Can we identify an LLM interlocutor with a single hardware instance?

Chalmers considers and rejects the view that the LLM interlocutor is the hardware instance — the particular GPU or server running the model at a given moment. Two empirical facts about contemporary inference infrastructure make this untenable.

First, distributed serving: a single conversation may be processed across multiple hardware instances sequentially or in parallel. Load-balancing, model-parallelism, and failover mean that the conversation's compute migrates across physical substrate during a single session. If the interlocutor were the hardware, it would change identity mid-conversation — a consequence no one wants.

Second, multi-tenancy: a single hardware instance typically hosts many conversations simultaneously. The same GPU processes tokens for many users within the same batch. If the interlocutor were the hardware, multiple users would share a single interlocutor — another consequence no one wants.

Together, these facts eliminate hardware as the individuation level. What remains as a candidate must be something whose identity is invariant under changes in physical substrate and under concurrent use of that substrate — which is what leads Chalmers to the virtual instance and thread levels. The negative argument is clean and hard to contest; anyone who wants to ground the interlocutor in physical substrate has to explain how identity is maintained through load-balancing and how distinctness is maintained through batching.

Source: What We Talk To When We Talk To Language Models (David J. Chalmers)