The rapid proliferation of solar energy has significantly expedited the integration of photovoltaic (PV) systems into contemporary power grids. Considering that the cloud dynamics frequently induce rapid fluctuations in solar irradiance, accurate intra-hour forecasting is critical for ensuring grid stability and facilitating effective energy management. To leverage complementary temporal, textual, and visual information, this paper has proposed PV-VLM, a multimodal forecasting framework that integrates temporal, textual, and visual information by three modules. The Time-Aware Module employed a PatchTST-inspired Transformer to capture both local and global dependencies in PV power time series. Meanwhile, the Prompt-Aware Module encodes textual prompts from historical statistics and dataset descriptors via a large language model. Additionally, the Vision-Aware Module utilizes a pretrained vision-language model to extract high-level semantic features from sky images, emphasizing cloud motion and irradiance fluctuations. The proposed PV-VLM is evaluated using data from a 30-kW rooftop array at Stanford University and through a transfer study on PV systems at the University of Wollongong in Australia. Comparative experiments reveal an average RMSE reduction of approximately 5% and a MAE improvement of nearly 6%, while the transfer study shows average RMSE and MAE reductions of about 7% and 9.5%, respectively. Overall, PV-VLM leverages complementary modalities to provide a robust solution for grid scheduling and energy market participation, enhancing the stability and reliability of PV integration.