We’ve been noticing this pretty consistently during longer inference sessions lately: the differences between deployments of the same model are starting to become obvious enough that users can often tell them apart without knowing which backend they’re hitting. Not on short prompts or benchmark-style tasks. Those usually look almost identical. The differences start showing up after sustained usage - long coding sessions, iterative agent workflows, large-context chats, repeated tool calls, etc. Some deployments stay coherent much longer before repetition starts creeping in. Some handle crowded context windows better. Others feel faster initially but become less stable once queue pressure increases. We’ve even seen formatting and reasoning consistency drift noticeably between different serving setups using the same base weights. At this point it feels misleading to talk about many frontier/open models as if the weights alone define the experience. The serving layer is starting to shape behavior almost as much as the model itself. submitted by /u/qubridInc
Originally posted by u/qubridInc on r/ArtificialInteligence