For ML/neural networks, the vector/matrix/tensor acceleration is still valuable. Thus, running them on GPUs or specialist hardware will make them faster to comp

For ML/neural networks, the vector/matrix/tensor acceleration is still valuable. Thus, running them on GPUs or specialist hardware will make them faster to complete -- such as generating images from stable diffusion. GPUs are also currently best suited to this due to being able to parallelize the calculations across the CUDA and specialist tensor cores. The other issue is the memory needed to run the models. NVidia's NVLink is useful for this to share memory in a combined space across the GPUs.