Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Serving
This work addresses the tradeoff between operational and embodied carbon in LLM caching for cloud providers aiming to reduce environmental impact.
GreenCache, a carbon-aware cache management framework for LLM serving, reduces operational carbon by dynamically reconfiguring resource allocation to balance SLO satisfaction and carbon emissions, achieving an average carbon reduction of 15.1% (up to 25.3%) for Llama-3 70B while meeting latency constraints for >90% of requests.
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.