MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?

• URL: http://arxiv.org/abs/2509.07727v1
• Date: Tue, 09 Sep 2025 13:28:41 GMT
• Title: MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
• Abstract summary: We present a comprehensive analysis of how compression-induced errors in different experts affect overall inference accuracy.<n>The results indicate that experts in the shallow layers exhibit minimal degradation in inference accuracy when subjected to bounded errors.<n>In contrast, errors in the middle-layer experts, which are central to model reasoning, significantly impair inference accuracy.
• Score: 8.691203544306157
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has emerged as a significant challenge. Offloading the non-activated experts to main memory has been identified as an efficient approach to address such a problem, while it brings the challenges of transferring the expert between the GPU memory and main memory. We need to explore an efficient approach to compress the expert and analyze how the compression error affects the inference performance. To bridge this gap, we propose employing error-bounded lossy compression algorithms (such as SZ3 and CuSZp) to compress non-activated experts, thereby reducing data transfer overhead during MoE inference. We conduct extensive experiments across various benchmarks and present a comprehensive analysis of how compression-induced errors in different experts affect overall inference accuracy. The results indicate that experts in the shallow layers, which are primarily responsible for the attention mechanism and the transformation of input tokens into vector representations, exhibit minimal degradation in inference accuracy when subjected to bounded errors. In contrast, errors in the middle-layer experts, which are central to model reasoning, significantly impair inference accuracy. Interestingly, introducing bounded errors in the deep-layer experts, which are mainly responsible for instruction following and output integration, can sometimes lead to improvements in inference accuracy.

Related papers

• Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs [2.464099668230234]
  Mixture-of-experts (MoE) architectures scale large language models efficiently.<n>To cope with such demand, models are commonly compressed to reduce their memory footprint.<n>We propose PARSER, a new residual sparsification method that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error.
  arXiv  Detail & Related papers  (2026-09-01T02:11:40Z)
• Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression [74.00650541246374]
  We propose a structural pruning framework tailored for MoE models.<n>Our approach reduces memory footprint by 5.27$times and consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-06-16T06:53:27Z)
• TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts [57.78282303757474]
  We propose TENP, a structured Trapezoidal ExpertNeuron Pruning framework.<n>We identify and retain important experts, while applying expert neuron pruning (ENP) to less important experts.<n>For ENP, we measure each neuron's projected contribution to the expert output to identify and retain important neurons.
  arXiv  Detail & Related papers  (2026-06-03T07:49:31Z)
• Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees [13.67631667766697]
  Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input.<n>MoE incurs substantial memory overhead during inference.<n>We propose a theoretically grounded expert-wise mixed precision strategy that assigns bit-width to each expert based on their change in routers l2 norm during training.
  arXiv  Detail & Related papers  (2026-04-07T23:17:23Z)
• Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition [53.68517860700599]
  We propose an ensemble hypergradient strategy to reduce the variance in HPO algorithms effectively.<n>We establish a connection between excess error and hypergradient estimation, offering some understanding of empirical observations.
  arXiv  Detail & Related papers  (2026-02-20T02:52:13Z)
• Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models [66.36240676392502]
  Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems.<n>Recent studies reveal a sharp performance drop in reasoning hop generalization scenarios.<n>We propose test-time correction of reasoning, a lightweight intervention method that dynamically identifies and deactivates ep heads in the reasoning process.
  arXiv  Detail & Related papers  (2026-01-29T03:24:32Z)
• ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization [13.182475975397251]
  ERMoE is a sparse MoE transformer that replaces learned gating logits with an "Eigenbasis Score"<n>We show that ERMoE achieves state-of-the-art accuracy on ImageNet classification and cross-modal image-text retrieval benchmarks.<n>A 3D MRI variant (ERMoE-ba) improves brain age prediction accuracy by more than 7% and yields interpretable expert specializations.
  arXiv  Detail & Related papers  (2025-11-14T05:31:37Z)
• Convergence Rates for Softmax Gating Mixture of Experts [78.3687645289918]
  Mixture of experts (MoE) has emerged as an effective framework to advance the efficiency and scalability of machine learning models.<n>Central to the success of MoE is an adaptive softmax gating mechanism which takes responsibility for determining the relevance of each expert to a given input and then dynamically assigning experts their respective weights.<n>We perform a convergence analysis of parameter estimation and expert estimation under the MoE equipped with the standard softmax gating or its variants, including a dense-to-sparse gating and a hierarchical softmax gating.
  arXiv  Detail & Related papers  (2025-03-05T06:11:24Z)
• HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference [54.40808356999408]
  We present HOBBIT, a mixed precision expert offloading system to enable flexible and efficient MoE inference. Our key insight is that dynamically replacing less critical cache-miss experts with low precision versions can substantially reduce expert-loading latency. HOBBIT achieves up to a 9.93x speedup in decoding compared to state-of-the-art MoE offloading systems.
  arXiv  Detail & Related papers  (2024-11-03T04:25:46Z)
• Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning [15.78336840511033]
  This paper introduces a novel framework designed to achieve a high compression ratio in Split Learning (SL) scenarios. Our investigations demonstrate that compressing feature maps within SL leads to biased gradients that can negatively impact the convergence rates. We employ a narrow bit-width encoded mask to compensate for the sparsification error without increasing the order of time complexity.
  arXiv  Detail & Related papers  (2024-08-25T09:30:34Z)
• Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study [65.11303133775857]
  Mixture-of-Experts (MoE) computation amalgamates predictions from several specialized sub-models (referred to as experts) Sparse MoE selectively engages only a limited number, or even just one expert, significantly reducing overhead while empirically preserving, and sometimes even enhancing, performance.
  arXiv  Detail & Related papers  (2024-03-26T05:48:02Z)
• Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization [51.98792406392873]
  Mixture of Experts (MoE) provides a powerful way to decompose dense layers into smaller, modular computations. A major challenge lies in the computational cost of scaling the number of experts high enough to achieve fine-grained specialization. We propose the Multilinear Mixture of Experts ($mu$MoE) layer to address this, focusing on vision models.
  arXiv  Detail & Related papers  (2024-02-19T21:20:22Z)
• The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models [11.156816338995503]
  Large language models (LLMs) provide faster inference, smaller memory footprints, and enables local deployment. Two standard compression techniques are pruning and quantization, with the former eliminating redundant connections in model layers and the latter representing model parameters with fewer bits. Existing research on LLM compression primarily focuses on performance in terms of general metrics like perplexity or downstream task accuracy. More fine-grained metrics, such as those measuring parametric knowledge, remain significantly underexplored.
  arXiv  Detail & Related papers  (2023-12-01T22:27:12Z)
• MoEC: Mixture of Expert Clusters [93.63738535295866]
  Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation.
  arXiv  Detail & Related papers  (2022-07-19T06:09:55Z)
• Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability [3.021134753248103]
  Sparsely-gated Mixture of Expert (MoE) layers have been successfully applied for scaling large transformers. In this work, we apply sparse MoE layers to CNNs for computer vision tasks and analyze the resulting effect on model interpretability.
  arXiv  Detail & Related papers  (2022-04-22T09:40:23Z)

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.