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Well there are 2 things to consider

• While increasing precalcValBufSize and precalcIdxBufSize work in the lower ranges (for example changing 1024MB to 2048MB), it does not when increasing it alot because the process just does not need that amount of memory.
• For now this means that given your training data, all possible features (the feature pool), can be stored in memory for the boosting process by using 7.5 GB of memory. Increase either the number of samples, or the dimensions of the model (which increases the amount of possible features per image) and you will see it increasing.

Well there are 2 things to consider

• While increasing precalcValBufSize and precalcIdxBufSize work in the lower ranges (for example changing 1024MB to 2048MB), it does not when increasing it alot in the upper regions (like 5-6GB) because the process just does not need that amount of memory.
• For now this means that given your training data, all possible features (the feature pool), can be stored in memory for the boosting process by using 7.5 GB of memory. Increase either the number of samples, or the dimensions of the model (which increases the amount of possible features per image) and you will see it increasing.