uber Eats and freight operations.

• Netflix – Metaflow: Netflix developed Metaflow, a human-centric framework designed to streamline the development and deployment of ML models. Metaflow empowers data scientists and engineers to:

  • Build Scalable ML Systems: Facilitating the creation of production-grade systems for personalized recommendations.
  • Manage Complex Workflows: Handling the entire ML lifecycle, from data collection to model deployment.
  • Enhance Viewer Engagement: Delivering personalized content recommendations to improve user satisfaction. By abstracting the complexities of ML infrastructure, Metaflow enables rapid experimentation and iteration, contributing to Netflix's ability to provide tailored content to its users.

• Airbnb – Bighead: Airbnb's Bighead is an end-to-end ML platform that supports various applications across the company, including:

  • Fraud Prevention: Detecting and mitigating fraudulent activities on the platform.
  • Search Ranking: Optimizing the order of listings to match user preferences and behaviors.
  • Customer Experience: Enhancing personalization and user interactions through ML-driven insights. Bighead provides a consistent and scalable environment for ML model development, enabling teams to build and deploy models efficiently.

While webscale companies currently lead the way, the strategic advantages of AI Factories are undeniable. As more organisations recognise the transformative potential of AI, we can expect a widespread adoption of these specialised facilities across diverse industries in the near future.

The AI Factory Imperative: Build for the Intelligence Economy

The shift from traditional data centres to purpose-built AI Factories marks a critical inflection point for organisations seeking to fully realise the potential of artificial intelligence. This is not a linear upgrade, it’s a foundational transformation of compute infrastructure, network architecture, and operational readiness to support the demands of next-generation intelligence workloads.

By investing in AI Factories, forward-looking enterprises equip themselves to handle the exponential growth in AI models, data volumes, and power density. They gain the strategic capability to innovate faster, compete smarter, and lead in an economy defined by intelligence.

This new era requires decisive action. Success hinges on infrastructure partners who can deliver across the “5S” dimensions—Speed, Scale, Sovereignty, Sustainability, and Security—without compromise.

Your AI Factory Starts Here - With NEXTDC

The intelligence economy isn’t on the horizon, it’s already transforming industries. To lead in this new era, you need infrastructure that’s not just ready for AI, but purpose-built for it.

NEXTDC’s high-performance, AI-optimised data centre platform delivers the density, sovereignty, and sustainability your workloads demand today and tomorrow.

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