A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments
Abstract
In this survey, we delve into the integration and optimization of Large Language Models (LLMs) within edge computing environments, marking a significant shift in the artificial intelligence (AI) landscape. The paper investigates the development and application of LLMs in conjunction with edge computing, highlighting the advantages of localized data processing such as reduced latency, enhanced privacy, and improved efficiency. Key challenges discussed include the deployment of LLMs on resource-limited edge devices, focusing on computational demands, energy efficiency, and model scalability. This comprehensive analysis underscores the transformative potential and future implications of combining LLMs with edge computing, paving the way for advanced AI applications across various sectors.
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GPT-3, with 175 billion parameters, necessitates vast computing resources and is impractical to deploy directly on mobile devices, edge systems, or IoT platforms.
“For example, the popular GPT-3 model [4], with 175 billion parameters, necessitates vast computing resources and is impractical to deploy directly on mobile devices, edge systems, or IoT platforms [16].”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [16]
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- Authors
- Bhardwaj, S., Singh, P., & Pandit, M. K.
- Affiliations
- National Institute Of Technology,Department of Computer Science,Hamirpur,India
- Venue
- 2024 16th International Conference on Computer and Automation Engineering (ICCAE)
- Publisher
- IEEE
- Published
- 2024-03-14
- Pages
- 168-172
- OpenAlex
- W4400189304
- Cited by
- 18
- References
- 49
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 3
- FWCI
- 7.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer and Automation Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, edge computing, resource-constrained edge devices, latency reduction, data privacy, energy efficiency, model scalability, model optimization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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