A practitioner's guide to testing and running GPU clusters

The article discusses the importance of testing and running large GPU clusters for training generative AI models, emphasizing the need for high-performance hardware like H100 GPUs and efficient storage systems. It highlights the challenges faced by companies due to potential misconfigurations and component failures in these complex systems. To address these issues, Together AI has developed a systematic acceptance testing process to ensure the reliability and performance of GPU clusters before deployment. This process includes hierarchical testing, starting from basic functionality to more complex performance evaluations. Key steps involve preparing and configuring the hardware, validating GPUs through stress tests, and ensuring effective communication between GPUs using NVLink and NVSwitch. Additionally, network and storage performance are validated to optimize the overall efficiency of the clusters. The article underscores the necessity of rigorous testing to maintain customer trust and satisfaction, especially as the demand for AI capabilities continues to grow.

- Acceptance testing is crucial for ensuring the reliability of GPU clusters used in AI model training.

- The testing process includes hierarchical evaluations, starting from basic functionality to complex performance checks.

- Validation of GPU communication and network performance is essential for effective distributed training.

- Storage performance is also a critical factor in the overall efficiency of machine learning workloads.

- Together AI's systematic approach aims to mitigate risks associated with hardware failures and misconfigurations.