In the context of sorting algorithms, a 'large input' is typically defined by the size of the input data in relation to the time complexity of the sorting algorithm. A large input in the case of bubble sort is one for which the time complexity of O(n2) becomes impractical, resulting in long processing times.

As the performance of bubble sort is highly dependent on the specific implementation and hardware used, there is no specific breakpoint in terms of MB/GB/TB of input data. However, as the size of the input data grows larger, the time complexity of bubble sort becomes more problematic, making it inefficient for larger inputs. As a result, bubble sort is generally regarded as inefficient for large inputs, whereas merge sort is regarded as more efficient due to its time complexity of O. (n log n). The precise point at which bubble sort becomes inefficient depends on the specific implementation and hardware, but it is generally agreed that bubble sort becomes inefficient for input sizes greater than several thousand elements.
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