AI Data Centres in SEA: Why Cooling Matters Now

A single AI-ready rack can now pull 20kW+—and that heat has to go somewhere. If you’re a Singapore SME investing in AI business tools, 5G connectivity, or real-time personalisation, this isn’t just “someone else’s data centre problem”. It’s the hidden constraint that decides whether your AI workloads stay fast, stable, and affordable.

Most SMEs think performance is about the model, the laptop, or the cloud plan. The reality? Infrastructure sets the ceiling. As Southeast Asia scales up data centres to meet AI demand, cooling becomes a make-or-break factor for cost, reliability, and sustainability.

This post is part of our “AI Business Tools Singapore” series, and the angle is simple: if you want AI to improve marketing and operations, you need to understand what makes AI infrastructure workable—especially in a hot, humid region like ours.

Liquid cooling is becoming the baseline for AI compute

Answer first: Liquid cooling is moving from “premium option” to “default requirement” because modern AI chips generate more heat than air cooling can efficiently remove at high rack densities.

AI workloads (training, fine-tuning, inference at scale) rely heavily on GPUs. These GPUs are power-hungry and tightly packed. Traditional air cooling worked when racks were far less dense, but many modern deployments push beyond what airflow can handle without massive energy overhead.

When cooling falls behind, data centres face:

• Thermal throttling (your expensive compute runs slower)
• Higher failure rates (heat shortens component lifespan)
• Rising operational expenditure (electricity bills climb, margins shrink)
• Deployment limits (you can’t safely increase rack density)

One detail from the source article matters a lot: “cutting-edge data centre racks can consume over 20,000 watts.” At that level, cooling isn’t a background facility item; it’s a core engineering decision.

What liquid cooling changes in practice

Liquid cooling transfers heat more efficiently than air because liquids have far higher heat capacity and thermal conductivity. In real deployments, that typically means:

• Higher rack density without cooking the room
• More stable temperatures (fewer hotspots)
• Lower cooling energy vs blasting chilled air
• Better performance consistency for AI workloads

The original article also noted that new GPU generations are designed with liquid cooling in mind (a direction the entire market is taking). That’s the clearest signal: cooling isn’t an optional add-on—it’s being baked into the roadmap.

For Singapore SMEs, “cloud AI” still depends on physical cooling

Answer first: Even if you never build a server room, your AI tools’ speed and price are tied to how efficiently regional data centres run.

Singapore SMEs adopting AI often start with:

• AI-powered ad targeting and creative testing
• customer support chatbots
• demand forecasting
• recommendation engines for e-commerce
• fraud detection or risk scoring

All of that compute runs somewhere. If data centres in Southeast Asia face cooling constraints, you’ll see it show up as:

• higher cloud costs (providers pass through energy + capacity constraints)
• capacity scarcity for GPU instances (longer provisioning times)
• less predictable latency for real-time tools

Here’s the stance I take: SMEs that treat infrastructure as “not my problem” often end up paying more—either in direct cloud spend or in lost speed-to-market.

A simple marketing example: real-time personalisation

If you’re running an e-commerce store and want AI to personalise offers as the user browses, you need fast inference.

• If inference is slow, you deliver the “personalised” banner after the customer has already bounced.
• If inference is expensive, you reduce coverage (personalise only for high-value users).

Cooling impacts whether the underlying GPUs can be packed densely and operated efficiently. That efficiency influences what you pay and what performance you get.

Southeast Asia’s “clean-slate” advantage is real—if it’s used

Answer first: Southeast Asia can build newer data centre infrastructure without legacy constraints, which makes it easier to adopt liquid cooling early and scale AI capacity faster.

The source article frames a strong regional point: many Southeast Asian markets are expanding digital infrastructure rapidly and can design for modern workloads from day one.

That matters because retrofitting legacy facilities is hard:

• floor loading limits
• pipe routing challenges
• downtime constraints
• stranded HVAC investments

A “clean slate” approach can prioritise:

• liquid cooling compatibility
• high-density rack layouts
• energy-aware management software
• better sustainability design from the start

For Singapore SMEs selling across ASEAN, this is good news: more regional capacity reduces latency and increases competitive options (not everything has to route to distant regions).

Why 5G makes the problem hotter

5G isn’t just faster phones. It pushes more real-time, always-on services:

• richer video commerce
• low-latency fintech experiences
• IoT sensor streams
• AI at the edge feeding central models

More data in motion means more compute in data centres. More compute means more heat. Cooling is the bottleneck that quietly decides how quickly these services can scale.

Sustainability: cooling is where efficiency gains actually show up

Answer first: Liquid cooling can materially reduce data centre energy use, and that affects both costs and carbon footprint—especially relevant as ESG pressure rises across supply chains.

The source article cites a concrete claim: up to 40% energy savings compared to traditional air-cooling systems (implementation-dependent, but directionally consistent with why the industry is moving).

For SMEs, sustainability can feel abstract until a customer or enterprise buyer asks for proof. More tenders now include sustainability criteria. More MNC procurement teams ask vendors to report emissions. Even if you’re small, your buyers may not be.

If your business relies heavily on AI services, your ability to:

• credibly talk about AI-enabled operations
• keep margins healthy ncan increasingly depend on how efficient your upstream compute is.

“Smart” cooling management is the next layer

The article also highlights AI-driven management systems that optimise cooling based on real-time conditions. In practice, this means:

• dynamic control to match cooling to workload
• predictive maintenance (catch failures early)
• better water/energy optimisation

It’s a nice loop: AI increases heat, and AI can also help manage that heat.

What SMEs should do in 2026: a practical checklist

Answer first: You don’t need to buy liquid cooling, but you should make AI adoption decisions as if capacity and efficiency will be constrained—and choose vendors accordingly.

Here’s what works when I advise SMEs on AI tooling and digital marketing operations.

1) Ask your AI and cloud vendors the right questions

You’re not being “too small” by asking. Serious vendors have answers.

• Where is compute hosted (Singapore / SEA region / elsewhere)?
• Are GPU workloads provisioned on high-density infrastructure?
• What uptime and performance guarantees exist for peak periods?
• How do they manage cost spikes when GPU demand surges?

You’re looking for maturity, not buzzwords.

2) Architect marketing workflows for cost-per-inference, not hype

If you plan to use AI for:

• ad creative generation
• product recommendation
• conversational sales

…measure how much value you get per AI call.

A quick rule:

• If your AI feature doesn’t improve revenue, retention, or service cost within 60–90 days, redesign it.

Cooling-driven cost pressure will make “nice-to-have” AI features the first to be cut.

3) Prefer tools that can degrade gracefully

When infrastructure is constrained, the winning systems are designed to handle it.

Examples:

• fall back from real-time personalisation to segment-based rules
• cache common answers for chatbots
• run batch scoring overnight instead of always-on inference

This keeps customer experience stable even when compute is expensive.

4) Treat sustainability as a sales asset (not a CSR page)

If you sell B2B, add a short section in proposals:

• what AI systems you use
• how you manage data responsibly
• how you reduce waste (including compute waste)

You don’t need perfect numbers. You do need a credible narrative.

A good stance for 2026: “We use AI where it reduces customer friction and operational waste, and we avoid compute-heavy features that don’t pay for themselves.”

Where this fits in the “AI Business Tools Singapore” series

AI tools are only as useful as the systems they run on. That’s the connective tissue between marketing outcomes and infrastructure realities.

As Singapore SMEs push harder on:

• automated lead qualification
• intent-based nurture journeys
• always-on customer service
• multilingual content generation for ASEAN

…we’re going to see a separation between companies that build sustainable AI operations and those that just pile on subscriptions.

Cooling sounds far from marketing, but it hits your P&L through cloud pricing, it hits your customer experience through latency, and it hits your roadmap through capacity constraints.

If you’re planning your 2026 AI stack, don’t just ask “what tool?” Ask “what does this tool assume about compute—and is that compute going to stay affordable?”