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  • NVIDIA DGX Cloud Benchmarking

    NVIDIA DGX? Cloud Benchmarking gauges training and inference performance across AI workloads and platforms, accounting for chips, cloud platforms, and application configurations.

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    How NVIDIA DGX Cloud Benchmarking Works

    NVIDIA DGX Cloud Benchmarking analyzes real-time metrics across GPU configurations and environments, with ready-to-use benchmarking templates and on-demand benchmarking for custom workloads. Baseline performance results for comparison are provided via interactive dashboards.

    Specifications:

     - Scaling analysis from 8 to 2,048 GPUs
     - Precision comparisons: FP8 versus BF16
     - Support for popular AI frameworks and models
     - Performance data across NVIDIA NeMo? framework versions

    A chart showing DGX Cloud Benchmarking results

    Example of improved metrics gained over time derived from NVIDIA DGX Cloud Benchmarking results.

    Introductory Blog

    NVIDIA DGX Cloud introduces ready-to-use templates and recipes to benchmark AI platform performance.

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    Benchmark Recipes

    The Benchmarking Collection provides an easy path to reproduce the latest performance results for deep learning workloads.

    View Recipes

    Explainer Blog

    Discover how NVIDIA DGX Cloud Benchmarking accurately measures performance in real-world environments and identifies optimization opportunities in AI training and inference workloads.

    Read Blog

    Get Started With NVIDIA DGX Cloud Benchmarking

    Benchmark AI Workloads

    Understand end-to-end platform performance and learn best practices for cluster sizing and workload optimization with NVIDIA DGX Cloud Benchmarking.

    Benchmarking Recipes

    Deploy ready-to-use benchmarking templates across any cloud platform. Leverage NVIDIA’s performance baselines to compare expected performance across precisions and scales.


    NVIDIA DGX Cloud Benchmarking Starter Kits

    See how well your own environment performs: log into NGC; select a model; download the container, benchmarking recipes, and dataset scripts; and launch to obtain throughput results.

    Benchmark Recipe for Meta Llama 3.1 70B

    Understand end-to-end platform performance and learn best practices for cluster sizing and workload optimization with DGX Cloud Benchmarking.

    Benchmark Recipe for NVIDIA NeMo Megatron

    This recipe contains information and scripts to produce training performance results of the NVIDIA NeMo Megatron workload.

    Benchmark Recipe for xAI Grok-1 314B

    This recipe contains information and scripts to produce performance results for the Grok1 314B training workload.


    NVIDIA DGX Cloud Benchmarking Learning Library

    Techblog

    NVIDIA-Optimized Code for Popular LLMs

    NVIDIA AI Foundation Models and Endpoints

    Learn tips to generate code, answer queries, and translate text on Llama, Kosmos-2, and SeamlessM4T with NVIDIA AI Foundation Models.

    Tutorial

    How to Deploy a NIM in 5 Minutes

    NVIDIA NIM

    NVIDIA NIM? is a set of easy-to-use inference microservices for accelerating the deployment of foundation models on any cloud or data center.

    Model

    Try NVIDIA NIM APIs

    NVIDIA Build

    Explore leading open models built by the community, optimized and accelerated by NVIDIA’s enterprise-ready inference runtime.

    Video

    NVIDIA DGX Cloud Create

    NVIDIA DGX Cloud

    NVIDIA DGX Cloud Create is a high-performance, fully managed AI training platform that provides optimized accelerated computing clusters on any leading cloud and access to NVIDIA experts.

    Techblog

    NVIDIA DGX Cloud Serverless Inference

    NVIDIA DGX Cloud

    NVIDIA DGX Cloud Serverless Inference simplifies deploying AI workloads across multiple regions with seamless auto-scaling, load balancing, and event-driven execution.


    More Resources

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    NVIDIA DGX Cloud Benchmarking FAQ

    Get Training and Certification

    Join the NVIDIA Developer Program

    Ethical AI

    NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

    For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

    Get started with DGX Cloud Benchmarking today

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