For large models (~13K paramemeters) the procedure ```r library(magrittr) temp_rds_file <- tempfile(fileext = ".rds") cmdstanr_fit$save_object(file = temp_rds_file) standalone_fit <- readRDS(temp_rds_file) ``` Takes an incredible amount of memory. When loaded into memory, the `cmdstanr_fit` is around ~8Gb, and the operations pick at 54Gb. This could be solved by handling ".csv" draws on-disk using DuckDB technology (toggling it with a parameter?) Here an example of csv handling by DuckDB. https://stackoverflow.com/questions/77797976/working-with-large-csv-file-using-duckdb-or-arrow-in-r