LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
Taeho Kim Yanming Wang Vatshank Chaturvedi Lokesh Gupta Seyeon Kim Yongin Kwon Sangtae Ha
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.