Disaggregated Memory

Andrew explains how disaggregating memory from compute in GPUs addresses challenges of fixed memory sizes, enabling support for large models without unnecessary components. By separating parameter storage from compute, users can customize memory-compute ratios, enhancing flexibility and efficiency in running large neural networks.