发表机构
Hangzhou Tsingxin quantum Co., Ltd.（杭州清芯量子有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在消费级硬件上本地部署万亿参数生物医学大语言模型，实现18小时内完成肿瘤全基因组测序分析，达到99.62%的变异检测F1分数，打破了对昂贵GPU集群的依赖，为基层医疗提供零额外成本的精准肿瘤学方案。
AI 中文摘要
全基因组测序（WGS）对于精准肿瘤学至关重要，然而其临床应用仍受限于高昂的计算成本和数天的周转时间。本研究提出一个完全本地化的低资源框架，能够在配备32GB系统内存和8GB显存的单台消费级RTX 4060笔记本电脑以及综合医院的常规临床工作站上，稳定部署万亿参数的生物医学大语言模型，完成从原始FASTQ输入到临床级全变异谱报告输出的完整肿瘤配对WGS工作流程。在标准30X深度配置下，我们的实现可在18小时内完成单例肿瘤配对WGS分析，体细胞变异检测的F1分数达到99.62%，与工业标准的A100集群流程一致性超过99.9%，完全满足临床肿瘤学准确性要求。定量分析显示，自适应异构内存调度占总执行时间的71%，而模型优化引入的检测误差不到总误差的9%。本研究是首次在消费级硬件上实现万亿参数生物医学大语言模型驱动的临床级基因组分析的工程实现，打破了万亿级基因组大语言模型需要数十万美元GPU集群和数天周转时间的行业范式，为全球基层医疗机构以零额外成本采用全基因组精准肿瘤学建立了一条低资源路径。
英文摘要
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.
Comments11 pages. Corresponding author: Yili Xu(22465225@qq.com)
