On-Device LLMs for SMEs: Challenges and Opportunities
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Abstract (arXiv)
This paper presents a systematic review of the infrastructure requirements for deploying Large Language Models (LLMs) on-device within the context of small and medium-sized enterprises (SMEs), focusing on both hardware and software perspectives. From the hardware viewpoint, we discuss the utilization of processing units like GPUs and TPUs, efficient memory and storage solutions, and strategies for effective deployment, addressing the challenges of limited computational resources typical in SME settings. From the software perspective, we explore framework compatibility, operating system optimization, and the use of specialized libraries tailored for resource-constrained environments. The review is structured to first identify the unique challenges faced by SMEs in deploying LLMs on-device, followed by an exploration of the opportunities that both hardware innovations and software adaptations offer to overcome these obstacles. Such a structured review provides practical insights, contributing significantly to the community by enhancing the technological resilience of SMEs in integrating LLMs.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 21 Oct 2024.
Builds on
14 of 29 references.
References · 14
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2023SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compressioncited by 359
- 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
- 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 153
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2024AIOS: LLM Agent Operating Systemcited by 94
- 2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 84
- 2024Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Modelscited by 64
- 2024LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Reportcited by 47
- 2024Accurate LoRA-Finetuning Quantization of LLMs via Information Retentioncited by 40
- 2024Retrieval Augmented End-to-End Spoken Dialog Modelscited by 20
- 2024Optimizing LLM Queries in Relational Data Analytics Workloadscited by 20
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