Session

DescriptionArtificial Intelligence (AI) has become a primary driver of compute demand and energy consumption, with workloads such as large language models and AI-powered computational science applications requiring sustained, infrastructure-scale resources. At the same time, modern data centers operate under increasingly tight power, carbon, and power grid constraints, particularly as they integrate variable renewable energy sources. Together, these trends highlight the growing tension between performance scalability, energy availability, and environmental impact. Addressing these challenges requires integrated efforts from component-level optimization to co-designing computing infrastructures, workloads, and energy systems. This minisymposium examines how sustainability can be embedded by design across AI and data-intensive computing infrastructures. Topics include (1) datacenter-power grid co-design to enable load flexibility under renewable integration, (2) infrastructure-level optimization of AI and AI-powered workloads for improved energy efficiency, (3) hardware-software co-design approaches that explore accelerator design trade-offs for AI and scientific computing, and (4) methods, metrics, and lifecycle assessment tools. The session also highlights the economic and environmental tensions between cost-optimal and emissions-optimal system configurations. By bringing together researchers and practitioners working on architectures, systems, and infrastructure design, this session aims to foster a shared understanding of the design principles needed to support sustainable growth in AI and data-intensive computing.