ATTUNE: Agentic Throughput Tuning for LLM Training

Abstract

Training a large language model can occupy thousands of GPUs for weeks, and its speed depends on its recipe: how the model is split across GPUs, how memory is traded for compute, and which kernels and communication overlaps it uses. Finding a good recipe is still manual work for experts. Automated tuners search fixed lists of knobs and check only that a job runs, so they miss large gains and can ship changes that silently alter what the model learns. ATTUNE tunes any working recipe unattended within a GPU-hour budget. A coding agent, guided by lessons from past tuning runs, finds each run's bottleneck and changes one setting at a time, and a correctness gate outside the agent refuses changes known to alter training. ATTUNE is deployed in production at Amazon Ads, a business that reported $19.8 billion in revenue in Q2 2026. Its recipes train the models that power Amazon's Ads Agent. On three of the models it tunes, its recipes raise throughput per GPU by 1.7x on average and up to 3.9x over internal expert-tuned recipes. It also speeds up four of NVIDIA's benchmark recipes by 7-26% and held-out public recipes by up to 2.6x, and beats Bayesian optimization, random search and OptiCo, a recent LLM-agent tuner, at the same GPU-hour budget. We release ATTUNE at github.com/amazon-science/attune and OpenRecipes, a leaderboard of more than 60 verified recipes, at openrecipes.ai.