High-Performance Linpack (HPL) is a memory-intensive benchmark that allocates a large portion of available GPU memory to store the matrix $N$. While a server may have 2TB of physical system RAM, the "not enough memory" error usually refers to theHBM (High Bandwidth Memory)on the GPUs themselves. In a DGX H100 system, each GPU has 80GB of HBM3. If the problem size ($N$) specified in the HPL.dat file is too large, the required memory for the matrix will exceed the aggregate capacity of the GPU memory. Reducing the problem size ($N$) while maintaining the optimal block size ($NB$) ensures that the problem fits within the GPU memory limits while still pushing the computational units to their peak performance. Increasing the block size (Option C) would actually increase the memory footprint of certain internal buffers, potentially worsening the issue. Reducing $N$ is the standard procedure to stabilize the run during the initial tuning phase of an AI cluster bring-up.