PRIME Framework Released: Revolutionizing MoE Model Compression for Enhanced Inference Efficiency and Memory Optimizatio

Summary:PRIME is an innovative unified optimization framework for compressing Mixture-of-Experts (MoE) models through adaptive pruning and merging. By casting the discrete prune-merge assignment problem as a frequency-weighted global Integer Linear Programming, PRIME jointly optimizes pruning and merging decisions while accounting for expert usage. This approach maintains high accuracy while significantly reducing memory footprint and improving inference efficiency. Experimental results on Qwen3-30B-A3B

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Technical Mechanism Analysis

The core innovation of the PRIME framework lies in unifying the pruning and merging processes of MoE models into a single global optimization problem. Specifically, PRIME transforms the discrete prune-merge assignment problem into a frequency-weighted global Integer Linear Programming (ILP) through the following steps:

1. Frequency-Weighted Objective Function: PRIME introduces a frequency-weighted objective function, prioritizing high-frequency experts as centroids through an error-bound analysis under fixed representative assignments, thereby enhancing routing stability.
2. Global Optimization: Jointly optimizes pruning and merging decisions, avoiding the irreversible information loss or representational pollution that often occurs with traditional isolated approaches.
3. Integer Linear Programming: Utilizes an ILP solver to find the optimal solution, ensuring that the computation is feasible within limited computational resources.

Engineering Trade-offs and Experimental Performance

PRIME demonstrates strong performance across multiple benchmarks:

• Accuracy Retention: On Qwen3-30B-A3B and Qwen3.5-122B-A10B models, PRIME achieves robust accuracy retention across knowledge, coding, math, and long-context tasks.
• Memory and Throughput: In vLLM benchmarks, PRIME significantly reduces memory footprint and improves throughput, showcasing its advantages in resource-constrained environments.

However, PRIME's implementation also faces some challenges:

• Computational Complexity: The ILP solver's complexity may introduce computational bottlenecks when dealing with very large models.
• Parameter Sensitivity: The performance of the frequency-weighted objective function is highly sensitive to parameter settings, requiring careful tuning.

Developer Implementation and Deployment Recommendations

For developers looking to adopt PRIME, the following recommendations may be helpful:

1. Model Selection: It is advisable to test PRIME on medium-sized MoE models first to evaluate its actual performance and computational overhead.
2. Parameter Tuning: Fine-tune the parameters of the frequency-weighted objective function according to the specific task requirements to achieve optimal performance.
3. Resource Planning: Given the complexity of ILP solving, it is recommended to deploy PRIME in an environment with sufficient computational resources.

Conclusion

The PRIME framework provides an innovative solution for MoE model compression, significantly improving the model's inference efficiency and memory utilization by unifying the pruning and merging processes. Despite some challenges in computational complexity and parameter sensitivity, its excellent performance in multiple benchmarks demonstrates its great potential in practical applications.

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Source: ArXiv Machine Learning (cs.LG) (2026-10-14)

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Tags: #MoE Architecture Follow #Model Compression Follow #arXiv Follow #PRIME Follow #Inference Optimization Follow