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How Singapore’s Data Centres Can Support AI Growth More Efficiently
Singapore’s data centres can make room for more AI and cloud computing by improving how existing power and cooling are used—not by assuming that one new cooling technology fits every site. Digital Realty’s Jon Curry described the challenge as cooling efficiently as computing demand rises; Singapore’s tropical climat...
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Key takeaways
- Singapore’s data centres can make room for more AI and cloud computing by improving how existing power and cooling are used—not by assuming that one new cooling technology fits every site.
- Digital Realty’s Jon Curry described the challenge as cooling efficiently as computing demand rises; Singapore’s tropical climate makes that harder.
- A practical sequence is to measure facility conditions, reduce avoidable cooling waste, select upgrades for the site and workload, and test changes before wider deployment.
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openai.comCreate a landscape editorial hero image for this Studio Global article: How can Singapore’s data centre operators support growing artificial intelligence, cloud and high-performance computing workloads without un. Article summary: Singapore’s operators should prioritise more computing output from existing power and space, rather than simply adding cooling and electrical capacity; reporting already describes a shift towards retrofits, efficiency an. Topic tags: general, general web, user generated. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fa
Singapore’s data centre operators face a balancing act: supporting growing AI, cloud and high-performance computing workloads while working within limits on land, power and cooling. The clearest opportunity is to get more useful computing capacity from existing facilities through operational improvements and targeted upgrades—not to assume that adding cooling equipment alone will solve the problem. 17
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The challenge is efficient cooling, not just more cooling
Singapore’s tropical climate and limited land constrain unrestricted data centre growth. 17 In a Singapore Business Review interview, Digital Realty Asia-Pacific operations vice president Jon Curry described the issue as keeping facilities cool efficiently as computing demand rises. Humidity and limited natural cooling add to the challenge.
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Power constraints are also influencing where operators invest. Singapore Business Review reports a growing focus on retrofits, higher-density infrastructure, airflow management, cooling and energy monitoring as operators seek to get more capacity from existing sites. 33
That strategy matters because efficiency gains do not, by themselves, prove that total electricity use will decline. Singapore is also seeking to expand data centre capacity to meet rising computing needs. 1 The useful measure is therefore not just whether cooling becomes more efficient, but whether facilities can support the required workloads while avoiding unnecessary energy use.
A practical sequence for improving efficiency
The following is an operational framework, not a set of verified quotations from Curry or other interviewees.
1. Measure conditions across the facility
Before changing cooling controls, operators can map temperature, humidity, airflow and computing demand across the site. Reliable measurements help identify where cooling is needed and where it may be excessive. If a facility has gaps in sensor coverage or environmental data, those gaps should be addressed before operators rely on automated recommendations.
2. Improve existing operations first
Review cooling settings, airflow and energy monitoring before committing to major equipment changes. These are among the areas Singapore Business Review identifies as priorities as operators improve existing facilities. 33 The best opportunities will depend on each site’s condition and operating requirements.
3. Match cooling technology to the workload
Liquid cooling is receiving attention as AI workloads increase. Singapore introduced a standard intended to support the adoption of liquid cooling in tropical data centres. 2 That does not establish liquid cooling as the right choice for every facility. Operators still need to consider the equipment, rack density, retrofit constraints and reliability needs of the specific site.
4. Test changes before scaling them
Cooling adjustments and automated controls should be checked under representative operating conditions before wider deployment. Singapore’s Sustainable Tropical Data Centre Testbed work is intended to examine cooling systems and energy-management practices suited to tropical conditions. 37
What the available evidence does—and does not—show
The sources support the broad case for improving existing facilities, managing airflow and evaluating cooling options for tropical conditions. 2
33 They do not substantiate detailed claims about sensor gaps in older sites, a specific talent model for digital twins, or cooling trials in Singapore and Melbourne that made real-time adjustments during workload spikes. Nor do they provide a measured estimate of electricity freed for other uses.
Those possibilities should be treated as questions for operators to test and document, not as proven results. The practical goal is clear: make cooling decisions using facility-level evidence, choose changes that suit the workload, and verify their performance before expanding them.
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People also ask
What is the short answer to "How Singapore’s Data Centres Can Support AI Growth More Efficiently"?
Singapore’s data centres can make room for more AI and cloud computing by improving how existing power and cooling are used—not by assuming that one new cooling technology fits every site.
What are the key points to validate first?
Singapore’s data centres can make room for more AI and cloud computing by improving how existing power and cooling are used—not by assuming that one new cooling technology fits every site. Digital Realty’s Jon Curry described the challenge as cooling efficiently as computing demand rises; Singapore’s tropical climate makes that harder.
What should I do next in practice?
A practical sequence is to measure facility conditions, reduce avoidable cooling waste, select upgrades for the site and workload, and test changes before wider deployment.
Sources
lkyspp.nus.edu.sglkyspp.nus.edu.sg › docs › casesSilicon vs. Sustainability - Singapore's AI Dilemma
sbr.com.sgSingapore data centre constraints push investment into efficiency
edb.gov.sgSingapore to expand data centre capacity by at least one-third ...