PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
arXiv:2511.04805v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of experts per input. However, their widespread deployment remains limited due to the high memory overhead associated with storing all expert parameters, particularly as the number of experts increases. To address this challenge, prior works have explored expert dropping and merging strategies, yet they often suffer from performance drop at high compression ratios. In this paper, we introduce PuzzleMoE, a
The proliferation of increasingly large language models necessitates more efficient methods for deployment and inference, especially for Mixture-of-Experts architectures.
Efficient compression of MoE models could significantly reduce computational and memory requirements, making advanced AI models more accessible and widely deployable.
The ability to achieve high compression ratios for MoE models with minimal performance degradation changes the economic viability and scalability of these powerful AI architectures.
- · AI developers
- · Cloud providers
- · Edge AI computing
- · AI-dependent industries
- · Hardware manufacturers reliant on high-margin, high-memory components
PuzzleMoE enables more efficient deployment of large MoE models on diverse hardware.
Reduced memory and computational costs could democratize access to advanced AI capabilities.
Widespread deployment of efficient MoE models accelerates the development and adoption of AI agentic systems and other complex AI applications.
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Read at arXiv cs.AI