EnviroLLM: Resource Tracking and Optimization for Local AI
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Abstract (arXiv)
Large language models (LLMs) are increasingly deployed locally for privacy and accessibility, yet users lack tools to measure their resource usage, environmental impact, and efficiency metrics. This paper presents EnviroLLM, an open-source toolkit for tracking, benchmarking, and optimizing performance and energy consumption when running LLMs on personal devices. The system provides real-time process monitoring, benchmarking across multiple platforms (Ollama, LM Studio, vLLM, and OpenAI-compatible APIs), persistent storage with visualizations for longitudinal analysis, and personalized model and optimization recommendations. The system includes LLM-as-judge evaluations alongside energy and speed metrics, enabling users to assess quality-efficiency tradeoffs when testing models with custom prompts.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 12 Dec 2025.
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11 of 13 references.
References · 11
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
- 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 72
- 2025Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analysescited by 14
- 2025Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurementscited by 8
- 2025Carbon Footprint Evaluation of Code Generation through LLM as a Servicecited by 4
- 2025Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insightscited by 3
- 2025Sustainability via LLM Right-sizingcited by 3
- 2025Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Taskscited by 2
- 2024OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Modelscited by 1
- 2025The Hidden Costs of Translation Accuracy: Distillation, Quantization, and Environmental Impactcited by 1
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