# Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service

arXiv: https://arxiv.org/abs/2304.09781 · HTML: https://mlsanity.com/paper/2304.09781/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2023.

## Built on by (most cited first, 17 of 17)

- [The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective](https://mlsanity.com/paper/2506.04301/) (2025)
- [Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems](https://mlsanity.com/paper/2407.04014/) (2024)
- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference](https://mlsanity.com/paper/2403.12900/) (2024)
- [Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers](https://mlsanity.com/paper/2503.04148/) (2025)
- [CarbonCall: Sustainability-Aware Function Calling for Large Language Models on Edge Devices](https://mlsanity.com/paper/2504.20348/) (2025)
- [Advancing AI Challenges for the United States Department of the Air Force](https://mlsanity.com/paper/2511.00267/) (2025)
- [CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing](https://mlsanity.com/paper/2603.27420/) (2026)
- [GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization](https://mlsanity.com/paper/2605.11603/) (2026)
- [The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures](https://mlsanity.com/paper/2605.11999/) (2026)
- [Energy-Aware Routing to Large Reasoning Models](https://mlsanity.com/paper/2601.00823/) (2025)
- [Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing](https://mlsanity.com/paper/2602.23935/) (2026)
- [EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving](https://mlsanity.com/paper/2605.10556/) (2026)
- [Greening AI Inference with Accuracy and Latency-aware User Incentives](https://mlsanity.com/paper/2605.27309/) (2026)
- [AgentDecarbonizer: Carbon-Aware Execution for AI Agents](https://mlsanity.com/paper/2608.20566/) (2026)
- [Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions](https://mlsanity.com/paper/2609.35569/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets.