# A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges

arXiv: https://arxiv.org/abs/2412.04782 · HTML: https://mlsanity.com/paper/2412.04782/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 63 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 30 of 47)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [DreamBooth3D: Subject-Driven Text-to-3D Generation](https://mlsanity.com/paper/2303.13508/) (2023)
- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation](https://mlsanity.com/paper/2409.18964/) (2024)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation](https://mlsanity.com/paper/2406.02509/) (2024)
- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion](https://mlsanity.com/paper/2404.07199/) (2024)
- [Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization](https://mlsanity.com/paper/2404.09956/) (2024)
- [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023)
- [On Learning to Summarize with Large Language Models as References](https://mlsanity.com/paper/2305.14239/) (2023)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models](https://mlsanity.com/paper/2312.16693/) (2023)
- [Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation](https://mlsanity.com/paper/2404.01030/) (2024)
- [PolyLM: An Open Source Polyglot Large Language Model](https://mlsanity.com/paper/2307.06018/) (2023)
- [IT3D: Improved Text-to-3D Generation with Explicit View Synthesis](https://mlsanity.com/paper/2308.11473/) (2023)
- [Sora as a World Model? A Complete Survey on Text-to-Video Generation](https://mlsanity.com/paper/2403.05131/) (2024)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (2023)
- [Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models](https://mlsanity.com/paper/2310.10449/) (2023)
- [EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer](https://mlsanity.com/paper/2409.10819/) (2024)
- [From Sora What We Can See: A Survey of Text-to-Video Generation](https://mlsanity.com/paper/2405.10674/) (2024)

## Built on by (most cited first, 18 of 18)

- [Causal LLM Routing: End-to-End Regret Minimization from Observational Data](https://mlsanity.com/paper/2505.16037/) (2025)
- [ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge](https://mlsanity.com/paper/2506.01646/) (2025)
- [Leveraging the true depth of LLMs](https://mlsanity.com/paper/2502.02790/) (2025)
- [Sustainability via LLM Right-sizing](https://mlsanity.com/paper/2504.13217/) (2025)
- [SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version](https://mlsanity.com/paper/2508.15478/) (2025)
- [Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations](https://mlsanity.com/paper/2503.23934/) (2025)
- [Bi-directional Model Cascading with Proxy Confidence](https://mlsanity.com/paper/2504.19391/) (2025)
- [Learnable quantum spectral filters for hybrid graph neural networks](https://mlsanity.com/paper/2507.05640/) (2025)
- [An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems](https://mlsanity.com/paper/2602.21059/) (2026)
- [Projected Compression: Trainable Projection for Efficient Transformer Compression](https://mlsanity.com/paper/2506.22255/) (2025)
- [ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy](https://mlsanity.com/paper/2507.05279/) (2025)
- [A robust methodology for long-term sustainability evaluation of Machine Learning models](https://mlsanity.com/paper/2511.08120/) (2025)
- [Convex Optimization for Alignment and Preference Learning on a Single GPU](https://mlsanity.com/paper/2605.23244/) (2026)
- [LLM-Guided Evolution for Medical Decision Pipelines](https://mlsanity.com/paper/2606.07342/) (2026)
- [Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing](https://mlsanity.com/paper/2606.22496/) (2026)
- [Engineering Reliable Autonomous Systems: Challenges and Solutions](https://mlsanity.com/paper/2606.23760/) (2026)
- [Samsone: A Family of Open Small Audio Language Models for On-Device Inference](https://mlsanity.com/paper/2609.21666/) (2026)
- [A Statistical Perspective on Knowledge Distillation: Foundations, Classical Methods, and Large Language Model Extensions](https://mlsanity.com/paper/2609.33727/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability. This survey paper comprehensively examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers. By synthesizing insights from existing literature, this work explores strategies such as resource-efficient training, sustainable deployment practices, and lifecycle assessments to mitigate the environmental impacts of LLMs. Key areas of emphasis include energy optimization, renewable energy integration, and balancing performance with sustainability. The findings aim to guide researchers, practitioners, and policymakers in developing actionable strategies for sustainable AI systems, fostering a responsible and environmentally conscious future for artificial intelligence.