# EnviroLLM: Resource Tracking and Optimization for Local AI

arXiv: https://arxiv.org/abs/2512.12004 · HTML: https://mlsanity.com/paper/2512.12004/

## 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.

## Builds on (most cited first, 11 of 11)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses](https://mlsanity.com/paper/2504.11972/) (2025)
- [Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements](https://mlsanity.com/paper/2509.22092/) (2025)
- [Carbon Footprint Evaluation of Code Generation through LLM as a Service](https://mlsanity.com/paper/2504.01036/) (2025)
- [Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights](https://mlsanity.com/paper/2504.06307/) (2025)
- [Sustainability via LLM Right-sizing](https://mlsanity.com/paper/2504.13217/) (2025)
- [Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks](https://mlsanity.com/paper/2506.20009/) (2025)
- [OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models](https://mlsanity.com/paper/2405.12843/) (2024)
- [The Hidden Costs of Translation Accuracy: Distillation, Quantization, and Environmental Impact](https://mlsanity.com/paper/2509.23990/) (2025)

## 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.