In　autonomous　driving,　3D　occupancy　prediction　outputs　voxel-wise　status　and　semantic　labels　for　more　comprehensive　understandings　of　3D　scenes　compared　with　traditional　perception　tasks,　such　as　3D　object　detection　and　bird＇s-eye　view　(BEV)　semantic　segmentation.　Recent　researchers　have　extensively　explored　various　aspects　of　this　task,　including　view　transformation　techniques,　ground-truth　label　generation,　and　elaborate　network　design,　aiming　to　achieve　superior　performance.　However,　the　inference　speed,　crucial　for　running　on　an　autonomous　vehicle,　is　neglected.　To　this　end,　a　new　method,　dubbed　FastOcc,　is　proposed.　By　carefully　analyzing　the　network　effect　and　latency　from　four　parts,　including　the　input　image　resolution,　image　backbone,　view　transformation,　and　occupancy　prediction　head,　it　is　found　that　the　occupancy　prediction　head　holds　considerable　potential　for　accelerating　the　model　while　keeping　its　accuracy.　Targeted　at　improving　this　component,　the　time-consuming　3D　convolution　network　is　replaced　with　a　novel　residual-like　architecture,　where　features　are　mainly　digested　by　a　lightweight　2D　BEV　convolution　network　and　compensated　by　integrating　the　3D　voxel　features　interpolated　from　the　original　image　features.　Experiments　on　the　Occ3D-nuScenes　benchmark　demonstrate　that　our　FastOcc　achieves　state-of-the-art　results　with　a　fast　inference　speed.　©　2024　IEEE.