A practitioner's guide to testing and running large GPU clusters for training generative AI models

At Together AI, they have developed a systematic approach to acceptance testing for GPU clusters designed to guarantee reliability and performance for demanding AI/ML workloads. This process involves configuring the cluster's hardware environment, stress testing and benchmarking individual subsystems and components, validating NVLink and NVSwitch communication, testing network configurations, measuring storage performance, running reference tasks tailored to customers' use cases, and continuously monitoring for hardware failures using tools like Telegraf. By adopting this comprehensive approach, companies can navigate the complexities of GPU clusters and ensure their infrastructure is stable and reliable, supporting top-tier computational resources and delivering expected end-to-end performance.