How will AI influence RAN energy efficiency?
While AI-driven applications are increasing traffic and energy demand, AI and automation are becoming critical enablers of greater energy efficiency. This report explores how CSPs can use energy-efficient AI inference, telco-grade AI, and intelligent automation to improve RAN energy performance while maintaining user experience. Discover real-world results from Swisscom and learn how AI-driven energy optimization is helping build the foundation for future AI-native 6G networks.
Key insights
What you will discover in this report
AI is creating new traffic patterns, increasing demand for uplink capacity and introducing new inference workloads. While this can raise energy demand, AI can also help networks operate more efficiently through automation, prediction and intelligent resource management.
AI-native RAN is expected to drive trillions of inference operations every day. At this scale, efficient inference becomes critical. It helps CSPs reduce OPEX by minimizing the energy cost of AI operations and lower CAPEX by enabling AI workloads to run on existing platforms.
Energy-efficient telco-grade AI RAN needs to combine the right model in the right place with the right compute across the network. This approach enables CSPs to continuously improve both network performance and energy efficiency, rather than treating them as competing objectives.
Yes. AI-powered optimization and intent-driven automation can dynamically adapt network resources to demand, helping CSPs reduce energy consumption while maintaining performance and user experience targets.
By applying AI and automation in today’s networks, CSPs can scale efficiently and prepare for AI-native, energy-efficient 6G networks, accelerating sustainable network growth.
