Graph　neural　networks　(GNN)　have　achieved　state-of-the-art　performance　on　various　industrial　tasks.　However,　the　poor　efficiency　of　GNN　inference　and　frequent　Out-Of-Memory　(OOM)　problem　limit　the　successful　application　of　GNN　on　edge　computing　platforms.　To　tackle　these　problems,　a　feature　decomposition　approach　is　proposed　for　memory　efficiency　optimization　of　GNN　inference.　The　proposed　approach　could　achieve　outstanding　optimization　on　various　GNN　models,　covering　a　wide　range　of　datasets,　which　speeds　up　the　inference　by　up　to　3x.　Furthermore,　the　proposed　feature　decomposition　could　significantly　reduce　the　peak　memory　usage　(up　to　5x　in　memory　efficiency　improvement)　and　mitigate　OOM　problems　during　GNN　inference.