That said, I want to caution against using topic modeling as a one-fits-all-solution. As the author stresses, this is one particular approach which uses a combination of embeddings (sentence, or other), umap and hdbscan. Both umap and hdbscan can be slow, so it might be worthwhile to check out the GPU enabled versions of both from the cuml package.
In addition, topic models have a huge number of degrees of freedom, and the solution you will get depends on many (seemingly arbitrary) choices. In other words, these are not the topics, they are some topics.
That said, it's awesome, really great work by Maarten Grootendorst and a great blog post by James Briggs.
[edit] here is a link to the fast cuda version of bertopic by rapidsai: https://github.com/rapidsai/rapids-examples/tree/main/cuBERT...