Solving the "thermal anxiety" of AI data center: Three high thermal conductivity materials of Ziitek are the cooling stability of CPU/GPU

Solving the "thermal anxiety" of AI data center: Three high thermal conductivity materials of Ziitek are the cooling stability of CPU/GPU

When AI big model and VR/AR are running wildly, the CPU and GPU in the data center are experiencing "high temperature baking test"-they are both the core of computing power and the number one heat source. Once the heat dissipation can't keep up, not only will the stability of the equipment be discounted, but the energy consumption and operation and maintenance costs will also rise. The key to solve this heat dissipation dilemma lies in a detail that is easily overlooked: the thermal interface material. So, where does the "heat anxiety" of the data center come from?

1. High thermal conductivity interface material: detailed explanation of the performance of three "cooling tools"

Heat-conducting silica gel sheet: a "flexible heat-conducting sheet" suitable for complex scenes. Core performance: The thermal conductivity is usually 1.0~13W/(m? K), with excellent flexibility and insulation, the product has a fire protection grade of UL94-V0, with self-adhesive property without additional adhesive, which can be customized according to the gap thickness of equipment; Application scenario: the very strict joint area between CPU/GPU radiator and motherboard and the gap filling of storage device module-it can adapt components with different heights at the same time to avoid equipment damage caused by hard contact; Advantages of AI scene: Facing the dense component layout in AI server, it can flexibly fill irregular gaps, taking into account heat conduction efficiency and equipment protection.

2. Thermal conductive phase change material: an "intelligent thermal conductive layer" adapted with temperature.

Core performance: it is solid at room temperature (easy to transport and install), and it will change into semi-liquid when the temperature reaches 50~60℃, which closely adheres to the surface of chip and heat sink; Application scenario: the core heat dissipation surface of high-performance CPU/GPU-it can fill nano-scale micro-cracks after phase change, greatly reducing the interface thermal resistance; Advantages of AI scene: During the training of AI large model, the chip will be in a high-load and high-temperature state for a long time, and the phase change material can keep close contact continuously, avoiding the thermal conductivity fault of traditional solid materials due to thermal expansion and cold contraction.

3. Thermal conductive gel: the core performance of "heat flow accelerator" that breaks through the upper limit of thermal conductivity.

The thermal conductivity directly exceeds 8.0W/(m? K) It has the fluidity of heat-conducting gel, and its temperature resistance ranges from-45℃ to 200℃. Even if the local temperature changes sharply due to the fluctuation of refrigeration system or long-term high load of equipment in the data center, it can maintain stable heat-conducting performance. Moreover, the products have passed UL94 V-0 flame retardant certification and RoHS environmental protection standards, which can not only ensure heat dissipation, but also avoid potential safety hazards at high temperature, and meet the needs of long-term stable operation of data centers. Applicable scenarios: AI accelerator cards, high-power GPU and other "high-heat-density core devices"; Advantages of AI scene: In the cluster of large computing power, the power consumption of a single chip continues to rise, and the gel with high thermal conductivity can quickly export the concentrated heat to avoid the frequency reduction of computing power caused by local overheating of the chip.

The combined use of these materials is equivalent to installing a "layered effective cooling channel" for the core equipment of the data center-it can not only accurately match the cooling requirements of different devices, but also reduce the energy consumption of fans and air conditioners, and indirectly reduce the operating cost of the data center.