Thermal Performance. Engineered for AI.

Thermal performance, engineered for AI

Advanced materials for efficient heat transfer in high-density compute environments. Designed for measurable gains in performance and reliability.

Lowest chip-to-cooler resistance

Vertically aligned carbon nanotubes and liquid metal interfaces minimize thermal barriers, enabling up to 10°C lower junction temperatures.

Sustained GPU and AI accelerator output

Reduces thermal throttling, supporting higher sustained clock speeds and maximizing hardware utilization in demanding workloads.

Optimized for next-gen architectures

Engineered for high-power chips and dense packaging, ensuring consistent performance as compute demands scale.

Proven efficiency, measurable ROI

Improves data center energy efficiency and increases revenue per GPU by enabling higher throughput and reduced cooling costs.

Proven results. Measurable impact.

10°C

Typical chip temp reduction

99.9%

Thermal interface reliability

30%

Efficiency gain per rack

Performance Data & Visualizations

Interactive metrics demonstrating the Aonyxx thermal advantage.

Temperature Delta

vs. Baseline Material
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Methodology: 600W TDP simulated load over 24 hours. Lower is better.

Thermal Resistance

K·mm²/W across pressure ranges
Methodology: ASTM D5470 standard test method at varying mounting pressures.

Sustained Performance

Clock speed stability over time
Methodology: Continuous LLM training workload. Higher sustained clock speed is better. RTX 5090 GPU @ 600 watts.

Projected GPU/Hr Revenue Uplift

GPU Cluster
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Methodology: Flow Modeling based on 600 watt RTX GPU

Infrastructure Economics

Cooling energy savings per rack
Methodology: Projected savings based on reduced fan speeds and chiller load at 80kW rack density.

Projected 5MW Data Center Revenue Uplift

GPU Cluster
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Methodology: Flow Modeling based on 600 watt RTX GPU

Frequently asked questions

Clear answers on performance and integration

Get in touch
How do these materials improve cooling?

Our thermal interface materials use vertically aligned carbon nanotubes and engineered liquid metal to reduce chip-level thermal resistance by up to 10°C compared to conventional TIMs.

What systems are these compatible with?

Our solutions are designed for AI accelerators, GPUs, and high-density compute hardware. Customization for specific architectures is available.

How do I evaluate or pilot the technology?

Contact our team to discuss pilot programs, technical requirements, and integration support for your hardware environment.

Engineered for extreme performance

Reduce heat. Unlock higher efficiency. Increase ROI.

Contact us to discuss pilot projects or technical evaluations.

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