64GB helps, but you should probably fix your approach first.

Yes, 64GB will let you load that 20GB CSV into pandas without choking. With 32GB you’re gambling — pandas memory overhead can easily exceed 2x the file size. So 64GB gives you breathing room.

But honestly? Loading 20GB into RAM is the wrong move. pandas is memory-hungry, and you’ll be fighting your machine every time you run it. Tools like Dask, Polars, or even pd.read_csv with chunksize iterate over the data without loading everything. You’ll get the job done on 32GB (or less) and your computer won’t sound like a jet engine.

If your workflow genuinely requires random access to the full dataset in memory — like iterative joins or complex groupbys across the whole thing — then 64GB starts to make sense. Otherwise, save the money and optimize your pipeline. Your computer (and your patience) will thank you.