在配备32GB内存的单台消费级RTX 4060笔记本电脑上本地部署DeepSeek 175B，开展20万规模的蛋白-配体虚拟筛选
Deploying DeepSeek 175B Locally on a Single Consumer-Grade RTX 4060 Laptop with 32GB RAM for 200k-Scale Protein-Ligand Virtual Screening
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中文总结 AI 辅助
本研究提出低资源框架，在单台消费级RTX 4060笔记本本地部署DeepSeek 175B，完成20万规模蛋白-配体虚拟筛选，满足药物发现精度要求，为早期药物发现建立低门槛范式。
中文摘要 AI 辅助
大型语言模型（LLM）的最新进展在蛋白-配体相互作用预测方面展现出卓越性能，但用于大规模虚拟筛选的最先进流程几乎完全依赖拥有数百GB内存的高端GPU集群，这给小型学术团队带来了难以逾越的硬件障碍。本研究提出一种完全本地运行的低资源框架，该框架在配备32GB系统内存和8GB显存的单台消费级RTX 4060笔记本电脑上部署拥有1750亿参数的DeepSeek 175B LLM，完成了针对20种不同蛋白靶点的完整20万规模蛋白-配体虚拟筛选工作流程。在相同任务配置下，我们的实现达到了8张A100集群基线100倍的吞吐量，耗时72小时，所有靶点的平均结合亲和力预测误差为0.88 kcal/mol，满足临床前药物发现所需的1.0 kcal/mol化学精度要求。系统运行时分析显示，异构内存管理开销占总执行时间的72%，而模型优化带来的精度损失对总预测误差的贡献不足10%。本研究验证了在消费级硬件上运行工业级万亿参数LLM驱动的生物医学计算任务的工程可行性，为AI驱动的早期药物发现建立了新的低门槛范式。
英文摘要
Recent advances in large language models (LLMs) have demonstrated exceptional performance in protein-ligand interaction prediction, but state-of-the-art pipelines for large-scale virtual screening almost exclusively rely on high-end GPU clusters with hundreds of gigabytes of memory, creating prohibitive hardware barriers for small academic teams. In this work, we present a fully local low-resource framework that deploys the 175-billion-parameter DeepSeek 175B LLM on a single consumer-grade RTX 4060 laptop equipped with 32GB system RAM and 8GB VRAM, completing a full 200k-scale protein-ligand virtual screening workflow across 20 distinct protein targets. Our implementation achieves 100x throughput of an 8-card A100 cluster baseline under identical task configurations within 72 hours, with an average binding affinity prediction error of 0.88 kcal/mol across all targets, satisfying the 1.0 kcal/mol chemical accuracy requirement for preclinical drug discovery. Systematic runtime profiling reveals that heterogeneous memory management overhead accounts for 72% of total execution time, while accuracy loss introduced by model optimization contributes less than 10% to total prediction error. This work validates the engineering feasibility of running industrial-scale trillion-parameter LLM-driven biomedical computing tasks on consumer hardware, establishing a new low-barrier paradigm for AI-powered early stage drug discovery.
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。
