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DeepSeek 175B LLM runs on consumer laptop for drug discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated the feasibility of running the large language model DeepSeek 175B on a single consumer-grade laptop with 32GB of RAM and 8GB of VRAM. This setup was used to perform a 200,000-scale protein-ligand virtual screening workflow, achieving a throughput 100 times greater than an 8-card A100 GPU cluster. The implementation successfully met the chemical accuracy requirements for preclinical drug discovery, with an average binding affinity prediction error of 0.88 kcal/mol. This work suggests a new paradigm for AI-powered drug discovery, enabling such tasks on more accessible hardware. AI

IMPACT Enables large-scale AI-driven drug discovery tasks on consumer hardware, lowering barriers for researchers.

RANK_REASON Research paper detailing a novel deployment method for a large language model on consumer hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek 175B LLM runs on consumer laptop for drug discovery

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23 / 100
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Research paper detailing a novel deployment method for a large language model on consumer hardware. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rui Xiao, Yili Xu ·

    Deploying DeepSeek 175B Locally on a Single Consumer-Grade RTX 4060 Laptop with 32GB RAM for 200k-Scale Protein-Ligand Virtual Screening

    arXiv:2608.30877v1 Announce Type: new Abstract: 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…

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