Real-time 3D reconstruction is becoming a key enabler for robotics, mixed reality, and autonomous vehicles. Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-fidelity volumetric modeling, and their integration with SLAM shows strong potential for real-time deployment. How ever, the substantial size of 3DGS models makes them difficult to deploy on heterogeneous devices, while their rendering quality remains highly sensitive to tracking accuracy under motion blur and abrupt texture variations. In this work, we propose LoD GS, a lightweight and robust 3DGS-SLAM framework that produces compact yet high-fidelity Gaussian scene representa tions for flexible deployment. LoD-GS integrates entropy-driven scene-complete volumetric mapping to improve pose quality and Gaussian initialization, a geometry-aware rendering quality optimizer that emphasizes near-field and structure-rich regions under limited optimization budgets, and a deployment-aware level-of-detail 3DGS compression module that enables adaptive resource-quality tradeoffs across heterogeneous devices. Exten sive experiments on public benchmarks and real-world office sequences demonstrate its effectiveness, reducing model size by up to 53.8%, increasing rendering FPS by up to 43.72%, and improving PSNR by up to 2.471 dB.