The demand for AI, especially large language models (LLMs), is exploding, but current computer systems are struggling to keep up. Training these massive models requires immense computational power and energy, pushing the limits of existing hardware and contributing to a growing carbon footprint. But what if there was a way to dramatically boost performance while slashing energy consumption? Researchers at IMEC are exploring superconducting digital (SCD) electronics as a potential game-changer. These chips leverage the unique properties of superconductors to operate at incredibly high frequencies with a fraction of the power needed by traditional chips. This research paper presents a system-level evaluation of SCD architecture for LLM training and inference. They've developed a specialized modeling approach to project how SCD systems would perform compared to today's GPUs. The results are promising, showing substantial performance gains in both training and inference, thanks to the superior memory and interconnect capabilities of superconductors. Imagine an AI system 4x faster than today's top GPUs while using significantly less energy. While challenges remain, this research suggests that superconducting chips could be the key to unlocking the next generation of AI. Future research will delve into scaling these systems beyond a single blade and exploring innovative memory management strategies enabled by the unique properties of superconducting memory. This could revolutionize how we build and deploy AI, making it more sustainable and accessible.