How 1GW AI Data Centers Are Transforming Power, Cooling, and High-Density Computing

The artificial intelligence revolution is entering a new phase — one where computing power, energy infrastructure, and thermal management are becoming the foundation of global AI competition.As large language models (LLMs), generative AI applications, and high-performance computing (HPC) continue to expand, technology companies are investing billions of dollars into next-generation data centers designed specifically for AI workloads.

Meta’s latest AI infrastructure expansion is one of the clearest examples of this trend.The company is reportedly investing approximately $10 billion to develop a large-scale AI data center project in Canada, with a planned power capacity of 1 gigawatt (GW). Located in Alberta’s Sturgeon County, the facility is expected to become one of Meta’s largest overseas data center projects and a critical component of its global artificial intelligence infrastructure strategy.

More importantly, this project highlights a major industry transformation:

Future AI data centers will no longer be limited by computing hardware alone — they will be defined by power availability, cooling efficiency, and advanced liquid cooling technologies.

Meta’s Canada AI Data Center: A New Era of Gigawatt-Scale Computing

Traditional data centers were mainly designed to support enterprise applications, cloud services, and general computing workloads.

However, AI infrastructure is fundamentally different.

• Modern AI training and inference require:
• Thousands of GPUs operating simultaneously;
• Extremely high rack power density;
• Continuous thermal management;
• Reliable energy supply;

Advanced cooling architectures. Meta’s planned Canadian AI data center reflects this new generation of infrastructure.

The project features:

• Investment scale: Approximately $10 billion;
• Power capacity: Up to 1GW;
• Location: Sturgeon County, Alberta, Canada;
• Purpose: Supporting AI computing, cloud infrastructure, and future digital services.

A 1GW data center represents a massive computing facility capable of supporting thousands of high-performance AI servers.

Compared with traditional enterprise data centers, AI-focused facilities require significantly greater attention to electrical infrastructure and heat dissipation.

Why Did Meta Choose Canada for a Gigawatt AI Data Center?

The location of AI data centers is changing.

In the past, companies prioritized: Internet connectivity; Real estate costs; Proximity to customers; Availability of skilled workers.

Today, AI data centers increasingly prioritize:

1. Reliable Energy Availability

AI servers consume significantly more power than traditional IT equipment.

High-performance GPU systems can generate enormous heat loads, making electricity one of the most important resources for future data centers.

Alberta provides several advantages: Established energy infrastructure; Strong industrial power capabilities; Access to natural gas and renewable energy resources; Stable grid development opportunities.

For a 1GW-scale AI facility, access to reliable power can determine whether a project succeeds.

2. Favorable Climate Conditions

Canada’s colder climate provides natural advantages for data center cooling.

Lower ambient temperatures can reduce cooling requirements during certain operating conditions.

However, climate alone is no longer enough.

The increasing thermal density of AI servers has exceeded what traditional air cooling systems can efficiently handle.

A modern AI rack can generate heat levels far beyond conventional data center designs.

This is where liquid cooling becomes essential.

AI Data Centers Are Reaching the Limits of Traditional Air Cooling

For decades, air cooling has been the standard method for managing data center temperatures.

Traditional cooling systems rely on: Air conditioners; Fans; Hot aisle/cold aisle containment;

Air circulation systems.

While effective for conventional servers, air cooling faces major challenges in AI environments.

Challenge 1: Increasing Rack Power Density

Traditional enterprise data centers typically operate at: 5–15kW per rack; Modern AI data centers may require; 50kW per rack; 100kW+ per rack;

Even higher densities in future AI clusters.

Moving this amount of heat through air requires extremely large airflow systems, increasing complexity and energy consumption.

Challenge 2: Higher Cooling Energy Consumption

Cooling systems can represent a significant portion of total data center energy usage.

As AI computing grows, relying only on air cooling can increase: Electricity consumption; Infrastructure costs; Data center footprint.

Challenge 3: Future AI Hardware Requires Better Thermal Management

Next-generation AI platforms, including advanced GPU servers and high-performance computing systems, are designed around higher thermal performance requirements.

Without efficient cooling solutions, organizations may face: Reduced computing efficiency; Hardware limitations; Higher operational costs.

Liquid Cooling Becomes the Foundation of Next-Generation AI Infrastructure

Liquid cooling is becoming one of the most important technologies for modern AI data centers.

Unlike air cooling, liquid cooling transfers heat directly from high-performance components using coolant.

Because liquids have much higher thermal conductivity than air, they can remove heat more efficiently from: GPUs; CPUs; AI accelerators; High-density computing modules.

Main Types of Data Center Liquid Cooling Technologies

Cold Plate Liquid Cooling

Cold plate liquid cooling is currently one of the most widely adopted solutions for AI servers.

The technology uses specially designed cold plates attached directly to high-heat components.

Heat is transferred through coolant circulation to a cooling distribution unit (CDU).

Advantages include:

High cooling efficiency;

Compatibility with existing server architectures;

Support for AI GPU platforms;

Easier maintenance compared with full immersion systems.

Cold plate liquid cooling is becoming a preferred solution for AI training clusters and enterprise AI infrastructure.

Immersion Cooling

Immersion cooling places servers directly into dielectric coolant.

Advantages:

Extremely high thermal performance;

Reduced dependency on fans;

Support for ultra-high-density computing.

It is especially suitable for future extreme-performance computing environments.

Meta’s Liquid Cooling Strategy Reflects a Global Data Center Trend

Meta’s Canadian AI data center project demonstrates an important industry direction.

The future of AI infrastructure is not only about deploying more GPUs.

It is about building complete ecosystems that combine:

• Advanced computing;
• Reliable power systems;
• Efficient cooling technologies;
• Sustainable operation models.

Meta’s adoption of advanced cooling approaches, including closed-loop cooling systems, reflects the broader movement toward more energy-efficient AI data centers.

As AI workloads continue expanding, liquid cooling will increasingly move from an optional technology to a standard requirement.

How Lian Li Supports the Future of AI Data Centers with Liquid Cooling Solutions

As AI computing continues to accelerate, Lian Li provides advanced liquid cooling solutions designed for high-density data centers, AI servers, and HPC applications.

Lian Li’s liquid cooling solutions include:

Data Center Cold Plate Liquid Cooling Systems

Designed for modern AI and high-performance computing environments, Lian Li cold plate liquid cooling systems provide efficient thermal management through:

• Liquid cooling cabinets;
• Coolant Distribution Units (CDUs);
• Precision coolant circulation systems;
• Intelligent monitoring solutions.

These systems support:

• AI training clusters;
• GPU computing platforms;
• Cloud data centers;
• HPC environments.

High-Density Liquid Cooling Rack Solutions

AI workloads require higher rack density and improved thermal performance.

Lian Li liquid cooling racks are designed to support:

• High-power AI servers;
• GPU-intensive workloads;
• Next-generation computing platforms;
• 100kW+ rack-level cooling requirements.

Benefits include:

• Higher computing density;
• Improved energy efficiency;
• Reduced cooling limitations;
• Faster deployment.

Customized Liquid Cooling Data Center Solutions

Every AI infrastructure project has different requirements.

Lian Li provides customized liquid cooling solutions covering:

• System architecture design;
• CDU configuration;
• Cooling loop planning;
• Rack integration;
• Complete deployment support.

By combining engineering expertise with advanced liquid cooling technology, Lian Li helps organizations build reliable, scalable, and energy-efficient AI data centers.

Conclusion: Liquid Cooling Will Define the Next Generation of AI Data Centers

Meta’s multi-billion-dollar investment in a 1GW AI data center in Canada is more than a single infrastructure project.

It represents a global shift toward a new generation of computing facilities.

As AI models become larger and GPU workloads become more intensive, data centers must overcome three major challenges:

• Power availability;
• Computing density;
• Thermal management.

Liquid cooling provides the foundation needed to address these challenges.

From AI factories and cloud platforms to HPC environments, advanced liquid cooling technology will play a critical role in enabling the next era of digital intelligence.Lian Li continues to develop innovative liquid cooling solutions that help global organizations build more efficient, reliable, and future-ready AI data center infrastructure.