Using fewer latent variables in Latte Transformer has implications on both computational efficiency and model capacity. When fewer latent variables are employed, the computational complexity decreases due to reduced interactions between input tokens and latent embeddings. This reduction in complexity may lead to faster inference times and lower memory requirements, making it more feasible to scale up models for longer sequences or deploy them on resource-constrained devices. However, using fewer latent variables might limit the expressive power of the model, potentially affecting its ability to capture intricate relationships within data.