SLIT is a machine-learning-guided evolutionary scheduler that, in simulation, reduces carbon, water, energy cost, and time-to-first-token relative to two existing LLM serving systems on a synthetic workload derived from a real trace.
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Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
SLIT is a machine-learning-guided evolutionary scheduler that, in simulation, reduces carbon, water, energy cost, and time-to-first-token relative to two existing LLM serving systems on a synthetic workload derived from a real trace.