# Data centres: Engineering methods

Air, free cooling, cold plates or tanks of fluid

Source page: https://www.materialinsights.org/energy/e_data_centres/methods/

What this tab covers The engineering methods that can do this conversion, compared on efficiency, scale, economics, limitations and reliability.

There is one basic conversion, electricity to computation to heat, but several ways to build and cool the facility.

#### Comparison

| Method | Efficiency | Scale | Economics | Limitations | Reliability |
|---|---|---|---|---|---|
| **Enterprise or legacy air-cooled data centre**^[2](https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf?version=0) | Older and smaller sites tend to sit above the 1.54 average PUE | Under a few MW | Low utilisation of servers and cooling raises cost per unit of computing | Cannot cool high-density AI racks | Depends on redundancy level; power problems caused 45% of impactful outages in the 2025 survey |
| **Hyperscale cloud campus with free cooling**^[1](https://www.iea.org/reports/energy-and-ai)^[3](https://datacenters.google/efficiency) | Google fleet PUE 1.09 (2025) | Hundreds of MW to over 1 GW per campus; a typical AI data centre uses as much electricity as 100,000 households, the largest under construction 20 times more | Lowest cost per unit through scale and utilisation | Needs very large grid connections and land; evaporative cooling uses water | Redundancy spread across sites as well as within them |
| **AI data centre with direct-to-chip liquid cooling**^[11](https://www.nvidia.com/en-us/data-center/gb200-nvl72/)^[12](https://www.datacenterdynamics.com/en/news/vertiv-and-nvidia-collab-on-7mw-reference-architecture-for-gb200-nvl72) | GB200 NVL72 rack about 2.7 TFLOPS per watt (FP16 with sparsity, calculated); less fan power than air cooling | Racks of up to about 132 kW | Higher capital cost per MW; much more computing per square metre | Plumbing in the white space, leak risk, new skills | Pumps and quick-disconnect fittings add failure points |
| **Immersion cooling** | Removes almost all fan power; not quantified in the sources used | Niche, mostly pilots and crypto-mining | Specialised hardware and fluids | Maintenance is harder; some fluids are fluorinated chemicals under regulatory pressure | Limited long-term data |
| **Colocation**^[2](https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf?version=0) | Close to industry average PUE | From a few MW to hundreds of MW | Customers pay per kW of power reserved | Operator controls the building, tenants control the IT, so efficiency incentives are split | Sold on uptime guarantees |

Efficiency gains in the building (PUE falling from 2.5 in 2007 to about 1.5) have largely been taken; the average has hardly moved since 2018, though the best hyperscalers reach 1.1. The bigger lever is now computing per watt inside the server and rack, and liquid cooling allows racks around 15 times denser than the average (calculated). The constraint has moved from efficiency to the sheer size of grid connections, with demand projected to more than double by 2030.

#### How each method works
**Enterprise or legacy air-cooled data centre**

Computer room air handlers blow chilled air into a raised floor or aisles; chillers make the cold water. Often small and run by the company itself.
**Hyperscale cloud campus with free cooling**

Very large, standardised halls use outside air or evaporative cooling for most of the year, high-efficiency power chains and custom servers, with software moving workloads between sites.
**AI data centre with direct-to-chip liquid cooling**

Cold plates on GPUs and CPUs carry warm water or coolant to coolant distribution units; the remaining heat in the air is removed conventionally.
**Immersion cooling**

Servers sit in tanks of dielectric (electrically insulating) fluid that carries heat away by circulation or by boiling and condensing.
**Colocation**

A developer builds and runs the facility and rents space, power and cooling to many customers who install their own servers.

#### Sources

1. [Energy and AI (World Energy Outlook special report)](https://www.iea.org/reports/energy-and-ai) IEA, 2025
2. [Uptime Institute Global Data Center Survey 2025](https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf?version=0) Uptime Institute, 2025
3. [Data centers: efficiency (power usage effectiveness)](https://datacenters.google/efficiency) Google, 2026
4. [NVIDIA GB200 NVL72 (specifications)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) Nvidia, 2025
5. [Vertiv and Nvidia collaborate on 7MW reference architecture for GB200 NVL72](https://www.datacenterdynamics.com/en/news/vertiv-and-nvidia-collab-on-7mw-reference-architecture-for-gb200-nvl72) DatacenterDynamics, 2024

## Other sections

- [Summary](https://www.materialinsights.org/energy/e_data_centres/index.md)
- [Overview](https://www.materialinsights.org/energy/e_data_centres/overview/index.md)
- [Correlation](https://www.materialinsights.org/energy/e_data_centres/correlation/index.md)
- [Science and limits](https://www.materialinsights.org/energy/e_data_centres/science/index.md)
- [Value chain](https://www.materialinsights.org/energy/e_data_centres/valuechain/index.md)
- [Unit economics](https://www.materialinsights.org/energy/e_data_centres/economics/index.md)
- [Equipment and suppliers](https://www.materialinsights.org/energy/e_data_centres/equipment/index.md)
- [Relationships](https://www.materialinsights.org/energy/e_data_centres/links/index.md)