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mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

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mLoRA significantly reduces fine-tuning time by 30% for large language models, suggesting improved efficiency with multiple GPUs.

mLoRA enables simultaneous fine-tuning of two Llama-2-13B models on four GPUs, increasing efficiency and accessibility.
Average fine-tuning task completion time can be reduced by 30% compared to existing methods like FSDP.
The novel LoRA-aware pipeline parallelism scheme enhances GPU utilization and reduces communication overhead.
This approach allows developers to adapt large language models to various tasks simultaneously, promoting cost-effective solutions.

Ye et al. (2025) studied this question.

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