An AI server is not one heat source.

A typical system may contain GPUs or AI accelerators, CPUs, voltage regulators, memory, storage, networking components, power conversion hardware, and other devices—all generating different amounts of heat in different locations.

That is why the question should not be:

“Should an AI server use liquid cooling or heat sinks?”

A better question is:

“Which components need direct liquid cooling, and which can still be cooled efficiently by air?”

The most practical AI server cooling solutions often combine GPU water blocks or liquid cold plates for high-heat-flux processors with heat sinks and controlled airflow for lower-power supporting components.

Jindu Tech currently develops GPU water blocks for AI and high-performance computing, custom liquid cold plates, and several air-cooled heat sink structures, allowing these technologies to be evaluated as parts of one thermal architecture rather than isolated products. Its GPU water block page specifically positions these products for AI and deep-learning computing environments.

Map the AI Server by Heat Zone Before Choosing Cooling Hardware

A server thermal map can be divided into several functional zones.

Zone A: GPUs and AI Accelerators

These are usually the most concentrated heat sources.

They may have:

• High local heat flux
• Tight temperature limits
• Limited vertical cooling space
• Multiple devices located close together

This zone is the strongest candidate for direct-to-chip liquid cooling.

Zone B: CPUs

High-performance CPUs can also justify liquid cooling, especially when the system is already using a liquid loop for GPUs.

In less thermally demanding configurations, a CPU heat sink may remain practical.

Zone C: VRM and Power Delivery

Voltage regulators and related power components can generate significant local heat but may not always require direct liquid contact.

Depending on power density and layout, they may use:

• Extended GPU water block contact
• Small heat sinks
• Thermal spreaders
• Directed airflow

Zone D: Memory and Storage

Memory modules, SSDs, and similar components generally have different heat-flux characteristics from GPUs.

Direct liquid cooling may be unnecessary unless system density or operating conditions specifically justify it.

Controlled airflow and compact heat sinks can remain valuable.

Zone E: Chassis and Rack Air Path

Even a liquid-cooled AI server may still require airflow.

Fans can continue cooling:

• Memory
• Storage
• Network controllers
• Motherboard components
• Power delivery
• Connectors
• Other secondary heat sources

Liquid-cooled processors do not automatically create an air-free server.

This is one of the most important principles when designing a hybrid AI server thermal system.

Component-to-Cooler Matching Matrix

Server Component	Cooling Option Worth Evaluating	Primary Engineering Concern
High-power GPU / accelerator	GPU water block or custom cold plate	Local heat flux and contact
CPU	Cold plate or heat sink	Power density and available height
GPU memory / VRM	Full-cover water block or local air cooling	Contact height and board layout
Power delivery components	Heat sink or shared cold plate where justified	Distributed hotspot control
DIMM memory	Directed airflow / local heat spreader	Air temperature and spacing
SSD	Small heat sink / airflow	Local temperature and airflow
Network controller	Heat sink	Moderate localized heat
Power supply electronics	Heat sink or separate liquid solution	Load and packaging
Server chassis	Fan and ducting	Air distribution
Multi-GPU manifold	Liquid distribution system	Branch balance and pressure drop

The table highlights an important design principle:

Cooling technology should follow heat density and physical location, not simply the component category.

Two server designs using similar GPUs may require different cooling structures because their board layout, rack density, coolant infrastructure, and airflow are different.

GPU Zone: When a GPU Water Block Makes More Sense Than a Generic Cold Plate

The term “GPU water block” often describes a liquid cooling assembly developed specifically around the layout of a GPU board.

A full-cover structure may be designed to contact:

• GPU processor
• VRAM
• VRM or nearby power components

Jindu Tech’s GPU Water Block Series includes full-cover and GPU-core-focused structures, with the full-cover concept intended to address several thermal zones on the graphics or accelerator board.

This is different from beginning with a generic rectangular cold plate and simply placing it over the GPU.

A GPU water block is attractive when:

• Board geometry is already defined
• Several GPU-board components require cooling
• Contact heights can be mapped accurately
• Compact internal channels are required around the GPU
• A board-specific mounting structure is acceptable

The design becomes closely tied to the PCB.

If the accelerator board changes significantly, the water block may also require revision.

Contact design matters as much as coolant design

A GPU water block must maintain appropriate contact with different thermal zones.

Possible engineering issues include:

• GPU package height
• Memory height
• Thermal pad thickness
• Mounting pressure
• Cold plate flatness
• PCB component clearance

A highly effective internal channel will not compensate for poor thermal contact above it.

Cold Plate Zone: When the Cooling Structure Is Defined by the Server, Not the GPU Board

A liquid cold plate can serve a broader system role.

Instead of following the complete GPU PCB, the cold plate may be designed around:

• One processor package
• Several accelerators
• Multiple power devices
• A larger baseplate
• An OEM computing module

This becomes useful when the cooling component is part of the mechanical architecture of the server rather than an accessory attached to a standardized GPU board.

Jindu Tech’s liquid cold plate solutions currently include deep-drilled, brazed, extruded, friction-stir-welded, and tube-based structures, with data centers and high-density AI processors listed among their application areas.

Cold plate design begins with the heat map

Internal coolant channels can be positioned according to:

• Heat-source locations
• Hotspot intensity
• Required surface temperature uniformity
• Flow direction
• Port position
• Pressure-drop limit

A simple cold plate may contain relatively direct flow paths.

A more demanding GPU or accelerator plate may require:

• Parallel channels
• Serpentine paths
• Internal fins
• Local high-surface-area regions
• Multiple flow zones

The geometry should earn its place through thermal performance.

Complex channels that do not meaningfully reduce processor temperature may only add pressure drop and manufacturing difficulty.

Heat Sink Zone: Why AI Servers Still Need Air Cooling

AI server cooling discussions often focus almost entirely on GPUs and CPUs.

That can obscure the rest of the system.

A motherboard contains many components that may not justify liquid cooling individually.

Air cooling remains useful because one controlled airflow path can remove heat from many distributed components simultaneously.

Examples include:

• DIMMs
• SSDs
• VRMs
• Chipsets
• NICs
• Auxiliary controllers
• Power components

A server heat sink can be designed around:

• Extruded fins
• Skived fins
• Zipper fins
• Bonded fins
• Heat pipes
• Other forced-air structures

Jindu Tech’s server heat sink solutions include high-density fin structures intended for forced-air applications such as servers and data centers. (jindutech.com)

Removing GPU heat from the air changes the airflow requirement

This is one major benefit of direct liquid cooling.

If GPUs previously released a large portion of their heat into the server air stream, fans had to move that heat through the chassis.

Once GPU heat moves into a liquid loop, the internal air path can focus more heavily on secondary components.

That does not mean fan design becomes unimportant.

It means the airflow budget changes.

Engineers can potentially reconsider:

• Fan speed
• Fin density
• Air ducting
• Component placement
• Temperature rise through the chassis

Three Practical AI Server Cooling Architectures

There is no single “AI server cooling architecture.”

Most systems can be understood as variations of three patterns.

Architecture 1: Air-Dominant Server

Cooling structure:

GPU / CPU → Heat Sink → Server Airflow

Secondary components → Airflow

This architecture makes sense when:

• Processor heat density remains manageable
• Enough fin volume is available
• Server fans can provide sufficient static pressure
• Acoustic and power requirements permit strong airflow
• Liquid infrastructure is not available

Advantages include system simplicity and avoiding coolant connections inside the server.

The primary constraint is packaging.

As processor heat density increases, maintaining acceptable temperature can demand larger or denser heat sinks and stronger airflow.

Architecture 2: Hybrid Direct-to-Chip Cooling

Cooling structure:

GPU / CPU → Water Block or Cold Plate → Coolant

Memory / SSD / VRM / Other Components → Heat Sink + Airflow

This is often the most intuitive architecture for high-density AI equipment because cooling technologies are assigned according to heat concentration.

Hybrid cooling removes the largest concentrated heat loads through liquid while preserving air cooling for distributed lower-power devices.

The engineering challenge shifts toward coordinating two thermal systems:

• Hydraulic network
• Server airflow network

Neither should be designed in isolation.

Architecture 3: Liquid-Dominant Server

More components are connected to a liquid cooling architecture.

This can reduce the amount of heat released into chassis air but increases:

• Cold plate count
• Fluid connections
• Manifold complexity
• Hydraulic balancing requirements
• Leakage-control requirements
• Service complexity

Liquid-dominant cooling becomes more attractive as equipment density and local heat flux increase, but every additional liquid-cooled component should justify the added system complexity.

Multi-GPU Servers Turn Cold Plates Into a Hydraulic Network

One GPU water block can be optimized as an individual component.

Eight GPU water blocks connected through one manifold create a different engineering problem.

The server now contains a hydraulic network.

Consider a parallel arrangement:

Supply manifold
→ GPU 1
→ GPU 2
→ GPU 3
→ GPU 4
→ …
→ Return manifold

Each branch may experience a different hydraulic condition.

What can change branch flow?

• Cold plate channel resistance
• Port dimensions
• Tube length
• Connector resistance
• Manifold geometry
• Manufacturing variation

If one branch has much lower resistance, it may receive more coolant than another.

A good average server flow rate does not prove that every GPU is adequately cooled.

Multi-GPU liquid cooling should be evaluated by per-branch flow and GPU-to-GPU temperature variation, not only by total coolant flow.

This makes cold plate pressure drop a system-design parameter.

A cold plate with extremely high resistance may perform strongly by itself but become difficult to integrate into a large parallel network.

The Coolant Has Its Own Temperature Map

Air warms as it moves through a heat sink.

Coolant also warms as it absorbs heat.

This becomes particularly relevant when several cold plates are connected in series.

Imagine:

Coolant inlet
→ GPU A
→ GPU B
→ GPU C
→ GPU D

GPU D receives warmer coolant than GPU A.

If devices have similar heat loads and temperature limits, the architecture may create uneven thermal margins.

Engineers may respond through:

• Parallel branches
• Split manifolds
• Different flow distribution
• Modified channel resistance
• Different component ordering

The appropriate solution depends on the complete cooling loop.

This is why an AI server cold plate should not be developed without knowing where it sits in the hydraulic system.

Pressure Drop Is the Hidden Cost of Aggressive GPU Cooling

Dense microchannels and internal fins can increase wetted surface area and local heat transfer.

They also restrict coolant.

The pump must overcome resistance from:

• GPU blocks
• CPU cold plates
• Manifolds
• Tubing
• Fittings
• Quick connectors
• Heat exchanger

A GPU water block therefore consumes part of the total system pressure budget.

This creates a three-way balance:

GPU Temperature ↔ Coolant Flow ↔ Pressure Drop

If the channel is too restrictive, the cooling loop may require more pump capacity.

If the channel becomes too open, local coolant velocity may become less favorable for the thermal design.

Jindu Tech’s cold plate page similarly treats heat transfer, flow-path distribution, and hydraulic pressure drop as linked design variables rather than independent specifications.

Hybrid Cooling Blind Spot #1: VRMs and Memory Do Not Disappear From the Heat Map

Moving the GPU core to liquid cooling can reduce a major heat source.

But surrounding components still generate heat.

If the previous air-cooled GPU heat sink also moved air across:

• VRM
• Memory
• Nearby PCB areas

replacing it with a compact liquid block can change local airflow.

That change needs to be reviewed.

Possible responses include:

• Full-cover GPU water block
• Separate VRM contact
• Small local heat sinks
• Directed chassis airflow
• Thermal spreaders

Do not evaluate only GPU core temperature and assume the entire board is now thermally solved.

Hybrid Cooling Blind Spot #2: Lower Fan Speed Can Create New Hotspots

A successful GPU liquid loop may encourage engineers to reduce server fan speed.

That can improve acoustic or electrical performance.

However, lower airflow also affects components that never moved to liquid cooling.

Monitor:

• Memory temperature
• SSD temperature
• VRM temperature
• Network controller temperature
• Power-stage temperature

The correct fan reduction is the one that preserves acceptable temperatures across the complete motherboard.

This is why server-level validation should follow component-level cold plate testing.

Hybrid Cooling Blind Spot #3: Mechanical Integration Can Limit a Good Thermal Design

AI servers are mechanically dense.

Cooling hardware competes for space with:

• GPU boards
• Busbars
• Memory
• Connectors
• Tubing
• Quick disconnects
• Manifolds
• Chassis structures

A technically effective water block may still be unsuitable if:

• Port orientation interferes with another board
• Tubing bend space is insufficient
• Cold plate height blocks service access
• Connector loads stress the PCB
• Mounting screws are inaccessible

Thermal and mechanical design should therefore proceed together.

Server-Level Validation Sheet

A GPU water block prototype should not be released for production because one GPU passed a bench test.

Validation should move through several levels.

Validation Level	What to Check
Contact interface	GPU/contact temperatures, flatness, mounting
Individual water block	Thermal performance, flow, pressure drop, leakage
GPU board	GPU, memory and VRM temperatures
Multi-GPU manifold	Per-branch flow and GPU-to-GPU temperature
Server chassis	Secondary component temperatures and airflow
Cooling loop	Pump operating point and total resistance
Production sample	Dimensional, hydraulic and leak consistency

The final pass condition belongs to the server, not only to the cold plate.

A cooling component can perform correctly while the overall architecture still contains an airflow or hydraulic distribution problem.

Manufacturing Strategy Changes With the Cooling Zone

Different server thermal zones may also require different manufacturing approaches.

GPU Water Blocks

A GPU-specific block may require:

• Precision-machined contact surfaces
• Compact internal channels
• Board-specific clearances
• Sealing structure
• Surface treatment
• Custom mounting

Jindu Tech’s GPU Water Block Series currently uses CNC-machined liquid-cooling structures and offers full-cover and GPU-core-focused configurations.

Liquid Cold Plates

Depending on architecture, manufacturing routes may include:

• Deep drilling
• Vacuum brazing
• Extrusion
• Friction stir welding
• Embedded tube structures

Process selection should follow internal geometry, pressure conditions, production requirements, and material.

Heat Sinks

Depending on fin density and airflow, options can include:

• Extrusion
• Skiving
• Zipper fins
• Bonded fins
• Heat pipe assemblies
• CNC-machined structures

The server does not need to use one manufacturing technology everywhere.

What an AI Server Cooling RFQ Should Look Like

An inquiry such as:

“We need cooling for eight high-power GPUs.”

is not enough for a useful engineering proposal.

Prepare a system thermal package.

Processor Map

• GPU / accelerator quantity
• CPU quantity
• Heat dissipation for each major processor
• Package dimensions
• Locations

Board Thermal Map

• VRM heat sources
• Memory arrangement
• Other high-temperature components
• Components requiring direct contact

Mechanical Architecture

• 2D drawings
• 3D server model
• Maximum cooling height
• Mounting locations
• Port keep-out zones
• Manifold position
• Tube routing space

Liquid-Cooling Conditions

• Coolant type
• Coolant inlet temperature
• Target total flow
• Per-device flow if known
• Pressure-drop budget
• Operating pressure
• Pump / CDU information
• Series or parallel architecture

Air-Cooling Conditions

• Fan model or performance data
• Airflow direction
• Server inlet temperature
• Existing ducts
• Secondary components relying on airflow

Project Requirements

• Prototype quantity
• Expected production volume
• Validation requirements
• Inspection requirements

For AI server cooling, the most useful RFQ describes the complete processor, airflow, and coolant architecture rather than specifying only the GPU power rating.

These inputs allow the supplier to determine which thermal zones should use water blocks, which need general cold plates, and which can remain on air cooling.

Engineering drawings and application conditions can be submitted through Jindu Tech for project evaluation.

Final Architecture: Cool Each Zone According to Its Thermal Density

AI server cooling is increasingly a system-partitioning problem.

GPUs and accelerators can justify direct liquid cooling because they concentrate substantial heat into a small contact area.

CPUs may join the same liquid loop when their thermal and packaging conditions justify it.

Memory, storage, network controllers, and many power components can continue to use air cooling when their heat density remains manageable.

This produces a layered architecture:

High heat flux → GPU Water Block / Liquid Cold Plate
Moderate local heat → Heat Sink
Distributed board heat → Controlled Airflow

The design becomes stronger when these layers are coordinated rather than optimized independently.

The most effective AI server cooling architecture is not necessarily the one with the most liquid-cooled components; it is the one that assigns cooling capacity where the server actually generates thermal risk.

That means evaluating:

• Processor contact
• Internal liquid channels
• Multi-GPU flow balance
• Pressure drop
• Secondary component airflow
• Mechanical integration
• Server-level validation

as one thermal system.

FAQ

What cooling solution is commonly used for AI servers?

AI servers can use forced-air heat sinks, direct-to-chip liquid cold plates, GPU water blocks, or hybrid systems combining air and liquid cooling. The appropriate architecture depends on processor heat density, server size, airflow, coolant infrastructure, rack density, and the thermal requirements of secondary components.

Is a GPU water block the same as a GPU cold plate?

The terms overlap, but a GPU water block is commonly designed specifically around a GPU or accelerator board and may cool the GPU, memory, and VRM. A GPU cold plate is a broader engineering term and may be designed around one processor or a larger OEM cooling structure.

Do liquid-cooled AI servers still need heat sinks?

Often, yes. Even when GPUs and CPUs use liquid cooling, components such as memory, SSDs, network controllers, VRMs, and other electronics may still rely on heat sinks and chassis airflow. Liquid cooling the highest-power processors does not automatically remove the server’s air-cooling requirement.

Why is pressure drop important in multi-GPU liquid cooling?

Every GPU water block, cold plate, fitting, manifold, and tube contributes hydraulic resistance. High pressure drop can reduce available flow or increase pump demand. In multi-GPU systems, resistance also influences how evenly coolant is distributed between parallel branches.

Should multiple GPU water blocks be connected in series or parallel?

Both configurations are possible. Parallel connections can provide GPUs with coolant closer to the same inlet temperature but require careful flow balancing. Series connections simplify the flow path but expose downstream GPUs to warmer coolant. The correct architecture depends on flow, pressure, manifold, and temperature requirements.

Can heat sinks still cool AI GPUs?

Heat sinks can remain practical when heat density, available fin volume, airflow, and fan pressure allow the GPU to stay within its required temperature range. As GPU heat flux or server density increases, however, achieving the target with air may require increasingly large or restrictive cooling structures.

What information is needed for a custom AI server cooling solution?

Provide GPU and CPU quantity, heat loads, board layout, cooling contact zones, mechanical space, fan conditions, coolant type, inlet temperature, target flow, pressure-drop budget, operating pressure, manifold architecture, prototype quantity, and expected production requirements.