As AI accelerators push data center power and thermal densities higher, cooling is becoming an increasingly important constraint on compute performance. Thermal materials are now part of that infrastructure equation. NovoLINC has launched MaxLINC, a thermal interface material (TIM) designed for high-heat-flux AI processors and advanced liquid-cooling systems.
NovoLINC says MaxLINC can deliver thermal resistance as low as 0.7 mm²·K/W while supporting heat flux above 100 W/cm². The company also claims the material can reduce cooling energy consumption for AI servers by more than 20% compared with conventional phase-change TIM and thermal-grease products.
Those claims address a growing problem in AI infrastructure. Modern GPUs, CPUs and application-specific accelerators are packing substantially more computing capability into increasingly dense systems, making the transfer of heat from silicon into cooling hardware a critical part of server design.
Gartner estimates that global data center electricity consumption will reach 565 TWh in 2026, up 26% from 2025. It also expects AI-optimized servers to account for 31% of data center power consumption this year, with their electricity consumption surpassing that of conventional servers in 2027.
Cooling is becoming part of the response. Gartner says data centers supporting AI and other high-density workloads should be designed for liquid cooling because extreme rack power densities make liquid cooling increasingly important for scalable growth.
MaxLINC is designed to sit between semiconductor devices and cooling hardware across several configurations, including direct chip-to-cold-plate interfaces and chip-to-package heat spreaders. NovoLINC says the material can accommodate device and package warpage of up to 350 micrometers, an important consideration as larger packages and increasingly complex accelerator assemblies create mechanical as well as thermal challenges.
The company is positioning the product for the transition from traditional air cooling toward direct liquid cooling and other advanced thermal architectures. NVIDIA, for example, is moving its latest AI infrastructure toward fully liquid-cooled systems, while Google Cloud and other infrastructure providers are developing increasingly integrated liquid-cooling architectures for AI clusters.
The TIM layer may appear small compared with GPUs, cold plates and coolant distribution systems, but its thermal performance can affect the efficiency of the entire cooling chain. Lower interface resistance can help transfer heat more effectively from the processor into the cooling system, potentially providing additional thermal headroom without simply increasing facility-level cooling capacity.
NovoLINC says MaxLINC samples are now available for customer qualification. The company is also expanding its Pittsburgh-area manufacturing footprint with a new facility in Sharpsburg, Pennsylvania, intended to support production scale-up, qualification, testing and research.
For AI infrastructure operators, the practical question will be whether new TIM technologies can deliver their claimed thermal and energy improvements under sustained production workloads. Qualification will also need to account for reliability, mechanical stress, compatibility with cooling architectures and manufacturing requirements.
As AI systems move toward higher-power accelerators and denser rack designs, thermal interface materials are becoming an increasingly strategic component of the AI hardware stack—not merely a materials-engineering detail.
Market Landscape
AI infrastructure is pushing thermal management from a server-level engineering concern toward a data center design priority. Gartner forecasts global data center power demand will reach 132 GW in 2026 and says power availability is becoming a constraint on AI scaling.
The cooling ecosystem now includes direct-to-chip liquid cooling, cold plates, coolant distribution units, two-phase cooling, thermal interface materials and facility-level heat rejection systems. NVIDIA’s 2026 technical material, for example, describes liquid-cooled AI systems designed to operate at substantially higher power densities, while other vendors are developing two-phase and waterless approaches.
This makes thermal efficiency a competitive consideration alongside GPU performance and networking. For enterprises and hyperscalers, adopting a new TIM will ultimately depend on independently validated thermal resistance, reliability over sustained workloads, manufacturing consistency and compatibility with the target accelerator and cooling architecture.
Top Insights
- NovoLINC’s MaxLINC targets high-heat-flux AI accelerators as increasing chip power makes thermal interface performance a constraint on data center efficiency.
- The company reports thermal resistance as low as 0.7 mm²·K/W and support for heat flux exceeding 100 W/cm² in AI applications.
- MaxLINC is designed for direct chip-to-cold-plate and other thermal interfaces while accommodating device and package warpage.
- AI infrastructure is accelerating adoption of liquid cooling as GPU and accelerator power densities rise beyond the practical limits of conventional air cooling.
- NovoLINC is expanding its Pittsburgh-area manufacturing capacity as MaxLINC enters customer qualification and potential production deployments.
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