T SNE and UMAP are techniques for dimensionality reduction. Both of them scale pretty well to high dimensionality problems. I think the author downsized the data because he wanted to provide an interactive model. If I were doing this I would probably just leave it as is for either method. The article I linked actually does benchmark on the same dataset though. For the full 70,000 images at 28x28, it claims 87 seconds for UMAP and 1450 for TSNE.