Applied LLMs
Serving Many LoRA Adapters
How specialised inference systems batch requests across hundreds of distinct LoRA adapters without duplicating the base model weights on GPU.
advanced · 8 min read · Premium
A company fine-tunes Llama-3 70B for 500 enterprise clients, one per customer domain. Each adapter is roughly 50 MB. The base model is 140 GB in BF16. Naively, serving each adapter in its own GPU instance would require 500 x 140 GB of VRAM - an absurd number. The practical question is: how do you share one copy of the base model weights across all 500 adapters while routing each incoming request to the right fine-tuned variant, with throughput close to single-model serving?
That question is what the LoRA serving literature addresses, and the answer is more subtle than it first looks.
Why Naive Batching Breaks
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