{
  "abstract": "The rapid scaling of large language models has created significant barriers to fine-tuning, with standard approaches requiring computational resources far exceeding typical hardware constraints. A 7-billion parameter model demands roughly 100 to 120GB of memory for full fine-tuning under mixed-precision training, effectively limiting model customization to well-funded organizations with access to expensive multi-GPU infrastructure. This survey reviews resource-efficient fine-tuning methods through four complementary approaches: parameter-efficient fine-tuning (PEFT) methods including Low-Rank Adaptation (LoRA) and its variants, adapter-based architectures, and prompt-tuning strategies; quantization techniques such as QLoRA, GPTQ, and AWQ that reduce memory footprints through lower-precision representations; memory optimization strategies encompassing gradient checkpointing, FlashAttention, and CPU/disk offloading; and distributed and federated learning approaches that enable collaborative training across heterogeneous devices while preserving data privacy. Each approach reduces memory substantially, and stacked configurations reach roughly an order-of-magnitude reduction relative to the approximately 112 GB required for mixed-precision full fine-tuning of a 7-billion-parameter model (Rajbhandari et al., 2020). Parameter-efficient methods match or approach full fine-tuning quality on standard classification and natural language understanding benchmarks (Hu et al., 2021), with task-dependent gaps on complex generation. These figures are drawn from across the surveyed studies rather than a single controlled evaluation. Based on empirical evidence from recent literature, we develop a decision framework for method selection under varying resource constraints and identify open challenges including automated hyperparameter selection, joint quantization-adaptation optimization, and scalable federated learning. This survey aims to democratize access to large language model customization by providing practitioners and researchers with actionable guidance for fine-tuning under realistic resource limitations.\n\nPreprint. This manuscript is under review at PeerJ Computer Science and has not completed peer review. Companion repository: https://github.com/jerryadamsfranklin/llm-efficient-finetuning-survey",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jerry Adams Franklin",
      "orcid": "0009-0006-8470-8349"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/66hhtke4bva1f.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23504006",
  "fulltext_url": null,
  "id": "66hhtke4bva1f",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23504006",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23504006/files/Franklin_LLM_finetuning_survey_preprint_v1.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/66hhtke4bva1f.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:40Z",
      "native_id": "23504006",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23504006"
    }
  ],
  "subjects": [],
  "title": "Fine-tuning large language models in resource-constrained environments: Methods and trade-offs",
  "title_text": "Fine-tuning large language models in resource-constrained environments: Methods and trade-offs",
  "type": "preprint",
  "updated_at": "2026-11-03T12:20:40Z",
  "url": "https://research.draconitic.com/paper/66hhtke4bva1f",
  "venue": "Zenodo",
  "versions": []
}