LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
This addresses the problem of efficient fine-tuning for researchers and practitioners with limited hardware, though it is incremental as it builds on existing distributed methods.
The paper tackles the challenge of GPU memory constraints in fine-tuning large language models by introducing LLMem, a tool that estimates memory usage and identifies optimal distributed fine-tuning methods, achieving error rates as low as 1.6% on single GPUs and 3.0% on multi-GPU setups.
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.