Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead

Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

Abstract: Fine-tuning large language models (LLMs) with low-rank adaptations (LoRAs) has become common practice, often yielding numerous copies of the same LLM differing only in their LoRA updates. This paradigm presents challenges for systems that serve real-time responses to queries that each involve a different LoRA. Prior works optimize the design of such systems but still require continuous loading and offloading of LoRAs, as it is infeasible to store thousands of LoRAs in GPU memory. To mitigate this issue, we investigate the efficacy of compression when serving LoRAs. We propose a method for the joint compression of LoRAs into a shared basis paired with LoRA-specific scaling matrices. We extend our algorithm to learn clusters of LoRAs that are amenable to joint compression, allowing it to scale gracefully to large LoRA collections. Our experiments with up to 1000 LoRAs demonstrate that compressed LoRAs preserve performance while offering major throughput gains in realistic serving scenarios with over a thousand LoRAs, maintaining 80\% of the throughput of serving a single LoRA.

Lay Summary: Large language models can be fine-tuned for many tasks by attaching thousands of small adapters, but servers slow down as they frequently swap these adapters in and out of limited memory. By condensing the adapters’ overlapping information into a single shared core and preserving only necessary task-specific fragments, our method allows one server to efficiently deliver thousands of customized models at nearly the speed and cost of serving just one.

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Primary Area: Deep Learning->Large Language Models

Keywords: LoRA, PEFT, Serving LoRAs

Submission Number: 4764