Artificial Intelligence is driving one of the fastest increases in computing power the industry has ever experienced.
Today's AI servers no longer consume just a few hundred watts per processor. Modern GPU accelerators routinely exceed 700–1000W, while next-generation AI platforms continue pushing thermal design power (TDP) even higher.
As heat density increases, cooling efficiency becomes one of the key factors determining AI server reliability, performance, and operating costs.
Among all cooling components, the Thermal Interface Material (TIM) is often overlooked—but it directly affects how efficiently heat moves from the processor to the heatsink or liquid cooling system.
This guide explains how engineers choose the right TIM for AI server applications and what factors matter most in real-world GPU cooling.
If you're short on time, here's what you need to know:
✔ AI GPUs are approaching 1000W power consumption.
✔ Thermal conductivity alone does not determine cooling performance.
✔ Low-modulus thermal gels are replacing traditional thermal pads in many AI server designs.
✔ Liquid cooling systems require TIMs with long-term stability under continuous high temperatures.
✔ Proper TIM selection reduces junction temperature, minimizes thermal resistance, and improves system reliability.
As AI models continue to grow, the heat flux of GPUs has reached a level that traditional servers are unable to handle.
Typical heat sources include:
Heat generated by these components must pass through several thermal interfaces before reaching the cooling system.
Even microscopic air gaps can significantly reduce heat transfer efficiency because air has a thermal conductivity of only 0.026 W/m·K.
A properly selected thermal interface material replaces these air pockets and creates an efficient thermal path.
Instead of immediately comparing products, engineers usually evaluate five key parameters.
Thermal conductivity indicates how quickly heat moves through a material.
However...
Higher conductivity doesn't always mean better cooling.
Real cooling performance also depends on
Modern AI GPUs increasingly use
These packages are pressure-sensitive.
A TIM that is too hard may increase package stress.
Low-modulus thermal gel has become one of the preferred solutions because it maintains good contact while minimizing mechanical load.
Different applications require different gap thicknesses.
| Interface Gap | Recommended TIM |
|---|---|
| <0.2 mm | Thermal Grease / PCM |
| 0.2–1 mm | Thermal Pad |
| 0.5–5 mm | Thermal Gel |
| Uneven Surface | Dispensed Thermal Gel |
AI data centers often operate 24/7.
TIMs should resist
Reliability is often more important than achieving the highest laboratory thermal conductivity.
Today's AI server manufacturers are also optimizing production.
Dispensable thermal gels eliminate
while improving automation and repeatability.
| Property | Thermal Pad | Thermal Gel | Thermal Grease | PCM | Liquid Metal |
|---|---|---|---|---|---|
| Thermal Conductivity | ★★★ | ★★★★ | ★★★★ | ★★★ | ★★★★★ |
| Automation | ★★★ | ★★★★★ | ★★ | ★★★ | ★ |
| Gap Filling | ★★★ | ★★★★★ | ★ | ★ | ★ |
| Mechanical Stress | Medium | Very Low | Low | Medium | High |
| Long-term Stability | High | High | Medium | High | Medium |
| Cost Efficiency | Medium | High | Medium | Medium | Low |
Instead of choosing one TIM for the entire server, different components often require different solutions.
| Application | Recommended TIM |
|---|---|
| GPU → Cold Plate | Thermal Gel |
| CPU | Thermal Grease / PCM |
| HBM Memory | Thermal Pad |
| VRM | Thermal Pad |
| Optical Module | Thermal Gel |
| SSD | Thermal Pad |
| Power Supply | Thermal Pad |
Many engineers compare only the conductivity number.
In reality, interface resistance often contributes more to cooling performance than bulk conductivity.
Excessive hardness may damage advanced AI packages.
Different components require different thermal and mechanical characteristics.
AI servers operate continuously for years.
Poor material stability increases thermal resistance over time.
Engineering Tip
When selecting a TIM for AI servers, engineers should evaluate the entire thermal interface—not just the material itself. Contact pressure, surface flatness, gap variation, and long-term reliability often have a greater impact on real-world cooling performance than thermal conductivity alone.
Yes. In many AI server applications, dispensing thermal gel provides better gap adaptation and supports automated manufacturing.
Liquid metal offers exceptional thermal conductivity but requires careful handling because it is electrically conductive and may react with certain metals.
The ideal thickness depends on the actual gap between components. Oversized pads may increase thermal resistance.
Repeated thermal cycling and mechanical stress can gradually force grease away from the contact interface.
Generally no. Different components have different thermal and mechanical requirements.
As AI computing continues to evolve, selecting the appropriate thermal interface material has become an essential part of server design.
The best solution is not necessarily the material with the highest thermal conductivity, but the one that offers the right balance of thermal performance, mechanical compliance, manufacturing efficiency, and long-term reliability.
By understanding application requirements and evaluating TIM technologies comprehensively, engineers can significantly improve cooling efficiency and overall system reliability.
Designing next-generation AI servers or high-performance computing systems?
Our engineering team can help evaluate your application and recommend suitable thermal interface materials based on thermal performance, assembly requirements, and long-term reliability.
Explore our portfolio of:
Contact us to discuss your AI server cooling challenges.
담당자: Ms. Dana Dai
전화 번호: +86 18153789196
Korean
