Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

• URL: http://arxiv.org/abs/2606.07819v1
• Date: Fri, 05 Jun 2026 20:02:29 GMT
• Title: Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
• Abstract summary: Post-training quantization (PTQ) and structural pruning are established techniques for reducing memory footprint and inference latency.<n>Most existing PTQ approaches optimize quantization errors on a per-layer basis, overlooking how errors accumulate and propagate through the network.<n>We introduce a novel end-to-end framework that addresses these limitations in two key ways.
• Score: 1.4857783122641302
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantization (PTQ) and structural pruning are established techniques for reducing memory footprint and inference latency, most existing PTQ approaches optimize quantization errors on a per-layer basis, overlooking how errors accumulate and propagate through the network, often resulting in suboptimal solutions. Traditional pipelines also tend to apply pruning and quantization in isolation or sequentially, further compounding sub-optimality. We introduce a novel end-to-end framework that addresses these limitations in two key ways. First, we propose a novel mixed-precision PTQ strategy that directly minimizes global error propagation across the entire model, rather than isolating layer-wise errors. Building on this, we develop a novel joint optimization approach that simultaneously learns structural pruning decisions and mixed-precision quantization policies within a unified search space. Extensive experiments show that, at ultra-low precisions (1-3 bits), our quantization method reduces WikiText perplexity by up to 21% compared to state-of-the-art (SoTA) weight-activation quantization baselines. Against leading weight-only quantization methods, it achieves up to 59% and 85% lower perplexity on WikiText and C4, respectively. Compared to the SoTA joint pruning-and-quantization techniques, our proposed method delivers superior perplexity and reasoning performance at ultra-low bits.

Related papers

• RUQuant: Towards Refining Uniform Quantization for Large Language Models [17.258420059228808]
  Post-training quantization (PTQ) has emerged as a practical solution by compressing models without requiring retraining.<n>Existing methods often suffer from substantial accuracy degradation due to the non-uniform nature of activation distributions.<n>In this work, we revisit the activation quantization problem from a theoretical perspective grounded in the Lloyd-Max optimality conditions.
  arXiv  Detail & Related papers  (2026-04-05T08:04:39Z)
• Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models [41.677469535447024]
  Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-constrained devices.<n>Post-training quantization (PTQ) is widely adopted for its efficiency, as it requires no retraining and only a small dataset for calibration.<n>Recent advances for post-training quantization have demonstrated that even sub-4-bit methods can maintain most of the original model performance.
  arXiv  Detail & Related papers  (2025-12-25T12:39:36Z)
• Beyond Outliers: A Study of Optimizers Under Quantization [82.75879062804955]
  We study impact of choice on model robustness under quantization.<n>We evaluate how model performance degrades when trained with different baselines.<n>We derive scaling laws for quantization-aware training under different parameters.
  arXiv  Detail & Related papers  (2025-09-27T21:15:22Z)
• End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost [53.25965863436039]
  Quantization-aware training (QAT) provides a more principled solution, but its reliance on backpropagation incurs prohibitive memory costs.<n>We propose ZeroQAT, a zeroth-order optimization-based QAT framework that supports both weight and activation quantization.<n>Experiments show that ZeroQAT consistently outperforms representative PTQ and QAT baselines while requiring significantly less memory.
  arXiv  Detail & Related papers  (2025-08-21T01:18:27Z)
• A Comprehensive Evaluation on Quantization Techniques for Large Language Models [46.75040730001041]
  Post-training quantization (PTQ) can significantly reduce memory footprint and computational overhead for large language models (LLMs)<n>We conduct an extensive review of state-of-the-art methods and perform comprehensive evaluations under the same conditions for fair comparison.<n>We evaluate and evaluate the latest MXFP4 and NVFP4 data formats and their performance.
  arXiv  Detail & Related papers  (2025-07-23T11:21:21Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation [55.12070409045766]
  Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression paradigm in recent years.<n>Current PTQ methods for Vision Transformers (ViTs) still suffer from significant accuracy degradation, especially under low-bit quantization.
  arXiv  Detail & Related papers  (2025-06-13T07:57:38Z)
• Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization [0.0]
  Layer-wise PTQ is a promising technique for compressing large language models (LLMs)<n>Recent progress in this area is saturating, underscoring the need to revisit its core limitations and explore further improvements.<n>We propose Quantization Error Propagation (QEP), a general, lightweight, and scalable framework that enhances layer-wise PTQ by explicitly propagating quantization errors and compensating for accumulated errors.
  arXiv  Detail & Related papers  (2025-04-13T15:56:00Z)
• PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models [64.84734437930362]
  Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization.<n>We propose an extremely low-bit PTQ method called PTQ1.61, which enables weight quantization to 1.61-bit for the first time.<n>Experiments indicate our PTQ1.61 achieves state-of-the-art performance in extremely low-bit quantization.
  arXiv  Detail & Related papers  (2025-02-18T08:04:58Z)
• Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart [1.5508907979229383]
  Quantization-aware training (QAT) is a common paradigm for network quantization.<n>The low-precision model exhibits limited representation capabilities and cannot directly replicate full-precision calculations.<n>We propose a general QAT framework for alleviating the concerns by permitting the forward and backward processes of the low-precision network to be guided by the full-precision partner.
  arXiv  Detail & Related papers  (2024-12-20T12:38:18Z)
• Pushing the Limits of Large Language Model Quantization via the Linearity Theorem [71.3332971315821]
  We present a "line theoremarity" establishing a direct relationship between the layer-wise $ell$ reconstruction error and the model perplexity increase due to quantization. This insight enables two novel applications: (1) a simple data-free LLM quantization method using Hadamard rotations and MSE-optimal grids, dubbed HIGGS, and (2) an optimal solution to the problem of finding non-uniform per-layer quantization levels.
  arXiv  Detail & Related papers  (2024-11-26T15:35:44Z)
• CBQ: Cross-Block Quantization for Large Language Models [66.82132832702895]
  Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.<n>We propose CBQ, a cross-block reconstruction-based PTQ method for LLMs.<n> CBQ employs a cross-block dependency using a reconstruction scheme, establishing long-range dependencies across multiple blocks to minimize error accumulation.
  arXiv  Detail & Related papers  (2023-12-13T07:56:27Z)
• Norm Tweaking: High-performance Low-bit Quantization of Large Language Models [21.855106896725598]
  We introduce a technique called norm tweaking, which can be used as a plugin in current PTQ methods to achieve high precision. Our method demonstrates significant improvements in both weight-only quantization and joint quantization of weights and activations. Our simple and effective approach makes it more practical for real-world applications.
  arXiv  Detail & Related papers  (2023-09-06T06:51:15Z)
• Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees [53.950234267704]
  We introduce Global-QSGD, an All-reduce gradient-compatible quantization method.<n>We show that it accelerates distributed training by up to 3.51% over baseline quantization methods.
  arXiv  Detail & Related papers  (2023-05-29T21:32:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.