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ALTO is a co-designed training system that accelerates LoRA hyperparameter tuning and improves cluster sharing across heterogeneous tasks. It exploits optimization opportunities arising from concurrent tuning jobs over a shared frozen backbone, using loss trajectory monitoring for early termination of unpromising configurations. ALTO achieves up to 13.8x speedup over state-of-the-art without sacrificing adapter quality by using fused grouped GEMM, rank-local adapter parallelism, and combined intra-task and inter-task scheduling.
Forget independent LoRA tuning jobs: ALTO co-optimizes them for a 13.8x speedup without sacrificing adapter quality.
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.
