Benchmarking the Energy Savings with Speculative Decoding Strategies
It tackles the energy efficiency problem for LLM practitioners, but is incremental as it focuses on benchmarking rather than proposing new methods.
This paper addresses the lack of attention to energy requirements in speculative decoding for LLMs by conducting a comprehensive survey analyzing how model size, strategies, and dataset characteristics influence energy optimizations.
Speculative decoding has emerged as an effective method to reduce latency and inference cost of LLM inferences. However, there has been inadequate attention towards the energy requirements of these models. To address this gap, this paper presents a comprehensive survey of energy requirements of speculative decoding strategies, with detailed analysis on how various factors -- model size and family, speculative decoding strategies, and dataset characteristics -- influence the energy optimizations.