Serving
LoRA serving applies low-rank adapter updates to a shared base model during inference. Multiple adapters can reuse the base weights while adding adapter-specific matrix work.
Adapter reuse avoids loading a full model copy for every specialization. Adapter memory, switching, batching compatibility, and added operations still affect capacity and latency.
Servers load or cache adapters, verify their base-model compatibility, and route requests to the correct identity. Operators measure cache misses, adapter churn, and mixed-adapter batch efficiency.
LoRA serving applies low-rank adapter updates to a shared base model during inference. Multiple adapters can reuse the base weights while adding adapter-specific matrix work. Servers load or cache adapters, verify their base-model compatibility, and route requests to the correct identity. Operators measure cache misses, adapter churn, and mixed-adapter batch efficiency.
Adapter reuse avoids loading a full model copy for every specialization. Adapter memory, switching, batching compatibility, and added operations still affect capacity and latency.
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