As the scale of graph analytics continues to grow, many applications require identifying biconnected components (bccs) and cut vertices in graphs that exceed the memory capacity of a single gpu. This paper presents an out-of-core, gpu-based batch processing algorithm designed to efficiently compute bccs and cut vertices in massive graphs that do not fit entirely into device memory. We propose a novel batch technique to process the graph incrementally, and maintain a Biconnectivity Compressed Graph to compute bccs and cut vertices. Experimental results on a range of large-scale benchmark graphs demonstrate that our technique achieves competitive performance compared to state-of-the-art cpu solutions, enabling the handling of graph instances previously considered intractable on gpu platforms.