If I understand the paper right...

At indexing time:

- run LLM over every data point multiple times ("gleanings") for entity extraction and constructing a graph index

- run an LLM over the graph multiple times to create clusters ("communities")

At query time:

- Run the LLM across all clusters, creating an answer from each and score them

- Run the LLM across all but the lowest scoring answers to produce a "global answer"

...aren't the compute requirements here untenable for any decent sized dataset?