OmniPilot: An LLM Inference Advisor for Optimizing GPU Cluster Configuration Selection
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[Submitted on 2 Jul 2026]
Summary
OmniPilot is an uncertainty-aware LLM inference advisor designed for heterogeneous GPU clusters. It helps users and operators select optimal GPU type, tensor-parallel degree, and precision configurations by predicting serving costs using a conformally calibrated quantile cost model paired with an out-of-distribution (OOD) abstention layer. The system ranks configurations via an economic utility metric calibrated to operator preferences. In evaluations across 460 benchmark runs on A100, H100, and H200 hardware across four precisions, OmniPilot achieves 6.2% MAPE for throughput prediction, 95% top-1 accuracy, and mean utility regret of 0.003. The abstention layer successfully flags unsupported configurations as low-confidence, with plans to integrate OOD scenarios into training to expand the support envelope over time.
Source
bskyOmniPilot: An LLM Inference Advisor for Optimizing GPU Cluster Configuration Selectionarxiv.orgKey quotes
· 5 pulledOmniPilot pairs a conformally calibrated quantile cost model (spanning eight serving targets) with an out-of-distribution (OOD) abstention layer.
In evaluations across 460 benchmark runs on A100, H100, and H200 hardware across four precisions, OmniPilot predicts aggregate throughput with a 6.2% mean absolute percentage error (MAPE) and a log-space R²=0.92.
The advisor achieves 95% top-1 accuracy with a mean utility regret of just 0.003.
When tested on an OOD holdout of unsupported cells, prediction error climbs to 24-46% and conformal intervals cover 0 of 5 points; however, the abstention layer successfully flags all five as low-confidence.
Over time, these OOD scenarios will be integrated into the training dataset to continuously expand the advisor's support envelope.
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