Global Liquid Cooling Industry Trends | 2026

As AI computing continues to scale, the thermal challenges of high-density data centers are becoming increasingly difficult to solve with traditional air cooling.

The rapid increase in GPU power consumption and rack-level power density is pushing data center operators toward a new generation of thermal management technologies. Liquid cooling, once primarily used for high-performance computing and specialized servers, is now becoming an essential part of modern AI data center infrastructure.

One of the most important developments is the move toward higher-temperature liquid cooling.

NVIDIA and other major AI infrastructure players are increasingly exploring liquid cooling architectures that can operate with coolant temperatures approaching 45°C under appropriate system conditions. Higher supply temperatures can reduce the need for mechanical refrigeration, improve cooling efficiency, reduce water consumption in some applications, and support higher-density AI racks.

At the same time, the industry is moving beyond chip-level cooling toward server-level, rack-level, and pod-level liquid cooling.

This shift is creating new opportunities across the liquid cooling supply chain, including cold plates, Coolant Distribution Units (CDUs), quick disconnects (QDs), rack manifolds, liquid cooling systems, and liquid-cooled AI servers.

1. 45°C Liquid Cooling Is Changing the AI Data Center Cooling Model

Traditional data centers generally rely on chilled-water systems, CRAH units, CRAC systems, and other mechanical cooling equipment to maintain the required room temperature.

However, the cooling requirements of modern AI servers are fundamentally different.

High-end GPUs can generate significantly more heat than conventional enterprise processors. As GPU power increases, simply moving more air through the server becomes increasingly inefficient and difficult.

Direct-to-chip liquid cooling provides a more effective way to remove heat by circulating coolant directly through cold plates attached to high-power processors.

The next step is to operate these liquid cooling systems at higher coolant temperatures.

A higher supply-water temperature can create opportunities for greater use of free cooling, dry coolers, cooling towers, and other low-energy heat rejection technologies, depending on the local climate and system architecture.

The significance of 45°C-class liquid cooling is therefore not simply that the coolant is warmer.

The more important change is that the entire data center cooling architecture can move away from dependence on low-temperature mechanical refrigeration.

Potential benefits include:

• Lower mechanical cooling requirements
• Reduced cooling-system auxiliary power
• Improved data center PUE
• Lower water consumption in suitable configurations
• Greater cooling capacity for high-density GPU racks
• Better support for high-power AI infrastructure

However, 45°C coolant does not mean that every component inside an AI server simply operates at 45°C.

The actual thermal design still depends on the interaction between the cold plate, coolant distribution unit, manifold, quick disconnects, pumps, heat exchanger, control system, and facility cooling loop.

This makes high-temperature liquid cooling a system-level engineering challenge rather than simply a change in coolant temperature.

2. NVIDIA's Liquid Cooling Roadmap: From GPU Cooling to Rack-Level Infrastructure

Liquid cooling is undergoing a significant architectural transition.

Early liquid cooling solutions primarily focused on removing heat from individual GPUs and CPUs through Direct-to-Chip Cooling.

That approach remains important, but it becomes insufficient as AI rack power continues to rise.

Once rack power reaches 100 kW or more, thermal management must consider the entire rack rather than individual processors.

The architecture increasingly becomes:GPU → Server → Rack Manifold → CDU → Facility Cooling System

This is an important industry shift.

Liquid cooling is no longer simply a thermal component installed inside an AI server. It is becoming part of the core infrastructure of the AI rack and data center.

For liquid cooling manufacturers, this creates new technical requirements.

A competitive solution must address:

• GPU cold plate performance
• Coolant flow distribution
• CDU capacity
• Rack manifold design
• Pressure drop
• Quick-disconnect reliability
• Leak detection
• Temperature control
• System redundancy
• Remote monitoring

As AI clusters scale from individual servers to dozens, hundreds, or thousands of racks, system reliability and deployment capability become just as important as individual component performance.

3. Cold Plate Technology Is Moving Toward Micro-Structured Cooling

The cold plate remains one of the most important components in a Direct-to-Chip liquid cooling system.

Traditional cold plates commonly use copper or other high-thermal-conductivity materials with machined, brazed, or bonded internal channels.

These designs are mature and widely used, but the continued increase in GPU heat flux is creating new technical challenges.

Key challenges include:

• Higher localized heat flux
• More concentrated GPU hotspots
• Limited coolant flow rates
• Increasing pressure-drop requirements
• Pump power constraints
• Tight mechanical packaging requirements

As a result, the next generation of cold plates is likely to focus less on simply increasing coolant flow and more on improving heat transfer efficiency per unit of flow.

Technologies such as micro-structured channels and jet-based cooling are attracting attention because they can direct coolant more precisely toward high-heat-flux regions.

The industry is therefore moving toward three major directions.

1. From conventional channels to micro-structured channels

Parallel and serpentine channels remain commercially important, but microchannels, jet impingement structures, and localized heat-transfer enhancement may become increasingly relevant for next-generation GPUs.

2. From uniform cooling to hotspot-focused cooling

GPU heat generation is not necessarily uniform across the entire die.

Future cold plates may therefore be designed around the actual thermal map of the processor, providing more cooling capacity exactly where the heat density is highest.

3. From experience-based design to AI-assisted optimization

Cold plate development is increasingly supported by:

CFD simulation + AI algorithms + digital twins + automated optimization

This approach can evaluate large numbers of channel geometries and identify designs that balance thermal performance, pressure drop, flow rate, manufacturability, and cost.

4. CDU Is Becoming the Thermal Management Hub of the AI Rack

If the cold plate removes heat directly from the processor, the Coolant Distribution Unit (CDU) manages the circulation and heat exchange of the larger liquid cooling system.

A CDU can perform several critical functions, including:

• Coolant circulation
• Temperature regulation
• Flow control
• Pressure management
• Heat exchange
• Primary and secondary loop isolation
• System monitoring
• Fault detection and alarms

As AI rack power increases from tens of kilowatts toward 100 kW and beyond, CDU capacity and reliability become increasingly important.

In a rack-level liquid cooling architecture, the CDU is no longer simply a heat exchanger.

It increasingly functions as the thermal management control center for the AI rack.

Future CDU development is likely to focus on:

• Higher cooling capacity
• Modular architecture
• Improved heat exchanger efficiency
• Intelligent control
• Redundant pump and control configurations
• Remote monitoring
• Predictive maintenance

For MW-scale AI data centers, however, another question becomes critical:

Can the CDU architecture scale reliably?

A liquid cooling provider must be able to support not only one rack, but potentially tens, hundreds, or thousands of racks.

That is why the market is gradually moving from product competition to system-level delivery competition.

5. Quick Disconnects and Rack Manifolds Become Critical at Scale

As liquid cooling deployments become larger, the importance of Quick Disconnects (QDs) is increasing.

An AI rack may contain numerous coolant connections, supply lines, return lines, and service points.

A failure at any connection can affect server availability and maintenance operations.

Therefore, QDs need to provide:

• High reliability
• Low pressure drop
• Long connection/disconnection life
• Reliable sealing performance
• Compatibility with elevated coolant temperatures
• Compatibility with different coolant systems
• Easy maintenance

Rack manifolds are equally important.

A manifold distributes coolant to multiple servers while collecting return coolant from the rack.

As rack power increases, manifold design must address:

flow balancing, pressure drop, piping layout, serviceability, redundancy, and overall system reliability.

This makes QDs and manifolds important components of the broader AI rack liquid cooling architecture.

6. AI Infrastructure Is Becoming a Power-and-Thermal Management Challenge

The AI infrastructure market is entering a new stage.

In the early phase of the AI boom, the primary question was:

Are there enough GPUs?

Today, the bottlenecks are expanding to include:

• Power availability
• Grid interconnection
• Data center land
• Electrical infrastructure
• Cooling capacity
• Water availability
• Construction timelines

As a result, competition in AI infrastructure is expanding from computing hardware to a broader combination of:

Power + Computing + Cooling + Data Center Infrastructure

NVIDIA's continued investment in the broader AI infrastructure ecosystem reflects this shift.

AI computing is no longer simply a server industry problem.

It increasingly involves semiconductor companies, cloud providers, data center developers, utilities, power infrastructure companies, liquid cooling manufacturers, and other technology suppliers.

For liquid cooling companies, this creates a major strategic opportunity.

Customers may increasingly expect more than a standalone cooling component. They may need an integrated thermal management solution covering the server, rack, CDU, manifold, piping, monitoring, and facility interface.

7. Liquid Cooling Is Becoming Standard for High-Density AI Server Platforms

Server manufacturers are also accelerating their liquid cooling strategies as AI server power density increases.

Platforms based on NVIDIA HGX systems, NVL72 architectures, and Direct-to-Chip liquid cooling are helping drive the transition from air-cooled servers toward liquid-cooled AI infrastructure.

The underlying relationship is straightforward:

Higher GPU power → Higher server power → Higher rack power → Greater air-cooling limitations → Higher liquid cooling adoption

When rack power reaches 100 kW or more, conventional air cooling requires significantly higher airflow and greater fan and cooling-system energy consumption.

This is why AI server architecture is increasingly evolving from:

Server-centric to: Rack-centric and eventually:Pod-centric

For liquid cooling manufacturers, this means the market opportunity is no longer limited to cold plates.

The ability to design and deliver a complete rack-level thermal management system is becoming increasingly important.

8. AI Is Also Changing the Way Cold Plates Are Designed

Interestingly, AI is not only driving demand for liquid cooling.

It is also beginning to change how liquid cooling hardware is developed.

Traditional cold plate design often relies on engineering experience, CFD analysis, prototype testing, and iterative optimization.

With generative algorithms and topology optimization, engineers can define a set of design requirements, such as:

Target temperature

Maximum hotspot temperature

Coolant flow rate

Maximum pressure drop

Material constraints

Manufacturing constraints

Algorithms can then generate and evaluate multiple channel configurations.

The development process can increasingly evolve from:

Engineer Design → CFD Simulation → Prototype → Testing

toward:

AI Generation → CFD Simulation → Digital Twin → Automated Optimization → Physical Validation

This approach is particularly valuable for next-generation GPUs, where thermal performance is increasingly constrained by extremely high local heat flux.

9. AI Server Growth Is Accelerating the Liquid Cooling Market

The continued expansion of AI computing is one of the strongest drivers of liquid cooling adoption.

NVIDIA GPU platforms, hyperscaler-developed AI accelerators, custom ASICs, and large-scale cloud infrastructure are all contributing to higher computing density.

The increase in computing density ultimately translates into higher rack power.

The underlying industry relationship can be summarized as:

AI Computing Growth → More Accelerators → Higher Chip Power → Higher Server Power → Higher Rack Power → Greater Liquid Cooling Adoption

This is one of the fundamental reasons why the AI data center liquid cooling market is expected to remain a high-growth segment.

The opportunity is not simply driven by the fact that liquid cooling is more efficient than air cooling.

It is driven by the structural change in the power density of AI infrastructure.

10. Key Liquid Cooling Opportunities in the 2026 AI Data Center Supply Chain

Based on current technology development and infrastructure requirements, the AI liquid cooling supply chain can broadly be divided into three major opportunity groups.

Tier 1: Cold Plates and CDUs

Cold plates and CDUs are among the most strategically important components in AI liquid cooling systems.

The cold plate determines how efficiently heat can be removed from high-power processors, while the CDU manages coolant circulation, heat exchange, temperature, and flow throughout the system.

As AI rack power continues to increase, both components are likely to see growing technical requirements and higher system value.

Key areas include:

• GPU cold plates
• CPU cold plates
• Direct-to-Chip liquid cooling
• Rack CDUs
• In-rack CDUs
• In-row CDUs
• High-capacity CDUs

Tier 2: Quick Disconnects and Manifolds

As liquid cooling moves from individual servers to large-scale rack deployments, connection reliability becomes increasingly important.

Although QDs and manifolds may have lower individual system value than GPUs or CDUs, they directly influence:

• System reliability
• Maintenance efficiency
• Coolant distribution
• Pressure drop
• Scalability

As the number of liquid-cooled AI racks increases, these components are likely to become increasingly important.

Tier 3: Liquid Cooling Materials

Long-term liquid cooling reliability depends heavily on material performance.

Key material categories include:

• Coolants
• Thermal interface materials
• Sealing materials
• Tubing and piping materials
• Metals and alloys
• Corrosion-resistant materials
• Gaskets and elastomers

For higher-temperature liquid cooling systems, long-term chemical compatibility, corrosion resistance, thermal stability, and sealing reliability will become increasingly important.

11. AI Liquid Cooling Is Entering the Era of Delivery Capability

One of the most important changes in the liquid cooling industry in 2026 is the shift from technology validation to large-scale deployment.

In the past, the key question was:

“Do you have a liquid cooling solution?”

Increasingly, customers are asking:

“Can you reliably deploy liquid cooling at scale?”

Designing a liquid cooling system for a laboratory or a single server is one thing.

Deploying a stable system for:

100 kW+ racks

dozens or hundreds of racks

MW-scale AI clusters

is a completely different challenge.

The system must ensure that CDUs, cold plates, manifolds, QDs, pumps, controls, and facility cooling infrastructure work together reliably.

Maintenance and fault recovery also become critical.

This means the competitive landscape is shifting toward three major capabilities.

Technical Capability

Can the supplier handle increasingly high chip heat flux and rack power density?

Product Capability

Can the supplier provide cold plates, CDUs, manifolds, QDs, controls, and other key components as an integrated system?

Delivery Capability

Can the supplier deploy and support liquid cooling infrastructure from individual racks to MW-scale and eventually GW-scale AI data centers?

Lianli One-Stop Liquid Cooling Solutions:
Founded in 2014 and headquartered in Longgang, Shenzhen—where its manufacturing base is also located—Lianli is a global provider of comprehensive, one-stop liquid cooling solutions for computing power, specializing in eco-friendly liquid cooling technologies.

The company integrates solution design, data center construction, equipment R&D and manufacturing, global installation, and full-lifecycle operations and maintenance. Its core product portfolio includes containerized data centers, rack-mount and cabinet-style CDUs, and liquid-cooled server cabinets. These products are widely compatible with diverse applications, such as A1 supercomputing, intelligent computing clusters, data industrial parks, and overseas computing infrastructure deployments. Backed by an experienced R&D team and proprietary patented technologies, the company enables PUE levels to drop below 1.15 and achieves an overall energy-saving rate exceeding 30%.

Conclusion: Liquid Cooling Is Becoming Core AI Data Center Infrastructure

The AI liquid cooling industry is entering a new phase in 2026.

From GPU cold plates and Direct-to-Chip Cooling to CDUs, rack manifolds, quick disconnects, and facility-level cooling systems, liquid cooling is developing into a complete infrastructure ecosystem.

At the same time, higher-temperature liquid cooling, rack-level cooling, pod-level architectures, and AI-assisted cold plate design are pushing the industry beyond conventional server thermal management.

The future of AI data centers will not be determined by GPU performance alone.

It will increasingly depend on the combination of:

Computing Density + Power Availability + Cooling Efficiency + Infrastructure Delivery

For the liquid cooling industry, the opportunity is therefore much larger than simply supplying cooling components for AI servers.

As rack power continues to rise, liquid cooling is becoming a fundamental infrastructure technology for the next generation of high-density AI data centers.

Among the most important areas to watch are cold plates, CDUs, quick disconnects, rack manifolds, and integrated liquid cooling solutions—all of which will play an increasingly important role in enabling the next generation of AI infrastructure.