MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees

Abstract

Open-weight large language model zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing accessibility, selecting the most appropriate model for a given task still largely relies on public benchmark leaderboards and educated guesses. This can be unsatisfactory for both inference service providers and end users. The providers prioritize cost efficiency, while the end users prioritize model output quality for their inference requests. In commercial settings, these two priorities are often brought together in Service Level Agreements (SLA). We present MESS+, an online stochastic optimization algorithm for energy optimal model selection in a model zoo that works on a per-inference-request basis. For a given SLA that requires high accuracy, we are up to 2.5× more energy efficient with MESS+ than with randomly selecting an LLM from the zoo while maintaining SLA quality constraints.