This paper explores the energy efficiency of large language model (LLM) inference systems, emphasizing the need to prioritize energy efficiency in the deployment of these models. As LLMs become more prevalent, their inference demands are increasing, leading to significant energy consumption in data centers. The paper presents a detailed analysis of the trade-offs between energy efficiency and performance in LLM inference, highlighting the various factors that influence energy consumption, such as workload type, batching, and model parallelism. The study characterizes the energy consumption of LLM inference environments under different settings, showing that these environments pose unique challenges not addressed by traditional power management schemes. The paper identifies key factors that affect energy efficiency, including the variability of workloads, the impact of different input and output lengths, and the effects of varying degrees of parallelism and batch sizes. It also explores the impact of GPU frequency scaling on energy consumption and performance, demonstrating that reducing GPU frequency can significantly lower power consumption without compromising performance. The paper also discusses the importance of optimizing energy efficiency in LLM inference systems, noting that these systems are a significant portion of data center loads and contribute substantially to global energy consumption and carbon footprint. The study provides insights into how energy-efficient LLM inference can be achieved through various strategies, including adaptive resource allocation, minimizing configuration overhead, and optimizing for both performance and energy efficiency. The paper concludes that there are platform decisions that can be made to improve energy efficiency without affecting cost or performance. These insights pave the way for more sustainable and energy-efficient LLM deployment in data center environments. The study also highlights the need for further research into energy-efficient LLM inference systems, emphasizing the importance of developing comprehensive frameworks for managing energy in these systems.This paper explores the energy efficiency of large language model (LLM) inference systems, emphasizing the need to prioritize energy efficiency in the deployment of these models. As LLMs become more prevalent, their inference demands are increasing, leading to significant energy consumption in data centers. The paper presents a detailed analysis of the trade-offs between energy efficiency and performance in LLM inference, highlighting the various factors that influence energy consumption, such as workload type, batching, and model parallelism. The study characterizes the energy consumption of LLM inference environments under different settings, showing that these environments pose unique challenges not addressed by traditional power management schemes. The paper identifies key factors that affect energy efficiency, including the variability of workloads, the impact of different input and output lengths, and the effects of varying degrees of parallelism and batch sizes. It also explores the impact of GPU frequency scaling on energy consumption and performance, demonstrating that reducing GPU frequency can significantly lower power consumption without compromising performance. The paper also discusses the importance of optimizing energy efficiency in LLM inference systems, noting that these systems are a significant portion of data center loads and contribute substantially to global energy consumption and carbon footprint. The study provides insights into how energy-efficient LLM inference can be achieved through various strategies, including adaptive resource allocation, minimizing configuration overhead, and optimizing for both performance and energy efficiency. The paper concludes that there are platform decisions that can be made to improve energy efficiency without affecting cost or performance. These insights pave the way for more sustainable and energy-efficient LLM deployment in data center environments. The study also highlights the need for further research into energy-efficient LLM inference systems, emphasizing the importance of developing comprehensive frameworks for managing energy in these systems.