MLPerf Benchmarks

The NVIDIA AI platform showcases leading performance and versatility in MLPerf Training, Inference, and HPC for the most demanding, real-world AI workloads.

What Is MLPerf?

MLPerf™ benchmarks—developed by MLCommons, a consortium of AI leaders from academia, research labs, and industry—are designed to provide unbiased evaluations of training and inference performance for hardware, software, and services. They’re all conducted under prescribed conditions. To stay on the cutting edge of industry trends, MLPerf continues to evolve, holding new tests at regular intervals and adding new workloads that represent the state of the art in AI.

Chalmers University is one of the leading research institutions in Sweden, specializing in multiple areas from nanotechnology to climate studies. As we incorporate AI to advance our research endeavors, we find that the MLPerf benchmark provides a transparent apples-to-apples comparison across multiple AI platforms to showcase actual performance in diverse real-world use cases.

— Chalmers University of Technology, Sweden

TSMC is driving the cutting edge of global semiconductor manufacturing, like our latest 5nm node which leads the market in process technology. Innovations like machine learning based lithography and etch modeling dramatically improve our optical proximity correction (OPC) and etch simulation accuracy. To fully realize the potential of machine learning in model training and inference, we are working with the NVIDIA engineering team to port our Maxwell simulation and inverse lithography technology (ILT) engine to GPUs and see very significant speedups. The MLPerf benchmark is an important factor in our decision making.

— Dr. Danping Peng, Director, OPC Department, TSMC, San Jose, CA, USA

Computer vision and imaging are at the core of AI research, driving scientific discovery and readily representing core components of medical care. We've worked closely with NVIDIA to bring innovations like 3DUNet to the healthcare market. Industry-standard MLPerf benchmarks provide relevant performance data to the benefit of IT organizations and developers to get the right solution to accelerate their specific projects and applications.

— Prof. Dr. Klaus Maier-Hein, Head of Medical Image Computing, Deutsches Krebsforschungszentrum (DKFZ, German Cancer Research Center)

As the preeminent leader in research and manufacturing, Samsung uses AI to dramatically boost product performance and manufacturing productivity. Productizing these AI advances requires us to have the best computing platform available. The MLPerf benchmark streamlines our selection process by providing us with an open, direct evaluation method to assess uniformly across platforms.

— Samsung Electronics

Inside the MLPerf Benchmarks

MLPerf Training v3.1 measures the time to train models across nine different use cases, including large language models (LLMs), image generation, computer vision, medical image segmentation, speech recognition, and recommendation.

MLPerf Inference v3.1 measures inference performance using seven different kinds of neural networks, including LLMs, natural language processing, computer vision, and medical image segmentation.

MLPerf HPC v3.0 measures training performance across four different scientific computing use cases, including climate atmospheric river identification, cosmology parameter prediction, quantum molecular modeling, and protein structure prediction. 

Large Language Model (LLM)

Large Language Models

Deep learning algorithms trained on large-scale datasets that can recognize, summarize, translate, predict, and generate content for a breadth of use cases. details.



Generates images from text prompts. details.



Delivers personalized results in user-facing services such as social media or ecommerce websites by understanding interactions between users and service items, like products or ads. details.

Object Detection (Lightweight)

Object Detection (Lightweight)

Finds instances of real-world objects such as faces, bicycles, and buildings in images or videos and specifies a bounding box around each. details.

Object Detection (Heavyweight)

Object Detection (Heavyweight)

Detects distinct objects of interest appearing in an image and identifies a pixel mask for each. details.

Image Classification

Image Classification

Assigns a label from a fixed set of categories to an input image, i.e., applies to computer vision problems. details.

Natural Language Processing (NLP)

Natural Language Processing (NLP)

Understands text by using the relationship between different words in a block of text. Allows for question answering, sentence paraphrasing, and many other language-related use cases. details.

Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR)

Recognizes and transcribes audio in real time. details.

Biomedical Image Segmentation

Biomedical Image Segmentation

Performs volumetric segmentation of dense 3D images for medical use cases. details.

Climate Atmospheric River Identification Category

Climate Atmospheric River Identification

Identify hurricanes and atmospheric rivers in climate simulation data. details.

Cosmology Parameter Prediction Category

Cosmology Parameter Prediction

Solve a 3D image regression problem on cosmological data. details.

Quantum Molecular Modeling Category

Quantum Molecular Modeling

Predict energies or molecular configurations. details.

Protein Structure Prediction

Protein Structure Prediction

Predicts three-dimensional protein structure based on one-dimensional amino acid connectivity. details.

NVIDIA MLPerf Benchmark Results

  • Training


  • Inference


  • HPC


The NVIDIA accelerated computing platform, powered by NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking, shattered large LLM training performance records in MLPerf Training v3.1, powering two submissions on the GPT-3 175B benchmark at an unprecedented scale of 10,752 H100 GPUs with near-linear scaling efficiency. And, on the newly-added text-to-image test based on Stable Diffusion v2, the NVIDIA platform set the bar, achieving the highest performance and demonstrating unrivaled scalability. Through relentless full-stack engineering at data center scale, NVIDIA continues to accelerate AI training performance at the speed of light.

NVIDIA Sets a New Large Language Model Training Record With Largest MLPerf Submission Ever

NVIDIA Sets a New Large Language Model Training Record With Largest MLPerf Submission Ever
Benchmark Per-Accelerator Records
(NVIDIA H100 Tensor Core GPU)
Large Language Model (GPT-3 175B) 548 hours (23 days)
Natural Language Processing (BERT) 0.71 hours
Recommendation (DLRM-DCNv2) 0.56 hours
Speech Recognition (RNN-T) 2.2 hours
Image Classification (ResNet-50 v1.5) 1.8 hours
Object Detection, Heavyweight (Mask R-CNN) 2.6 hours
Object Detection, Lightweight (RetinaNet) 4.9 hours
Image Segmentation (3D U-Net) 1.6 hours

NVIDIA AI Platform Achieves Highest Performance on Every MLPerf Training Test

In addition to breakthrough performance at scale on the cutting-edge large language model and text-to-image tests, NVIDIA also achieved new performance records on the recommender, object detection, medical image segmentation, and natural language processing workloads in MLPerf Training v3.1. With NVIDIA H100 GPUs and NVIDIA Quantum-2, the NVIDIA platform continues to deliver the fastest time to train on every benchmark, demonstrating its unmatched performance, scalability, and versatility to handle the full range of AI workloads. 

Max-Scale Performance

Benchmark Time to Train
GPT-3 3.92 minutes
Stable Diffusion v2 2.47 minutes
DLRM-DCNv2 1.0 minutes
BERT-large 0.12 minutes
ResNet-50 v1.5 0.18 minutes
Mask R-CNN 1.5 minutes
RetinaNet 0.92 minutes
3D U-Net 0.77 minutes
RNN-T 1.7 minutes

The NVIDIA H100 Tensor Core GPU powered the highest-throughput systems across every MLPerf Inference v3.1 data center workload and scenario. In its MLPerf debut, the NVIDIA GH200 Grace Hopper™  Superchip ran every workload and extended the exceptional performance of H100. The NVIDIA L4 Tensor Core GPU, optimized to be the most efficient NVIDIA accelerator for mainstream servers, also achieved great results across the board. For energy-efficient edge AI and robotics applications, NVIDIA Jetson AGX Orin™ and Jetson Orin NX continued to demonstrate outstanding system-on-module inference capabilities.

Offline Scenario for Data Center and Edge (Single GPU)

NVIDIA GH200 Grace Hopper Superchip (Inferences/Second) NVIDIA H100 (Inferences/Second) NVIDIA L4 (Inferences/Second) NVIDIA Jetson AGX Orin (Max Inferences/Query) NVIDIA Jetson Orin NX (Max Inferences/Query)
GPT-J (Large Language Model) 13.34 13.29 1.30 N/A N/A
DLRMv2 (Recommender) 49,002 42,856 3,673 N/A* N/A*
BERT (Natural Language Processing)** 8,646 7,878 631 554 195
ResNet-50 v1.5 (Image Classification) 93,198 88,526 12,882 6,424 2,641
RetinaNet (Object Detection) 1,849 1,761 226 149 67
RNN-T (Speech Recognition) 25,975 23,307 3,899 1,170 432
3D U-Net (Medical Imaging) 6.8 6.5 1.07 0.51 0.20

The NVIDIA H100 Tensor Core supercharged the NVIDIA platform for HPC and AI in its MLPerf HPC v3.0 debut, enabling up to 16X faster time to train in just three years and delivering the highest performance on all workloads across both time-to-train and throughput metrics. The NVIDIA platform was also the only one to submit results for every MLPerf HPC workload, which span climate segmentation, cosmology parameter prediction, quantum molecular modeling, and the latest addition, protein structure prediction. The unmatched performance and versatility of the NVIDIA platform makes it the instrument of choice to power the next wave of AI-powered scientific discovery.

Up to 16X More Performance in 3 Years

NVIDIA Full-Stack Innovation Fuels Performance Gains

Up to 16X More Performance in 3 Years
Up to 16X More Performance in 3 Years

The Technology Behind the Results

The complexity of AI demands a tight integration between all aspects of the platform. As demonstrated in MLPerf’s benchmarks, the NVIDIA AI platform delivers leadership performance with the world’s most advanced GPU, powerful and scalable interconnect technologies, and cutting-edge software—an end-to-end solution that can be deployed in the data center, in the cloud, or at the edge with amazing results.

Pre-trained models and Optimized Software from NVIDIA NGC

Optimized Software that Accelerates AI Workflows

An essential component of NVIDIA’s platform and MLPerf training and inference results, the NGC™ catalog is a hub for GPU-optimized AI, HPC, and data analytics software that simplifies and accelerates end-to-end workflows. With over 150 enterprise-grade containers—including workloads for generative AI, conversational AI, and recommender systems; hundreds of AI models; and industry-specific SDKs that can be deployed on premises, in the cloud, or at the edge—NGC enables data scientists, researchers, and developers to build best-in-class solutions, gather insights, and deliver business value faster than ever.

Leadership-Class AI Infrastructure

Achieving world-leading results across training and inference requires infrastructure that’s purpose-built for the world’s most complex AI challenges. The NVIDIA AI platform delivered leading performance powered by the NVIDIA GH200 Grace Hopper Superchip, the NVIDIA H100 Tensor Core GPU, the NVIDIA L4 Tensor Core GPU, and the scalability and flexibility of NVIDIA interconnect technologies—NVIDIA NVLink®, NVSwitch™, and Quantum-2 InfiniBand. These are at the heart of the NVIDIA data center platform, the engine behind our benchmark performance.

In addition, NVIDIA DGX™ systems offer the scalability, rapid deployment, and incredible compute power that enable every enterprise to build leadership-class AI infrastructure. 

Leadership-Class AI Infrastructure

Learn more about our data center training and inference product performance.