{
  "abstract": "Whole genome sequencing (WGS) is essential for precision oncology, yet its clinical adoption remains limited by prohibitive computational costs and multi-day turnaround times. This work presents a fully localized low-resource framework enabling stable deployment of a trillion-parameter biomedical LLM on a single consumer-grade RTX 4060 laptop with 32GB system memory and 8GB VRAM, as well as on routine clinical workstations in general hospitals, completing the entire tumor-paired WGS workflow from raw FASTQ input to clinical-grade full-variation-spectrum report output. Under standard 30X depth configurations, our implementation finishes a single tumor-paired WGS analysis within 18 hours, achieving 99.62% F1 score for somatic variant detection with over 99.9% concordance to the industrial-standard A100 cluster pipeline, fully meeting clinical oncology accuracy requirements. Quantitative profiling shows adaptive heterogeneous memory scheduling accounts for 71% of total execution time, while model optimization introduces less than 9% of total detection error. This work is the first engineering implementation of trillion-parameter biomedical LLM-driven clinical-grade genomic analysis on consumer-grade hardware, breaking the industry paradigm that trillion-scale genomic LLMs require hundred-thousand-dollar GPU clusters and multi-day turnaround, establishing a low-resource pathway for global primary medical institutions to adopt whole-genome precision oncology at zero additional cost.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17620",
  "author_total": 2,
  "authors": [
    {
      "name": "Rui Xiao"
    },
    {
      "name": "Yili Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6hfjtm7c1a5vr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17620",
  "fulltext_url": null,
  "id": "6hfjtm7c1a5vr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17620",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1306|Cancer Genomics and Diagnostics"
    ]
  ],
  "openalex": "W7213518529",
  "pdf_url": "https://arxiv.org/pdf/2609.17620",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6hfjtm7c1a5vr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:05Z",
      "native_id": "2609.17620",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17620"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware",
  "title_text": "Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:05Z",
  "url": "https://research.draconitic.com/paper/6hfjtm7c1a5vr",
  "venue": "arXiv",
  "versions": []
}