Computational Bottlenecks of Training Small-scale Large Language Models

• URL: http://arxiv.org/abs/2410.19456v2
• Date: Sun, 01 Dec 2024 11:27:09 GMT
• Title: Computational Bottlenecks of Training Small-scale Large Language Models
• Authors: Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar, Fartash Faghri,
• Abstract summary: Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. In this study, we explore the computational bottlenecks of training SLMs. We assess these factors on popular cloud services using metrics such as loss per dollar and tokens per second.
• Score: 19.663560481459164
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• Abstract: While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. However, there is limited research on the training behavior and computational requirements of SLMs. In this study, we explore the computational bottlenecks of training SLMs (up to 2B parameters) by examining the effects of various hyperparameters and configurations, including GPU type, batch size, model size, communication protocol, attention type, and the number of GPUs. We assess these factors on popular cloud services using metrics such as loss per dollar and tokens per second. Our findings aim to support the broader adoption and optimization of language model training for low-resource AI research institutes.

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