Why does distributed serving infrastructure defeat hardware-instance accounts of the interlocutor?
This explores why you can't pin down 'who' you're talking to in an LLM by pointing at a physical machine — because the way these models are actually served splits one conversation across many machines and crams many conversations through one.
This explores the idea that when you ask 'which entity am I talking to?', the intuitive answer — *this specific running instance on this specific hardware* — quietly collapses once you look at how models are actually served in production. The corpus's most direct treatment of this is Can we identify an LLM interlocutor with a single hardware instance?, which lays out the mechanics: load-balancing and model-parallelism scatter a single conversation across multiple machines, while batching funnels many separate conversations through a single machine at once. Neither direction preserves a one-to-one mapping. There is no stable 'box' that *is* your interlocutor, so hardware can't be the level at which you individuate it.
The interesting move is what fills the vacuum once hardware is ruled out. What actually specifies a virtual instance in conversation? argues that what specifies a 'virtual instance' is the conversation itself — the jointly produced language between you and the system — not any property of the model or the silicon. Persistence isn't *located* anywhere; it's distributed across the conversational context, the serving infrastructure, and the model weights. So the two notes form a pincer: one tears down the hardware account, the other rebuilds individuation at the level of the dialogue. The thing you're talking to is constituted by the talking.
This turns out to rhyme with a broader pattern in the collection: the meaningful unit of an AI system keeps migrating *up*, away from low-level physical resources. Do persistent agents really cost less per token? makes the parallel economic argument — when context persists and 82.9% of tokens are cache reads, the unit that matters stops being the token and becomes the completed artifact. Same logic, different axis: the naive physical denominator (a machine, a token) stops carrying the identity or the value, and a higher-order, context-defined unit takes over.
What's worth taking away is that the defeat of the hardware account isn't a quirk of cloud deployment you could engineer around — it's structural to how distributed serving works, and it forces a genuinely different answer to 'who am I talking to.' The interlocutor isn't a thing in a server rack you could in principle point at. It's something that exists only across the conversation, and it would dissolve if you tried to find its physical address.
Sources 3 notes
Load-balancing and model-parallelism route single conversations across multiple hardware instances, while batching routes multiple conversations through one instance. These architectural facts break any stable one-to-one mapping, making hardware an untenable level of individuation.
The conversational context—jointly produced language between human and system—specifies the virtual instance, not any property of the model itself. Persistence is distributed across conversation, infrastructure, and model weights rather than located in the AI.
A 115-day case study found 82.9% of tokens were cache reads. When context persists and reuses, the meaningful cost denominator becomes completed artifacts, not individual tokens.