I require 64GB to fit an entire dataset in memory for deep learning, but have only 12GB RAM. Virtual memory being the next-best alternative, I learned it can be effectively increased via increasing the pagefile size - but [this source](https://www.computerhope.com/issues/ch001293.htm) suggests it'd increase system instability.

All other sources state to the contrary, only noting lowered SSD lifespan, which isn't a problem - but I rather not take chances; this said, is there a limit to how much pagefile size can be increased without yielding instability? 

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**Additional info**: Win10 OS, 26GB OS-allocated pagefile size (need 52GB + c, c = safe minimum) 

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**PRE-ANSWER**: proceeded as described [here](https://superuser.com/questions/1479006/does-using-ssd-as-ram-burn-out-ssd-2019), with ~70GB [memory-mapped](https://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html) data; the average data load speedup is **42-FOLD**. I suspect this figure may be bumped to *~130*, though won't work on it now unless someone answers [this](https://superuser.com/questions/1481251/how-to-store-data-in-pagefile). Lastly, this *is* sustainable and [won't degrade](https://superuser.com/questions/1479006/does-using-ssd-as-ram-burn-out-ssd-2019) the SSD, as the use is 99.9%+ reads. Will post full answer with details eventually.