This blog post explores innovations in power devices, gate drivers and advanced controllers with Digital Signal Processing (DSP) capabilities to meet Artifical Intelligence (AI) servers' power and efficiency needs. The rise of artificial intelligence (AI) has significantly increased computing. As AI proliferates, the extreme power demanded by processors such as NVIDIA's Grace Hopper H100 super-chip require a two- or three-fold increase - from around 30 to 40 kW per cabinet in current servers to 100 kW or more. There are physical considerations too. The space afforded to power. Frank Long is a vice president at the Goldman Sachs Global Institute, where he focuses on AI. Packing processors closer together creates significant performance and cost improvements for both training and inference workloads. But delivering this power efficiently and reliably, without degrading signal integrity or introducing thermal. A new KAIST roadmap reveals HBM8-powered GPUs could consume more than 15kW per module by 2035, pushing current infrastructure, cooling systems, and power grids to breaking point. In collaboration with NVIDIA, Infineon will develop the next generation of power systems based on a new architecture with centralized power generation through 800V high-voltage direct current.