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

arXiv:2404.10933v1 Announce Type: cross
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 …

abstract arxiv challenges constraints cs.ai cs.cl cs.lg distributed fine-tuning gpu hardware however language language models large language large language models llms memory type usage