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

Rui Xiao, Yili Xu

arXiv:2609.17620v1q-bio.GNcs.LG

TL;DR

Clinical tumor WGS faces high computational costs and multi-day turnaround, with trillion-scale genomic LLM deployment relying on high-end GPU clusters. This work runs a low-resource workflow locally on consumer hardware. It reports an 18-hour per-sample turnaround, 88% shorter than traditional CPU cluster solutions, and a 99.62% F1 score for somatic variant detection.

  • Problem

    Trillion-scale genomic LLM deployment has relied on high-end GPU clusters with hundreds of gigabytes of VRAM, high per-site costs, and sample turnaround exceeding six business days.

    Source: §1 · p. 3

  • Method

    A complete low-resource tumor-paired WGS workflow runs locally on consumer hardware, including an RTX 4060 laptop with 8GB VRAM.

    Source: §1 · p. 4, §6 · p. 9

  • Results

    18 hours per sample was 88% shorter than traditional CPU cluster solutions, with a 99.62% F1 score for somatic variant detection.

    Source: §6 · p. 9

  • Takeaways & Limitations

    The work validates the feasibility of consumer-hardware clinical genomic computing as a low-cost, accessible, privacy-preserving pathway for precision oncology.

    Source: §5 · p. 8, §6 · p. 9

    The current workflow does not yet support detection of ultra-million-base-pair extra-large structural variants.

    Source: §5 · p. 9

Abstract

from arXiv · show

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.

1. Introduction

Tumor-paired WGS is compute-intensive, while trillion-scale genomic analysis has depended on costly, high-end GPU clusters and multi-day turnaround. This work proposes a low-resource workflow for consumer hardware, reporting an 18-hour 30X analysis and clinical-grade accuracy.

  • Disclosed trillion-scale genomic LLM deployments required A100/H100 clusters with hundreds of gigabytes of VRAM, costing over one million RMB per site and taking over six business days per sample.These hardware, operational, and time costs limit access for county-level and primary hospitals, which account for more than 90% of medical institutions worldwide.Source: §1 · p. 3
  • The work proposes an end-to-end low-resource workflow based on open-source trillion-parameter biomedical LLMs for clinical tumor WGS analysis.Its stated contributions include runtime and prediction-error decomposition, with adaptive heterogeneous memory management identified as a future optimization direction.Source: §1 · pp. 3–4
  • The study reports high-accuracy tumor-paired WGS analysis for 120 multi-cancer clinical solid tumor samples on one consumer-grade device.The scope includes simultaneous detection of short variants, copy number variations, large structural variants, and gene fusions across the genome.Source: §1 · pp. 3–4
  • An 18-hour 30X tumor-paired WGS analysis reduced turnaround by 88% versus traditional CPU cluster solutions while meeting clinical gold-standard accuracy requirements.The framework compresses peak VRAM occupancy to within 8GB, compatible with consumer GPUs and at least 16GB system memory.Source: §1 · p. 4

2. Related Work

Prior genomic LLM work emphasized accuracy using cloud clusters and closed models, while low-resource optimization for trillion-scale, long-sequence biomedical models remained underexplored. This paper applies edge medical computing to full-spectrum tumor WGS analysis.

2.1 LLM-driven Genomic Analysis

  • Earlier genomic LLM studies mainly used cloud high-performance clusters and closed-source commercial models, prioritizing accuracy over accessibility and carrying genetic-data privacy risks.Open-source trillion-parameter biomedical LLMs with million-base-pair context windows offer a basis for local WGS analysis.Source: §2.1 · pp. 4–5

2.2 Low-resource LLM Optimization

  • Low-resource LLM methods were largely evaluated on general NLP, while clinical-grade trillion-scale genomic analysis on 8GB VRAM remained underexplored.The study uses publicly available general optimization strategies without disclosing special implementation details.Source: §2.2 · p. 5

2.3 Edge AI for Medical Computing

  • This work extends edge medical computing from small-scale imaging and lightweight genetic annotation to trillion-parameter, full-variation-spectrum tumor WGS analysis.Edge AI is described as local deployment of complex medical models on consumer hardware to eliminate cloud dependency and enhance patient-data privacy.Source: §2.3 · p. 5

3. Methods

The methods combine low-resource model optimization and domain-specific adaptation with a fully local, sequential tumor-WGS pipeline. The pipeline covers quality control through clinical report export and supports continuous batch processing.

3.1 Low-resource Optimization Framework

  • Structured sparsity, mixed-precision quantization, and targeted removal of redundant weights reduce trillion-parameter model VRAM occupancy to fit an 8GB GPU.The framework is designed for stable operation with at least 16GB system memory.Source: §3.1 · pp. 5–6
  • Adaptive heterogeneous memory management schedules weight loading and computation to reduce data movement and avoid system-level out-of-memory interruptions.The optimization strategies combine publicly available technologies without disclosing special implementation details.Source: §3.1 · p. 6

3.2 Domain-specific Model Adaptation

  • Domain-specific adaptation uses 500,000 annotated tumor WGS paired samples to preserve subtle long-sequence variant features after model compression.The process is described as limiting accuracy loss to clinically acceptable thresholds.Source: §3.2 · p. 6

3.3 End-to-end Analysis Pipeline

  • The fully local pipeline sequentially performs FASTQ quality control, reference alignment, error recalibration, full-spectrum variant detection, and clinical report export.Batch-processing logic and automatic checkpoint resumption support continuous operation.Source: §3.3 · p. 6

4. Experimental Results

Across the reported tests, consumer hardware completed tumor-paired WGS faster than the traditional CPU cluster, while retaining high reported accuracy across variant classes. Runtime profiling identified memory scheduling as the largest overhead.

4.1 Experimental Setup

  • The RTX 4060 8GB VRAM/32GB system-memory setup was compared with 8×A100 and 32-core CPU GATK Mutect2 pipelines on 120 tumor samples, with HG002 included for accuracy validation.The clinical samples covered non-small cell lung cancer, breast cancer, soft tissue sarcoma, and cancer of unknown primary origin.Source: §4.1 · p. 7

4.2 Throughput Performance

  • 15.2±2.1 hours per sample on RTX 4060 was 88% shorter than the CPU cluster’s 132±24 hours; the 8×A100 cluster took approximately 8–12 hours.The consumer device processed 4–6 WGS samples during 72 hours of continuous operation.Source: §4.2 · p. 7

4.3 Bottleneck and Error Decomposition

  • Heterogeneous memory page-swap scheduling accounted for 71% of runtime versus 22% for GPU computation; model optimization contributed less than 9% of detection error, with 91% from sequencing noise and model generalization error.The authors identify system-level memory management, rather than model computation or optimization accuracy loss, as the core performance bottleneck.Source: §4.3 · p. 8

4.4 Generalization Accuracy

  • Somatic single-nucleotide variant F1 averaged 99.62%, copy number variant detection accuracy was 98.7%, and large structural variant F1 was 97.2%.The results met clinical tumor-testing accuracy standards and showed over 99.9% concordance with the gold-standard A100-cluster pipeline.Source: §4.4 · p. 8

5. Discussion

The discussion argues that clinical-grade tumor WGS can run on consumer-grade hardware without expensive GPU clusters while retaining required detection accuracy and processing data locally. It identifies memory scheduling as a runtime bottleneck and ultra-million-base-pair structural variants as a current limitation.

5. Discussion

  • Consumer-grade hardware can support clinical-grade tumor WGS without expensive GPU clusters, while retaining required detection accuracy and processing data locally.The authors frame this combination as a route to lower-cost, more accessible precision oncology.Source: §5 · p. 8
  • Memory scheduling overhead is the core performance bottleneck, motivating future hardware-aware prefetching and pipeline scheduling.These optimizations are expected to reduce single-sample runtime from 18 hours to under 12 hours.Source: §5 · p. 9
  • Ultra-million-base-pair extra-large structural variants remain a detection limitation in the current workflow.The authors say this will be addressed in future framework iterations.Source: §5 · p. 9
  • Open-source trillion-parameter LLMs could let primary medical institutions perform full-variation-spectrum tumor WGS without investing in expensive GPU clusters.The discussion presents this as lowering the hardware access threshold for precision oncology.Source: §5 · p. 9

6. Conclusion

The conclusion reports clinical-grade 30X tumor-paired WGS on consumer hardware, with an 18-hour turnaround and 99.62% F1 for somatic variant detection. It presents the workflow as a low-cost, accessible approach and describes the datasets used for validation and testing.

6. Conclusion

  • 18 hours per sample is 88% shorter than traditional CPU cluster solutions, with 99.62% F1 score for somatic variant detection meeting clinical-grade accuracy standards.Source: §6 · p. 9
  • The workflow performs clinical-grade 30X tumor-paired WGS on an RTX 4060 laptop with 8GB VRAM and on routine clinical workstations.Source: §6 · p. 9
  • The authors present open-source LLM deployment on consumer hardware as a low-cost, accessible paradigm for expanding precision oncology in primary medical institutions.Source: §6 · p. 9
  • Accuracy validation used the publicly available GIAB HG002 reference genome, and the 120 paired tumor-normal test samples came from TCGA and ICGC.Source: §6 · p. 10
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