LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs

• URL: http://arxiv.org/abs/2404.10933v1
• Date: Tue, 16 Apr 2024 22:11:35 GMT
• Title: LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
• Authors: Taeho Kim, Yanming Wang, Vatshank Chaturvedi, Lokesh Gupta, Seyeon Kim, Yongin Kwon, Sangtae Ha,
• Abstract summary: Fine-tuning pre-trained large language models with limited hardware presents challenges due to GPU memory constraints. We introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods. We show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%.
• Score: 4.536118764799076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given environment remains unclear. To address this challenge, we introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods across multiple GPUs and identifies the optimal method. We conduct GPU memory usage estimation prior to fine-tuning, leveraging the fundamental structure of transformer-based decoder models and the memory usage distribution of each method. Experimental results show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%. Additionally, it shows an average error rate of 3.0% when applying distributed fine-tuning methods to LLMs with more than a billion parameters on multi-GPU setups.

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