Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs).Compressing the whole input sequence in a finite-dimensional representation enables recurrent layers to model long-range dependencies while maintaining a constant inference cost for each token and a fixed memory requirement.However, the practical deployment of LLMs in resource-limited environments often requires further model compression, such as quantization and pruning.While these techniques are well-established for attention-based models, their effects on recurrent layers remain underexplored.In this preliminary work, we focus on post-training quantization for recurrent LLMs and show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs.We show that the reason for difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs.We report baseline results for post-training quantization of Mamba that do not take into account the activation outliers and suggest first steps for outlier-aware quantization.