# SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation

arXiv: https://arxiv.org/abs/2402.06194 · HTML: https://mlsanity.com/paper/2402.06194/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2024.

## Built on by (most cited first, 11 of 11)

- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [Robust LLM Training Infrastructure at ByteDance](https://mlsanity.com/paper/2509.16293/) (2025)
- [Minder: Faulty Machine Detection for Large-scale Distributed Model Training](https://mlsanity.com/paper/2411.01791/) (2024)
- [Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training](https://mlsanity.com/paper/2509.03018/) (2025)
- [SIGMA: An AI-Empowered Training Stack on Early-Life Hardware](https://mlsanity.com/paper/2512.13488/) (2025)
- [Efficient AllReduce with Stragglers](https://mlsanity.com/paper/2505.23523/) (2025)
- [Guard: Scalable Straggler Detection and Node Health Management for Large-Scale Training](https://mlsanity.com/paper/2605.17879/) (2026)
- [EROICA: Online Performance Troubleshooting for Large-scale Model Training](https://mlsanity.com/paper/2506.08528/) (2025)
- [BootSeer: Analyzing and Mitigating Initialization Bottlenecks in Large-Scale LLM Training](https://mlsanity.com/paper/2507.12619/) (2025)
- [Co-Design of Memory-Storage Systems for Workload Awareness with Interpretable Models](https://mlsanity.com/paper/2603.15571/) (2026)
- [CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training](https://mlsanity.com/paper/2605.04478/) (2026)

## Abstract (arXiv)

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called "gray failure", for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cause analysis for failures and regressions. We introduce SuperBench, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability. SuperBench features a comprehensive benchmark suite, capable of evaluating individual hardware components and representing most real AI workloads. It comprises a Validator which learns benchmark criteria to clearly pinpoint defective components. Additionally, SuperBench incorporates a Selector to balance validation time and issue-related penalties, enabling optimal timing for validation execution with a tailored subset of benchmarks. Through testbed evaluation and simulation, we demonstrate that SuperBench can increase the mean time between incidents by up to 22.61x. SuperBench has been successfully deployed in Azure production, validating hundreds of thousands of GPUs over the last two years.