DeepSeek 175B在普通笔记本上完成20万蛋白质筛选，比A100集群快100倍，误差仅0.88，让小团队也能做药物发现。
研究人员成功将1750亿参数的DeepSeek 175B模型部署在单台RTX 4060笔记本电脑上，该设备配备32GB系统内存和8GB显存。该框架完成了针对20种不同蛋白质靶点的20万规模蛋白质配体虚拟筛选工作流。在相同任务配置下，该实现比8卡A100集群基线快100倍，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的1.0 kcal/mol化学精度要求。
Deploying DeepSeek 175B Locally on a Single Consumer-Grade RTX 4060 Laptop with 32GB RAM for 200k-Scale Protein-Ligand Virtual Screening
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.