Summary of On-device Llms For Smes: Challenges and Opportunities, by Jeremy Stephen Gabriel Yee et al.
On-Device LLMs for SMEs: Challenges and Opportunities
by Jeremy Stephen Gabriel Yee, Pai Chet Ng, Zhengkui Wang, Ian McLoughlin, Aik Beng Ng, Simon See
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper conducts a systematic review of infrastructure requirements for deploying Large Language Models (LLMs) on-device within small and medium-sized enterprises (SMEs), focusing on both hardware and software perspectives. The authors discuss GPU, TPU utilization, memory, storage solutions, and deployment strategies to address limited computational resources in SME settings. They also explore framework compatibility, operating system optimization, and specialized libraries for resource-constrained environments from the software perspective. By identifying challenges faced by SMEs and opportunities offered by hardware innovations and software adaptations, the review provides practical insights enhancing the technological resilience of SMEs integrating LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at what companies need to do to use Large Language Models (LLMs) on their own devices, like computers or phones. They’re trying to help small businesses that don’t have as many resources as big companies. The authors think about things like how to make the most of computer processing power, memory, and storage space. They also talk about software that can work well with LLMs in these kinds of settings. By understanding what’s holding these small businesses back and what they need to do to get started, the paper wants to help them use LLMs more effectively. |
Keywords
» Artificial intelligence » Optimization
