Evaluating an AI System Holistically
In the rapidly evolving landscape of AI systems and workloads, achieving optimal model training performance extends far beyond chip speed. It requires a comprehensive evaluation of the entire stack, from compute to networking to model framework.
Tuning Workloads to Optimal Performance
Beyond the job execution aspect of benchmarking, NVIDIA DGX Cloud Benchmarking Recipes are also playbooks for optimizing popular models and workloads. These recipes provide workload-specific strategies to maximize performance for popular models such as Llama 3.1, Grok, and Mixtral.
Using FP8
DGX Cloud Benchmarking Recipes provide optimized configurations and tuning recommendations specifically for FP8 workloads, helping you achieve optimal performance with this precision format. For example, the recipe for Llama 3.1 70B training includes FP8 settings that have been carefully tested and optimized for DGX Cloud platforms.
Get Started with DGX Cloud Benchmarking Recipes
The recipes for benchmarking platform performance are hosted in NVIDIA’s public registry, NGC Catalog. For more information about the latest release of recipes, see DGX Cloud Benchmarking 24.11.1.
Keep Moving the Platform Performance Goalposts
In today’s AI landscape, achieving optimal performance requires looking beyond individual components to understand how entire systems work together. While raw GPU capabilities matter, full optimization comes from carefully tuning every layer of the stack, from hardware and software configuration to workload-specific parameters.
Conclusion
Training large models can take weeks or months and cost millions in compute resources, so modest performance improvements can translate into substantial time and cost savings. By using continually evolving performance optimizations and workload-specific recipes from NVIDIA, your organization can maximize AI infrastructure investments and focus engineering efforts on innovation rather than infrastructure tuning.
FAQs
Q: What are DGX Cloud Benchmarking Recipes?
A: DGX Cloud Benchmarking Recipes are end-to-end benchmarking suites that measure performance in real-world environments and identify optimization opportunities in AI training workloads.
Q: What are the benefits of using DGX Cloud Benchmarking Recipes?
A: By using DGX Cloud Benchmarking Recipes, you can optimize AI workloads for specific environments, assess how close a cluster’s performance is to NVIDIA’s observed performance, and identify performance bottlenecks in your current setups.
Q: Can I use DGX Cloud Benchmarking Recipes for FP8 workloads?
A: Yes, DGX Cloud Benchmarking Recipes provide optimized configurations and tuning recommendations specifically for FP8 workloads, helping you achieve optimal performance with this precision format.
Q: How can I get started with DGX Cloud Benchmarking Recipes?
A: The recipes for benchmarking platform performance are hosted in NVIDIA’s public registry, NGC Catalog. For more information about the latest release of recipes, see DGX Cloud Benchmarking 24.11.1.
