Work

Cache Your Prompt When It's Green — Carbon-Aware Caching for Large Language Model Serving

Year: 2026

Type: article

Abstract: As large language models (LLMs) become widely used, their environmental impact — especially carbon emission — has attracted more attention. Prior studies focus on compute-related carbon emissions. In this paper, we find that storage is another key contributor. LLM caching, which saves and reuses KV caches for repeated context, reduces operational carbon by avoiding redundant computation. However, this benefit comes at the cost of embodied carbon from high-capacity, high-speed SSDs. As LLMs scale, the embodied carbon of storage grows significantly. To address this tradeoff, we present GreenCache, a carbon-aware cache management framework that dynamically derives resource allocation plans for LLM serving. GreenCache analyzes the correlation between carbon emission and SLO satisfaction, reconfiguring the resource over time to keep the balance between SLO and carbon emission under dynamic workloads. Evaluations from real traces demonstrate that GreenCache achieves an average carbon reduction of 15.1% when serving Llama-3 70B in the FR grid, with reductions reaching up to 25.3%, while staying within latency constraints for > 90% of requests.

Source: Proceedings of the ACM on Measurement and Analysis of Computing Systems

DOI: 10.1145/3788087

Authors: Yuyang Tian, Desen Sun, Yi Ding, Sihang Liu

Institutions: University of Waterloo, Purdue University West Lafayette

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Responsible consumption and production

Open Access status: hybrid

Funder: National Science Foundation