Asklemmy

Caching is separate. Honestly, I don't know how to explain it to you without giving you a college level explanation of time complexity.

Think about the most basic way to sort a list of arbitrary size; comparing the first item to the second item, swapping if the second item is smaller, and then repeating until no more swaps occur. This has a time complexity of O(n^2), so on a list of n size, it would be extremely cheap on memory (O(1)) but will take a very long time if n is large.

There's another sorting algorithm called quicksort that is much faster (how it works isn't very important atm), with a time complexity of O(nlog n). This is far faster than the other method of sorting, but it comes at the cost of needing a lot more memory to execute the algorithm, O(log n) of space.

If you scale this simple process to a multithreaded piece of software executing thousands of algorithms per second, like a browser, the size of n scales to the rest of the app. Browsers are generally written in C++, which requires memory to be preallocated. The size of the app includes this memory requirement, as well as the executable.