GPU workloads are among the most costly computing resources in today's data centers, with significant challenges arising from the need to manage GPU state during container restarts. Traditional container checkpoint/restore methods like CRIU, while effective for CPU tasks, struggle with the complexity of GPU memory and state management. This complexity includes CUDA runtime state, driver-level state, and container-specific configurations, which are not captured by standard methods. Previous solutions relying on API interception faced performance and compatibility issues. However, the development of CRIUgpu, a 2025 breakthrough, integrates NVIDIA's cuda-checkpoint with CRIU to enable transparent GPU container checkpointing. CRIUgpu avoids API interception, instead creating unified CPU-GPU snapshots without performance overhead, supporting statically linked applications and offering deterministic restore behavior. It captures comprehensive GPU state, including memory contents and contexts, and is integrated into the CRIU project for production use, supported by container runtimes like Podman. Despite its advancements, CRIUgpu has limitations such as lack of UVM support and challenges with multi-node distributed training. Nevertheless, it presents a compelling case for organizations with GPU-intensive workloads, offering efficient utilization and zero-downtime operations, prompting a shift towards integrating this technology into container orchestration pipelines.