Benchmarking an infrastructure before using it for LLM tasks, such as training and inference, is crucial for several reasons. First, it helps understand the infrastructure's capacity to handle the computational demands of these tasks. Many AI workloads, such as generative AI or LLM tasks, require significant computational resources, and benchmarking can provide insights into whether the current infrastructure can meet these demands. Second, it allows for the optimization of resource allocation, ensuring efficient and cost-effective use of the infrastructure. Third, benchmarking can identify potential bottlenecks that may hinder the AI task performance. By addressing these issues early, you can ensure smooth and efficient operation. Lastly, benchmarking provides a baseline for measuring future upgrades or changes to the infrastructure, aiding continuous improvement efforts. Therefore, benchmarking is a vital step in preparing an infrastructure.
Four specific applications were tested on the proposed solution: MLPerf Training v4.0 and Inference v4.1 and NVIDIA HPC-Benchmarks 24.06 for HPL, HPL-AI, and HPCG.