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

• URL: http://arxiv.org/abs/2412.04782v2
• Date: Sat, 18 Jan 2025 15:46:42 GMT
• Title: A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
• Authors: Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei,
• Abstract summary: Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning.<n>Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability.<n>This survey paper examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers.
• Score: 0.7889270818022226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights [2.1249213103048414]
  The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions. This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these concerns.
  arXiv  Detail & Related papers  (2025-04-07T21:56:59Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment. We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• A Survey on Post-training of Large Language Models [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration. These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance. This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms.
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Systems-of-Systems for Environmental Sustainability: A Systematic Mapping Study [0.7095350526841507]
  This study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation. We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities. Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored.
  arXiv  Detail & Related papers  (2025-02-27T12:00:27Z)
• Addressing the sustainable AI trilemma: a case study on LLM agents and RAG [7.6212949300713015]
  Large language models (LLMs) have demonstrated significant capabilities, but their widespread deployment and more advanced applications raise critical sustainability challenges. We propose the concept of the Sustainable AI Trilemma, highlighting the tensions between AI capability, digital equity, and environmental sustainability.
  arXiv  Detail & Related papers  (2025-01-14T17:21:16Z)
• Enhancing Green Economy with Artificial Intelligence: Role of Energy Use and FDI in the United States [0.0]
  This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. The findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.
  arXiv  Detail & Related papers  (2024-12-20T03:03:21Z)
• Architectural Tactics to Improve the Environmental Sustainability of Microservices: A Rapid Review [0.0]
  This rapid review gathers 22 peer-reviewed studies and synthesizes architectural tactics that improve the environmental sustainability of systems. We list 6 tactics that are presented in an actionable way and categorized according to their sustainability aspects and context.
  arXiv  Detail & Related papers  (2024-07-19T22:44:58Z)
• The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
  Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
  arXiv  Detail & Related papers  (2024-06-24T15:55:49Z)
• Recommendations for public action towards sustainable generative AI systems [0.0]
  This paper presents the components of the environmental footprint of generative AI. It highlights the massive CO2 emissions and water consumption associated with training large language models. The paper also explores the factors and characteristics of models that have an influence on their environmental footprint.
  arXiv  Detail & Related papers  (2024-01-04T08:55:53Z)
• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models [33.50873478562128]
  Large Language Models (LLMs) bring forth challenges in the high consumption of computational, memory, energy, and financial resources. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs.
  arXiv  Detail & Related papers  (2024-01-01T01:12:42Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis [0.0]
  We offer a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, and AI-based evaluation of renewable technologies.
  arXiv  Detail & Related papers  (2021-10-02T15:51:51Z)
• Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning [77.34726150561087]
  Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
  arXiv  Detail & Related papers  (2020-06-22T16:17:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.