Almost all scientific computing. In spite of considerable effort, there are relatively few workloads that use GPUs well, and even for codes which do work on GPU, you tend to end up with partial functionality, and a very restrictive region of parameter space where they work efficiently. (And often you run out of memory very quickly - scientific workloads typically run multi-node and share RAM across the nodes via Global Arrays or equivalent. Using multiple terabytes of memory is absolutely normal.) The computation in deep learning is actually quite simple (matrix-vector operations), and still it has taken considerable effort to develop efficient kernels (such as cuDNN).

The next UK supercomputer is based around AMD's 64 CPU chips for this reason. One concern is that we may now be limited by the shared memory bandwidth. https://www.nextplatform.com/2019/10/18/amd-cpus-will-power-...