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EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models

arXiv 2508.09471 ↗First posted 13 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

As Large Language Models (LLMs) become more widely adopted and scale up in size, the computational and memory challenges involved in deploying these massive foundation models have grown increasingly severe. This underscores the urgent need to develop more efficient model variants. Faced with this challenge, the present work introduces EGGS-PTP: an Expander-Graph Guided Structured Post-training Pruning method. The proposed approach leverages graph theory to guide the design of N:M structured pruning, effectively reducing model size and computational demands. By incorporating concepts from expander graphs, EGGS-PTP ensures information flow within the pruned network, preserving essential model functionality. Extensive numerical experiments demonstrate that EGGS-PTP not only achieves significant acceleration and memory savings due to structured sparsity but also outperforms existing structured pruning techniques in terms of accuracy across various LLMs.

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References · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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  4. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  5. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
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  7. 2023SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shotcited by 1,121
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  9. 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 800
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  11. 2020Movement Pruning: Adaptive Sparsity by Fine-Tuningcited by 403
  12. 2017Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Sciencecited by 328
10 more
  1. 2021Accelerating Sparse Deep Neural Networkscited by 223
  2. 2020BinaryBERT: Pushing the Limit of BERT Quantizationcited by 178
  3. 2021Sparse Training via Boosting Pruning Plasticity with Neuroregenerationcited by 84
  4. 2021Prune Once for All: Sparse Pre-Trained Language Modelscited by 66
  5. 2024AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Modelscited by 36
  6. 2017Deep Expander Networks: Efficient Deep Networks from Graph Theorycited by 23
  7. 2024SparseLLM: Towards Global Pruning for Pre-trained Language Modelscited by 23
  8. 2024Pruning Large Language Models with Semi-Structural Adaptive Sparse Trainingcited by 20
  9. 2025EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Modelscited by 10
  10. 2023Explicit two-sided unique-neighbor expanderscited by 1

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