MoE-Mamba: Efficient Selective State Space Models with Mixture of
  Experts
Authors: name: Maciej Pióro, name: Kamil Ciebiera, name: Krystian Król, name: Jan Ludziejewski, name: Sebastian Jaszczur

State Space Models (SSMs) have become serious contenders in the field of
sequential modeling, challenging the dominance of Transformers. At the same
time, Mixture of Experts (MoE) has significantly improved Transformer-based
LLMs, including recent state-of-the-art open-source models. We propose that to
unlock the potential of SSMs for scaling, they should be combined with MoE. We
showcase this on Mamba, a recent SSM-based model that achieves remarkable,
Transformer-like performance. Our model, MoE-Mamba, outperforms both Mamba and
Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba
in 2.2x less training steps while preserving the inference performance gains of
Mamba against the Transformer.