I don’t know how you’ve measured the GPU memory usage and also don’t know if you are facing any issues (e.g. if you had to lower the batch size or change the training in any way).
Some expected reasons for a different memory usage:

the CUDA context size is expected to increase due to the additional SMs on A100 compared to V100
besides that, also the libraries increased in size (CUDA, cuDNN etc.)
If you are checking the peak memory, note that cuDNN could pick kernels with a larger workspace on A100, if they are not yielding OOM errors and are giving a speedup (especially if cudnn.benchmark=True is used). You can limit the cuDNN workspace via CUDNN_CONV_WSCAP_DBG