It massively helps arithmetic intensity to batch during inference, and the desired batch sizes by that tend to exceed the memory capacity of a single GPU. Thus desire to do training-like cluster processing to e.g. use a weight for each inference stream that needs it every time it's fetched from memory. It's just that you can't fit 100+ inference streams of context on one GPU, typically, thus the desire to shard along less-wasteful (w.r.t. memory bandwidth) dimensions than entire inference streams.