The emerging 3D Gaussian Splatting has demonstrated high-quality scene modeling and novel view synthesis. However, the efficiency is constrained by per-scene optimization on densely captured images and feedforward generalizable reconstruction for large indoor scenes continues to pose a significant challenge. In this paper, we propose G \(^2\) S-Indoor, a novel framework that is capable of reconstructing large-scale indoor scenes through fast inference. We represent the scene as 3D Gaussians with generalizable structured feature volume trained across large-scale data, achieving photorealistic rendering and rapid reconstruction. Specifically, we first process the input frames to reconstruct the local feature volumes and decode the properties of 3D Gaussians from features via direct inference. To mitigate the loss of details due to the limited resolution of voxels, we develop an error-based voxel subdivision strategy, which effectively captures varying levels of detail with newly generated Gaussians. Finally, we fuse the local Gaussian feature volumes with a recurrent volume-based fusion scheme to incrementally reconstruct the global Gaussian-splatting scene representation. Our method demonstrates state-of-the-art rendering quality on the ScanNet dataset while supporting generalizable large-scale indoor scene reconstruction. The zero-shot inference on the Replica and ScanNet++ datasets further demonstrates our cross-dataset generalization ability.