Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware

This paper reports a fully localized, low‑resource framework that runs a trillion‑parameter biomedical large language model on a single consumer‑grade RTX 4060 laptop (32 GB system memory, 8 GB VRAM) and routine clinical workstations. The system completes an entire tumor‑paired whole‑genome sequencing workflow—from raw FASTQ input to a clinical‑grade full‑variation‑spectrum report—in 18 hours at 30× depth, achieving a 99.62 % F1 score for somatic variant detection and over 99.9 % concordance with an industrial‑standard A100 cluster pipeline. The study demonstrates that adaptive heterogeneous memory scheduling accounts for 71 % of execution time and that model optimization adds less than 9 % of detection error, establishing a low‑cost, high‑accuracy pathway for global primary medical institutions to adopt precision oncology without expensive GPU clusters.

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