VISA's scalability for models with a large number of latent variables is impacted by the use of sequential sample-average approximations in several ways. Firstly, as the number of latent variables increases, maintaining a fixed set of samples for each SAA iteration may become computationally intensive. The need to refresh samples based on ESS thresholds can lead to increased computational overhead, especially when dealing with high-dimensional spaces where evaluating the model is already resource-intensive. Additionally, larger latent variable spaces may require more frequent updates to the proposal distribution parameters and trust regions, further adding to the computational complexity.