mlsanityData to 7 Oct 2026

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

arXiv 2304.09781 ↗First posted 19 Apr 2023cs.DCcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 8.

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  1. 2025The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspectivecited by 38
  2. 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systemscited by 27
  3. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  4. 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
  5. 2025Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Serverscited by 2
  6. 2025CarbonCall: Sustainability-Aware Function Calling for Large Language Models on Edge Devicescited by 1
  7. 2025Advancing AI Challenges for the United States Department of the Air Forcecited by 1
  8. 2026CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computingcited by 1
  9. 2026The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architecturescited by 1
  10. 2025Energy-Aware Routing to Large Reasoning Modelscited by 0
  11. 2026Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computingcited by 0
  12. 2026EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Servingcited by 0
4 more
  1. 2026GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimizationcited by 0
  2. 2026Greening AI Inference with Accuracy and Latency-aware User Incentivescited by 0
  3. 2026AgentDecarbonizer: Carbon-Aware Execution for AI Agentscited by 0
  4. 2026Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisionscited by 0