Toward Efficient Deployment of Mixture of Experts Models: Quantization and Compression Analysis

Recent mixture of experts (MoE)-based transformer models have achieved remarkable performance across various applications. However, as model size increases, the associated memory demand escalates dramatically, limiting their deployment in resource-constrained edge environments. To address this challenge, this study investigates the trade-off between performance and computational overhead under extreme compression conditions by applying ternary quantization and additional compression schemes to MoE models. Experimental results demonstrate that Huffman coding achieves a higher compression ratio but suffers from substantial decoding latency, while dictionary coding provides a lower compression ratio with significantly faster decoding. These findings highlight that efficient deployment of MoE models requires an integrated compression framework that jointly optimizes compression ratio and overhead. Furthermore, based on these observations, potential research directions are suggested, including hybrid coding formats and look-up table-based dedicated hardware accelerators for efficient MoE model compression.