AI Infrastructure Forum 2025

From training to inferencing: Scaling AI sustainably

Free Virtual Event | December 2nd 2025 9 AM EST / 2 PM UK

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AI demand is growing at a pace never seen before. Training massive models have already stretched data centers, chipsets, power grids, and supply chains. Inference, while less power-hungry and increasingly distributed to the edge and devices, is not a silver bullet. It introduces new challenges in synchronization, data management, and operational balance between centralized clusters and edge deployments. The urgency is clear: AI must scale sustainably, efficiently, and intelligently.

Through expert presentations, cross-industry panels, and analyst insights, the forum explores the entire lifecycle of AI infrastructure—from energy-intensive training to distributed inference. We’ll examine strategies for upgrading legacy facilities, deploying greenfield builds, orchestrating edge environments, and making smarter choices about where data and workloads belong. We’ll also address the regulatory, political, and societal pressures shaping how AI infrastructure evolves worldwide.

At RCR’s AI Infrastructure Forum 2025, we bring together the ecosystem to define the roadmap for building AI’s backbone and confront this defining challenge: scaling AI sustainably while balancing the demands of training and inference.

Key Themes

◦ Economic and Business Models for AI Infrastructure: Explore how AI infrastructure is creating new revenue streams, reshaping CAPEX/OPEX planning, and enabling services such as Energy-as-a-Service (EaaS) and GPU-as-a-Service (GPUaaS).

◦ Scaling Compute, Networking and Storage: From legacy data centers to greenfield builds, learn strategies for upgrading and designing infrastructure that meets the exponential growth in AI workloads.

◦ Sustainable and Energy-Efficient AI: Address the energy dilemma of AI: power sources, cooling architectures, operational optimization, and regional regulatory disparities, ensuring high performance without compromising sustainability.

◦ Networking, Interconnects, and Data Movement: Delve into high-speed fabrics, optical and chip-to-chip interconnects, multi-site synchronization, and testing strategies to keep AI data flowing efficiently across clouds, edges, and data centers.

◦ Operational Excellence and Orchestration: Understand how orchestration platforms, AI-driven analytics, and workflow standardization transform raw infrastructure into a reliable, repeatable “AI Factory,” while enabling edge and regional deployments.

◦ Inference and the Edge: Explore how inference shifts AI closer to users—into regional data centers, gateways, and devices—reducing latency and power use. Address challenges of synchronization with centralized training, data governance at the edge, and new opportunities for operators and enterprises to deliver AI-as-a-Service.