Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving

A team of researchers including Ferran Agullo, Joan Oliveras, and Jordi Torres has published a paper introducing a novel data-driven optimization pipeline for distributed LLM adapter serving. The core problem addressed is the inefficient use of GPU resources when hosting hundreds of specialized adapters—small, fine-tuned modules that customize large base models like Llama 3 or GPT-4. While previous research focused on minimizing latency, this work prioritizes maximizing throughput to improve resource efficiency. The proposed solution aims to compute the optimal placement of adapters across a GPU cluster to serve a given workload with the minimum number of GPUs, while avoiding request starvation and memory errors.

The pipeline integrates three key components: a high-fidelity Digital Twin (DT) that emulates real serving dynamics, a distilled machine learning model trained on DT-generated data for fast performance prediction, and a greedy placement algorithm that uses these predictions. The DT achieves below 5% throughput estimation error while running up to 90 times faster than full LLM benchmarking. This enables scalable optimization, substantially reducing the GPU footprint required for target workloads. Beyond GPU efficiency, the authors note the framework's versatility for other objectives like latency minimization, positioning it as a critical tool for future large-scale, cost-effective AI infrastructure where adapter-based specialization is prevalent.