Get the 64GB.

Yes, for a 10GB CSV and in‑memory pandas workflows, 64GB is significantly better—mostly because 32GB likely won’t cut it.

A 10GB CSV in pandas can easily balloon to 25–40GB of RAM once you load it (object columns, copies, temporary operations). With 32GB, you’re either swapping to disk or hitting out‑of‑memory errors before you even start exploring. Chunking or Dask can help, but if your workflow is truly “in‑memory,” 32GB is a bottleneck, not enough.

The practical difference: 64GB lets you load the full dataset, transform it, and maybe run a few models without dancing around memory limits. 32GB forces you to micro‑optimize, chunk, or sample constantly. That time adds up fast. If your datasets are only going to get bigger, 64GB is the safe bet. If they stay under 5GB, 32GB is fine. But for 10GB CSVs? Don’t cheap out.

Future You will thank you for not fighting pandas memory errors at 11 PM.