Summary of Empirical Guidelines For Deploying Llms Onto Resource-constrained Edge Devices, by Ruiyang Qin et al.
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
by Ruiyang Qin, Dancheng Liu, Chenhui Xu, Zheyu Yan, Zhaoxuan Tan, Zhenge Jia, Amir Nassereldine, Jiajie Li, Meng Jiang, Ahmed Abbasi, Jinjun Xiong, Yiyu Shi
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 investigates how large language models (LLMs) should be designed for deployment on resource-constrained edge devices, which will become increasingly important as they are used as personalized intelligent assistants. The scaling laws for designing LLMs were studied under the assumption of unlimited computing resources, but this is no longer realistic. The study explores tradeoffs between various design factors and their impact on learning efficiency and accuracy. Key design factors include learning methods for customization, amount of personalized data, types and sizes of LLMs, compression methods, available time for learning, and difficulty levels of target use cases. Through extensive experimentation and benchmarking, the paper draws guidelines for deploying LLMs onto resource-constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are becoming very important as personalized intelligent assistants. But these models need to be designed in a way that works well on devices with limited computing resources. Right now, we don’t know how to do this. The researchers studied the problem and found some surprising things. For example, the best way to customize an LLM might depend on what you’re trying to use it for. Also, giving the model more time to learn doesn’t always help. The study showed that compressed LLMs can be a good choice if you have limited data. |
Keywords
» Artificial intelligence » Scaling laws
