Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes

arXiv — cs.CL•Wednesday, November 5, 2025 at 5:00:00 AM

A recent study highlights the significant challenges posed by the large size of language models (LLMs), which restricts storage and processing capabilities. Most current applications rely on pre-trained LLMs that are subsequently fine-tuned for specific tasks to improve performance. However, even these fine-tuned models continue to present substantial storage difficulties, complicating their deployment in environments with limited resources. This issue underscores the tension between the growing complexity of LLMs and the practical constraints of hardware infrastructure. The study contributes to ongoing discussions about optimizing LLM inference efficiency, particularly in low storage regimes. These findings align with broader research trends addressing the scalability and accessibility of advanced AI models. As LLMs become more integral to various applications, resolving storage and efficiency challenges remains a critical focus for the field.

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