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

Rui Xiao, Yili Xu

arXiv:2608.30877v1cs.LG

TL;DR

Large-scale LLM-driven virtual screening remains constrained by high-compute biomedical optimization needs and infrastructure requiring hundreds of gigabytes of memory. This work presents a low-resource DeepSeek 175B workflow on a consumer-grade RTX 4060 laptop, completing screening across 20 targets and 200,000 pairs with reported throughput and accuracy results. The workflow validates engineering feasibility on consumer hardware, while ultra-long protein sequences exceeding 2000 residues remain unsupported.

  • Problem

    Current industrial LLM-driven virtual screening pipelines require hundreds of gigabytes of physical memory and dedicated data-center infrastructure, highlighting a research gap in domain-specific optimization for high-compute-density biomedical tasks.

  • Method

    The paper proposes a complete low-resource DeepSeek 175B LLM workflow for protein-ligand virtual screening on a single consumer-grade RTX 4060 laptop with 32GB system RAM.

  • Results

    100x the 8-card A100 cluster baseline throughput was achieved within 72 hours while maintaining an average binding affinity prediction error of 0.88 kcal/mol.

  • Takeaways & Limitations

    The work validates the engineering feasibility of running the complete 20-target, 200,000-pair workflow on consumer hardware.

  • Takeaways & Limitations

    The workflow has limitations supporting ultra-long protein sequences exceeding 2000 residues, which subsequent framework iterations will address.

Abstract

from arXiv · show

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.

1. Introduction

The paper addresses the gap in adapting low-resource LLM optimization to high-compute biomedical screening, where direct compression can harm binding-affinity accuracy. It proposes and evaluates a complete DeepSeek 175B workflow on consumer hardware for large-scale screening.

  • Research gap: High-compute biomedical screening remains insufficiently served by low-resource LLM optimization research.Existing studies largely focus on general language tasks, leaving domain-specific biomedical scenarios underexplored.
  • Research gap: Direct pruning and quantization can substantially degrade binding affinity prediction accuracy below drug-discovery precision requirements.The paper identifies this as a technical pain point for real-world biomedical use.
  • Contribution: The proposed workflow deploys the 175B-parameter DeepSeek biomedical LLM on an RTX 4060 laptop with 8GB VRAM and 32GB system RAM.The implementation targets stable local execution on consumer hardware.
  • Contribution: Runtime and error decomposition identifies adaptive memory management as the main future optimization direction.The analysis separates system execution overhead from prediction-error sources.

2. Related Work

Prior protein-ligand LLM screening systems generally prioritized accuracy on high-end or cloud infrastructure, while low-resource methods were rarely adapted to biomedical workloads. The paper positions DeepSeek 175B and edge-side AI as a route toward local, large-scale virtual screening.

  • Existing screening systems: Existing industrial LLM-driven virtual screening workflows primarily use cloud high-performance clusters and closed commercial models.These systems emphasize prediction accuracy but impose substantial hardware requirements and raise data-privacy concerns.
  • Alternative models: Lightweight graph neural networks reduce computational cost but do not match the molecular representation capabilities attributed to very large LLMs.The related-work discussion contrasts lower cost with representation capacity.
  • Low-resource optimization: Low-resource optimization methods such as pruning, quantization, distillation, and heterogeneous memory scheduling have rarely been adapted to biomedical computing.Most validations use general NLP benchmarks including GLUE and MMLU.
  • Open challenge: Stable deployment of 175B-scale LLMs on 32GB RAM and 8GB VRAM hardware remains largely unexplored for industrial-scale tasks.Earlier consumer-GPU demonstrations were mainly limited to 7B–70B models.
  • Paper positioning: The paper extends edge-side AI for science from small-scale molecular and protein applications to large-scale DeepSeek 175B virtual screening.This extension is presented as expanding the capability boundary of edge biomedical computing.

3. Methods

The methods combine publicly available compression, precision, memory, and biomedical adaptation techniques to run DeepSeek 175B locally without system-level out-of-memory interruptions. The pipeline then performs local preprocessing, encoding, affinity prediction, and result export with continuous batching and resumption support.

  • Model optimization: Structured sparse processing, mixed-precision quantization, and adaptive memory management compress DeepSeek 175B to fit 8GB VRAM with 32GB system RAM.The framework removes redundant parameters considered irrelevant to molecular-interaction feature extraction.
  • Biomedical adaptation: Domain-specific adaptation uses 2.3 million annotated protein-ligand binding-affinity entries to preserve chemical interaction features.The adaptation process is designed for biomedical prediction after compression.
  • Biomedical adaptation: 0.2 kcal/mol is the stated upper bound for total accuracy loss from the adaptation process.The reported loss is below the acceptable pharmaceutical error threshold.
  • Screening pipeline: The local pipeline sequentially preprocesses sequences and SMILES, encodes molecules in batches, predicts affinities in parallel, and sorts and exports results.It runs on the RTX 4060 laptop without external cloud computing resources.
  • Screening pipeline: Continuous batching is tailored to DeepSeek 175B’s long-sequence characteristics and supports automatic breakpoint resumption for uninterrupted execution.These design choices target sustained operation across the full workflow.

4. Experimental Results

The evaluation compares a consumer RTX 4060 laptop workflow with an 8-card A100 cluster under matched screening conditions and profiles both runtime and prediction error. The laptop completed the full workload faster in the reported window while maintaining sub-chemical-threshold errors across all targets.

  • Experimental setup: The test laptop uses an RTX 4060 with 8GB VRAM and 32GB RAM, while the baseline is an 8-card A100 80GB cluster.Both platforms use the same open-source inference framework and identical 20-target, 200k-ligand data.
  • Throughput: 200,000 protein-ligand pairs were completed by the laptop within 72 hours, whereas the A100 cluster completed 2,000 pairs in the same window.The cluster therefore processed 1% of the total task volume reported for the laptop.
  • Throughput: 100x is the reported throughput of the consumer-hardware workflow relative to the high-end cluster baseline.The paper attributes the advantage primarily to a deeply optimized batching pipeline.
  • Runtime and error analysis: 72% of total execution time is attributed to heterogeneous memory page-swap scheduling overhead, compared with 21% for GPU core computation.The runtime profile identifies memory management as the dominant execution cost.
  • Runtime and error analysis: Less than 10% of total prediction error comes from model optimization, while 90% comes from dataset noise and model generalization error.The reported decomposition distinguishes compression-related loss from other error sources.
  • Cross-target accuracy: 0.88 kcal/mol is the average prediction error across all 20 targets, whose individual errors range from 0.5–0.95 kcal/mol.All target errors are reported below the 1.0 kcal/mol chemical-accuracy threshold.

5. Discussion

The discussion presents consumer-hardware deployment of DeepSeek 175B as feasible for large-scale biomedical computing, while identifying memory scheduling as the main optimization target and ultra-long proteins as a current boundary.

  • DeepSeek 175B biomedical computing tasks do not require expensive high-end GPU clusters when paired with low-resource optimization and targeted workflow design.The discussion frames this combination as achieving comparable or superior throughput on consumer hardware while retaining chemical-level prediction accuracy.
  • 72% of total execution time is attributed to page swap scheduling overhead, making heterogeneous memory management the core performance bottleneck.The proposed optimization direction is hardware-aware prefetch and related memory-management improvements.
  • 72 hours is the current time required for 200k-scale screening, with design and operator fusion optimization expected to reduce it to within 24 hours.The within-24-hour figure is presented as a future-work expectation rather than a current result.
  • The workflow remains limited in supporting ultra-long protein sequences exceeding 2000 residues.The discussion states that subsequent framework iterations will address this limitation.
  • The framework is presented as lowering hardware access barriers for small research teams and independent researchers conducting large-scale virtual screening with open-source LLMs.The stated consequence is broader accessibility for AI-driven pharmaceutical research without expensive GPU clusters.

6. Conclusion

The conclusion reports a complete high-precision 200,000-pair screening workflow across 20 targets on one RTX 4060 laptop with 32GB system RAM. It reports 100x the throughput of an 8-card A100 baseline while maintaining 0.88 kcal/mol average binding-affinity prediction error, and presents the result as evidence of consumer-hardware feasibility for industrial biomedical computing.

  • 20 targets and 200,000 protein-ligand pairs were screened through a complete high-precision workflow on a single consumer-grade RTX 4060 laptop with 32GB system RAM.
  • 100x throughput was achieved relative to the 8-card A100 cluster baseline within a 72-hour workflow.
  • 0.88 kcal/mol average binding affinity prediction error was maintained, satisfying industrial-grade chemical accuracy standards.
  • The work validates the feasibility of deploying trillion-parameter industrial biomedical computing tasks based on open-source LLMs on consumer hardware.
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