Summary of Computational Bottlenecks Of Training Small-scale Large Language Models, by Saleh Ashkboos et al.
Computational Bottlenecks of Training Small-scale Large Language Models
by Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar, Fartash Faghri
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This study investigates the computational requirements of small-scale large language models (SLMs) with up to 2 billion parameters, which are gaining attention due to cost and efficiency demands. The researchers explore how various hyperparameters and configurations affect training behavior, including GPU type, batch size, model size, communication protocol, attention type, and number of GPUs. They assess these factors on popular cloud services using metrics such as loss per dollar and tokens per second. The findings aim to support the broader adoption and optimization of language model training for low-resource AI research institutes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. This study explores how different hyperparameters and configurations affect SLMs, which is important because there is limited research on this topic. The researchers looked at things like the type of computer chip used, the size of the data set, and the way information is shared between computers. They also looked at how these factors change when using different cloud services. The study’s findings aim to help make language model training more efficient and accessible for low-resource AI research institutes. |
Keywords
* Artificial intelligence * Attention * Language model * Optimization
