PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression
Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses severe memory challenges for deployment.
This paper introduces PRIME, a unified framework for compressing Mixture-of-Experts (MoE) models by integrating pruning and merging operations through frequency-aware optimization.
What happened
The authors developed PRIME, a novel compression technique for MoE models that unifies pruning and merging operations. The method uses a frequency-aware objective function to optimize expert selection, demonstrated through experiments on large language models.
Why it matters
Efficient MoE compression is critical for deploying large language models in resource-constrained environments. PRIME's approach reduces memory requirements without significant accuracy loss, enabling more practical applications of complex AI systems.
- Unified pruning-merging frameworks improve MoE compression efficiency
- Frequency analysis enhances routing stability in expert systems
- Memory optimization is critical for practical large model deployment
- ILP-based approaches offer systematic solutions for complex model compression
The bigger picture
The method provides a scalable solution for MoE model compression, potentially enabling deployment of larger models on standard hardware while maintaining performance across diverse tasks.
PRIME presents an effective approach to MoE model compression by integrating pruning and merging through frequency-aware optimization, demonstrating significant memory savings without compromising model performance.
Technical details
- Integer Linear Programming (ILP) formulation for joint pruning-merging decisions
- Frequency-weighted surrogate objective function
- Expert usage frequency analysis for routing stability
- vLLM benchmarking for memory-throughput evaluation
FAQ
What is PRIME's main contribution?
PRIME introduces a unified framework for MoE compression that combines pruning and merging operations using frequency-aware Integer Linear Programming.
How does PRIME improve upon existing methods?
Unlike previous approaches that treat pruning and merging separately, PRIME optimizes both simultaneously with a frequency-weighted objective, reducing information loss and improving routing stability.
What are the practical benefits of this research?
The method enables more efficient deployment of large language models by significantly reducing memory requirements while maintaining performance across multiple tasks.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 14, 2026.
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