Regarding metric and dimension - it is really problem dependent as is throughput. Recall and latency numbers reported in benchmarks are typically on very well curated and structured datasets and average across all queries. Recall is not just a function of the HNSW algorithm. I can tell you though you can do 70M vector indexes with 768 dimensions <100ms including inference on very real world datasets. We will publish some benchmarks shortly as we are doing more evaluations on real world data. I also compiled throughput on open CLIP models here as well https://docs.google.com/spreadsheets/d/1ftHKf4MovnAyKhGyi05e.... If there are particular things you want to see let us know and we can add them!