This would depend on your requirements and type of inference. Say you need to compute inference across 1000's of content/documents/images every second or so, out of some corpus of millions-billions, then having a kv store on disk/SSD (NVME) might be for more efficient & cheaper (in terms of grabbing those embeddings to conduct a downstream ML task). How you update the corpus matters too -- a lot of embedding spaces need to be updated in aggregate.