Exemplar Performance

Exemplar Performance

Performance Recipes are ready-to-use templates for evaluating performance of specific AI use cases across hardware and software combinations. These containerized recipes allow users to quickly set up and run standardized benchmarking methodology in their own environment, designed to produce consistent and comparable results across platforms.

These Performance Recipes support performance characterization

• across a variety of defined AI workloads, including pre-training, fine-tuning, and inference.
• across GPU-based infrastructure, whether running on-premises or with cloud service providers (CSPs).

Each recipe maps to one workload and can be run at various cluster scales and precisions. These workloads are tested against NVIDIA Reference Architectures to establish baselines for comparison. These performance metrics are collected from production environments and are subject to real-world variability.

Prerequisites

To use the Performance Recipes, make sure the following prerequisites are available on your cluster:

General Prerequisites

• Bash 4.2 or newer
• Git LFS
• NGC Registry Access
• Python 3.12.x
• CUDA: at least 12.3, recommended 12.8 or newer
• NV Driver: at least 535.129.03, recommended 570.172.08 or newer
• OFED: 5.9-0.5.6.0.127 or newer
• NCCL: 2.19.4 or newer

Cluster-Specific Prerequisites

Depending on your cluster's job scheduler, make sure the following requirements are met:

• Slurm Clusters
  • Version 22.x or newer
  • task/affinity plugin required for process pinning
  • PMIx support required; Slurm must be built with --with-pmix (verify with srun --mpi=list)
  • Enroot 4.0.0 or newer
    • E