AEWIN Showcases AI Infrastructure

The push for more powerful AI is creating a significant heat problem, with modern AI chipsets from companies like NVIDIA and AMD now exceeding 1,000 watts of thermal design power (TDP). Traditional air cooling is becoming insufficient for these high-density computing environments, driving the demand for more advanced thermal management solutions. Two-phase direct liquid cooling (2P DLC) addresses this by submerging hardware in a non-conductive fluid that boils at a low temperature (around 40-50°C). This liquid-to-vapor phase change absorbs massive amounts of thermal energy far more efficiently than air or single-phase liquid cooling, leading to a Power Usage Effectiveness (PUE) as low as 1.05, where cooling accounts for just 5-8% of total data center energy use. AEWIN's specific 2P DLC solution is engineered to remove over 2kW of heat per chip, a capacity needed for next-generation AI GPUs. The system uses a PFAS-free, low-GWP (Global Warming Potential) coolant, addressing environmental sustainability concerns while ensuring high-performance operation. The servers themselves are built on the latest Intel Xeon 6 and AMD EPYC processors. AEWIN's flagship 2U network appliances feature up to eight PCIe 5.0 slots for expansion, supporting network interface cards up to 200G and multiple GPU or FPGA accelerators for data-intensive AI workloads. This hardware is being showcased at the RSA Conference, a top cybersecurity event where AI's role in threat detection and response is a central theme. AEWIN's infrastructure is positioned to power applications like AI-enhanced traffic management and extended detection and response (XDR). The market for such technologies is expanding rapidly. The global data center cooling market was valued at over USD 15 billion in 2023 and is projected to grow at a compound annual growth rate of over 13.5% through 2032 as AI adoption surges. This highlights a critical infrastructure sector for data-heavy industries. For data scientists, this level of hardware is the foundation for running complex models without performance throttling. In sports analytics, it enables large-scale player performance simulations and real-time predictive modeling, while in tech, it powers the massive computational tasks required for developing and deploying generative AI and other advanced algorithms.