APAC's Hyperscaler AI Infrastructure Race: Key Strategies for Dominance

Across Asia-Pacific, hyperscalers are accelerating their AI infrastructure strategies, identifying new growth markets, evaluating critical infrastructure capabilities, and preparing to deploy next-generation workloads at unprecedented scale. This aggressive push is driven by the insatiable demand for cutting-edge AI services, from the computational intensity of generative AI and large language models (LLMs) to the rapid-fire requirements of real-time inference and complex scientific simulations. The region, with its vast and growing digital economies, diverse regulatory landscapes, and burgeoning talent pools, presents both immense opportunity and unique infrastructure challenges.

Informed by extensive industry conversations, powerful infrastructure demand signals, and recurring themes across the hyperscale ecosystem, we observe a distinct shift in priorities. These aren't just incremental adjustments; they represent fundamental re-evaluations of what constitutes an optimal data centre partner in the age of AI. The race to achieve AI dominance in this dynamic region means that traditional data centre metrics, once sufficient, now fall critically short.

Jensen Huang's Vision: The Imperative for 'AI Factories' in APAC

NVIDIA CEO Jensen Huang has been a leading voice in articulating the transformative shift in computing. His vision for the future of AI infrastructure resonates deeply with the strategic imperatives now facing hyperscalers in APAC. Huang maintains that AI isn't merely a new application; it's a new form of industrial infrastructure, akin to electricity or the internet, that demands 'AI Factories.'

These 'AI Factories,' as Huang describes them, aren't the data centres of the past. They're specialised, purpose-built facilities designed to efficiently produce intelligence, consuming vast amounts of energy to generate 'tokens.' His core messages for organisations looking to scale AI directly inform the rigorous criteria hyperscalers are now applying to their APAC deployments:

Embrace 'AI Factories' as the New Computing Infrastructure:** Huang stresses that traditional data centres are fundamentally unsuited for the demands of modern AI. Hyperscalers must invest in infrastructure designed from the ground up for continuous, high-density AI production.
Accelerate Everything with Full-Stack Computing:** With 'general purpose computing running out of steam,' Huang advocates for accelerated computing, leveraging GPUs and specialised networking – coupled with a robust software stack to achieve unprecedented speed and efficiency.
Prioritise Efficiency and Sustainability:** Huang highlights the need for solutions that drive down the 'total cost of ownership' by maximising performance per watt and ensuring sustainable operations, even as compute consumption explodes.
Partner to Build, Don't Just Buy:** Recognising the complexity, Huang emphasises that building these 'AI Factories' often requires deep partnerships with providers who can deliver integrated, full-stack solutions.
Strategic & Sovereign Deployment:** Implicit in his discussions on national AI strategies, Huang underlines the need for secure, compliant, and geopolitically agile infrastructure.

AI at Scale: The 10 Critical Infrastructure Imperatives for Hyperscalers

Today, ten key priorities are acutely shaping their strategic direction, reflecting the complex and demanding nature of AI at scale:

**Unprecedented Power & Density Requirements**

The exponential growth of AI workloads, particularly for training, demands power densities far exceeding traditional data centre capabilities, often reaching hundreds of kilowatts per rack. Hyperscalers need facilities designed from the ground up to support this, with clear pathways to even higher future requirements like 1MW+ per rack. This isn't just about megawatts; it's about the very physics of power delivery, sophisticated in-rack power management, and redundant power distribution to ensure continuous operation.

**Advanced Thermal Management Solutions**

Closely linked to power, the intense heat generated by AI compute makes conventional air cooling obsolete. Hyperscalers are urgently seeking data centres that have either implemented or can rapidly deploy advanced liquid cooling solutions, including direct-to-chip (D2C) and potentially immersion cooling, along with robust cooling redundancy (N+1/2N) to maintain stable operating temperatures and prevent throttling.

**Ultra-Low Latency & Robust Interconnectivity**

AI's real-time inference needs and the massive data transfers inherent in training require networks that are not only high-bandwidth but also exceptionally resilient and low-latency. Physical proximity to major internet exchange points (IXPs), leading cloud on-ramps, and diverse, redundant fibre routes becomes paramount. The internal data centre network must also be high-bandwidth (e.g., InfiniBand, 400GbE) to support rapid GPU-to-GPU communication within AI clusters.

**Rapid Deployment Velocity & Scalability**

The AI landscape is evolving at breakneck speed. Hyperscalers need to deploy new infrastructure, scale existing clusters, and iterate on models faster than ever to maintain a competitive edge. Prolonged build times or slow provisioning cycles can equate to significant lost market opportunity. They require partners who can deliver ready-to-deploy, AI-optimised infrastructure swiftly, whether through build-to-suit engagements, rapidly provisioned modular solutions, or phased expansions.

**Verifiable Sustainability & Energy Efficiency**

With increasing scrutiny on energy consumption and environmental impact, hyperscalers are prioritising data centres that demonstrate a clear commitment to renewable energy sources, industry-leading energy efficiency (low PUE), and effective water management. This includes direct procurement of green power, participation in verifiable renewable energy programmes, and a clear roadmap to net-zero operations.

**Stringent Sovereignty & Regulatory Compliance**

Geopolitical considerations and evolving data privacy laws make data residency and regulatory compliance non-negotiable. Hyperscalers demand data centres that not only comply with local laws but also offer granular controls and verifiable assurances regarding data residency, access, and security, ensuring sensitive AI models and training data remain within specified geographic or jurisdictional boundaries.

**Comprehensive Physical & Cyber Security**

The immense value of AI intellectual property and sensitive data necessitates robust, multi-layered security. This includes stringent physical security measures like multi-factor authentication, biometric access controls, 24/7 surveillance, and highly trained on-site personnel. Equally vital are the data centre's internal cybersecurity posture, network segmentation policies, DDoS protection, and adherence to leading security certifications (e.g., ISO 27001).

**Vibrant Ecosystem Access & Partnerships**

Seamless integration with the broader digital ecosystem is crucial. Hyperscalers look for data centres offering direct connectivity and peering with major cloud providers, telecommunications carriers, and other strategic partners. Equally important is access to subsea cable landing stations, which provide ultra-low-latency routes to international markets, global redundancy, and high-bandwidth capacity for data-intensive AI workloads.

**Flexible and Resilient Power Supply**

Hyperscalers require data centres with flexible and resilient power supply systems to ensure continuous operation. This includes redundant power sources, uninterruptible power supplies (UPS), and backup generators to handle power outages and maintain stable operations.

**High-Performance Storage Solutions**

AI workloads demand high-performance storage solutions that can handle massive data sets and support rapid data access. Hyperscalers need data centres with scalable, high-speed storage options, including NVMe and SSD technologies, to ensure optimal performance and data integrity.

These strategic imperatives are the bedrock upon which hyperscalers must build their competitive advantage in APAC, driving the race for AI dominance in the region.