SlimMoE: Structured Compression of Large MoE Models via Expert Slimming
  and Distillation
Authors: name: Zichong Li; name: Chen Liang; name: Zixuan Zhang; name: Ilgee Hong; name: Young Jin Kim; name: Weizhu Chen; name: Tuo Zhao
Published: 2025-06-23 07:15:59

Abstract: The Mixture of Experts (MoE) architecture has emerged as a powerful paradigm
for scaling large language models (LLMs) while maintaining inference
efficiency. However, their enormous memory requirements make them prohibitively
expensive to fine-tune or deploy in resource-constrained environments. To
address this challenge, we introduce SlimMoE, a multi-stage compression
framework for transforming large MoE models into much smaller, efficient
variants without incurring the prohibitive costs of training from scratch. Our
method systematically reduces parameter counts by slimming experts and
transferring knowledge through intermediate stages, effectively mitigating the
performance degradation common in one-shot pruning approaches. Using this
framework, we compress Phi 3.5-MoE (41.9B total/6.6B activated parameters) to
create Phi-mini-MoE (7.6B total/2.4B activated parameters) and Phi-tiny-MoE
(3.8B total/1.1B activated parameters) using only 400B tokens--less than 10% of
the original model's training data. These compressed models can be fine-tuned
on a single GPU (A100 for Phi-mini-MoE, A6000 for Phi-tiny-MoE), making them
highly suitable for academic and resource-limited settings. Our experiments
demonstrate that these compressed models outperform others of similar size and
remain competitive with larger models. For instance, Phi-mini-MoE achieves
similar or better performance to Phi-3-mini using only 2/3 of the activated
parameters and yields comparable MMLU scores to Llama 3.1 8B despite having
significantly lower latency. Our findings demonstrate that structured pruning
combined with staged distillation offers an effective path to creating
high-quality, compact MoE models, paving the way for broader adoption of MoE
architectures. We make our models publicly available at
https://huggingface.co/microsoft/Phi-mini-MoE-instruct and
https://huggingface.co/microsoft/Phi-tiny-MoE-instruct .