Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quality adapter often requires systematic hyperparameter tuning because LoRA performance is highly sensitive to configuration choices. In practice, this leads to many concurrent LoRA jobs, often spanning heterogeneous tasks in multi-tenant environments. Existing systems largely handle these jobs independently, which both wastes computation on weak candidates and leaves GPUs underutilized. We present ALTO (Adaptive LoRA Tuning and Orchestration), a co-designed training system that accelerates LoRA hyperparameter tuning while enabling efficient cluster sharing across heterogeneous tasks. The central insight behind ALTO is that when multiple tuning jobs run concurrently over a shared frozen backbone, they expose optimization opportunities that single-job designs cannot exploit. Building on this, ALTO monitors loss trajectories to terminate unpromising configurations early, uses fused grouped GEMM together with a new rank-local adapter parallelism to co-locate surviving adapters and reclaim freed GPU capacity, and combines intra-task and inter-task scheduling to improve multi-task placement by leveraging the predictable duration of LoRA jobs. Extensive evaluation shows that ALTO achieves up to $13.8\times$ speedup over state-of-the-art without sacrificing adapter quality.

翻译：低秩适配（LoRA）已成为大语言模型参数高效微调的主流方法，但由于LoRA性能对配置选择高度敏感，获得高质量适配器通常需要系统性的超参数调优。在实践中，这导致大量并发LoRA任务在多租户环境中横跨异构任务运行。现有系统大多独立处理这些任务，既浪费了薄弱候选任务的算力，又导致GPU利用率不足。本文提出ALTO（自适应LoRA调优与编排），这是一个协同设计的训练系统，能在加速LoRA超参数调优的同时实现跨异构任务的高效集群共享。ALTO的核心洞察在于：当多个调优任务在共享冻结基座模型上并发运行时，会暴露出单任务设计无法利用的优化机会。基于此，ALTO通过监控损失轨迹提前终止无前景配置，利用融合分组GEMM结合新的秩局部适配器并行机制对存活适配器进行共置并回收释放的GPU算力，同时联合任务内与跨任务调度，通过利用LoRA任务可预测的持续时间来优化多任务部署。大量评估表明，ALTO在不牺牲适配器质量的前提下，相较于现有最优方案实现了高达$13.8\times$的加速比。