GemaTEG™ Introduces DaTEG 1.0: A Revolutionary Thermal Management Solution for AI Servers

 Accelerating AI Infrastructure

GemaTEG™ introduces DaTEG 1.0, a thermal management solution that significantly reduces energy costs and enhances chip performance in AI infrastructures.

Why It Matters

  • As the AI industry’s appetite for computing power grows, so does the strain on data center infrastructure.
  • Traditional cooling systems are energy-intensive and often lack efficiency.
  • GemaTEG’s DaTEG 1.0 helps top chip designers in the HPC industry, hyper-scalers, data center providers, and systems integrators transcend AI industry limitations by enhancing capacity without increasing resource demands.

What’s new

  • DaTEG 1.0 is an integrated and modular Thermal Management solution that takes far less server space than traditional, less efficient cooling technologies.
  • The breakthrough lies in its ability to maintain peak operational conditions for GPUs and CPUs, thereby extending their longevity and enhancing their output.

Deep Background

  • Developed by astroparticle physicist Maurizio Miozza (Co-Founder and CTO) from decades of experience in silicon properties, DaTEG 1.0 results from over five years of research in materials science and fluid dynamics.
  • The technology incorporates advances from aerospace to nuclear science, providing an integrated cooling solution.

By the Numbers

  • DaTEG 1.0 supports chips consuming up to 500 watts, with future enhancements to accommodate chip designs of up to 1000 watts.
  • Testing shows potential performance reaches advertised GHz levels without the risk of overheating—critical for maintaining uptime for AI in data centers.

Expert Voice

  • From Maurizio Miozza: “With DaTEG, we’re not just preventing chips from overheating; we’re optimizing their operational conditions to push the boundaries of what’s possible in AI computing.”

What’s Next

  • GemaTEG is collaborating with global academic and industry leaders to refine and expand the capabilities of DaTEG, ensuring it meets the future needs of AI infrastructure.
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