graph neural networks

Jun 12, 2024

NVIDIA Sets New Generative AI Performance and Scale Records in MLPerf Training v4.0

Generative AI models have a variety of uses, such as helping write computer code, crafting stories, composing music, generating images, producing videos, and...

11 MIN READ

Decorative image of graphs as light web.

Apr 03, 2024

Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 2

Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges...

5 MIN READ

Mar 14, 2024

Applying Mixture of Experts in LLM Architectures

Mixture of experts (MoE) large language model (LLM) architectures have recently emerged, both in proprietary LLMs such as GPT-4, as well as in community models...

12 MIN READ

An illustration representing WholeGraph.

Mar 08, 2024

Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 1

Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing...

9 MIN READ

PyG and Accelerated with NVIDIA logos on a white background.

Jan 17, 2024

Release: PyTorch Geometric Container for GNNs on NGC

The NVIDIA PyG container, now generally available, packages PyTorch Geometric with accelerations for GNN models, dataloading, and pre-processing using...

1 MIN READ

Nov 28, 2023

One Giant Superchip for LLMs, Recommenders, and GNNs: Introducing NVIDIA GH200 NVL32

At AWS re:Invent 2023, AWS and NVIDIA announced that AWS will be the first cloud provider to offer NVIDIA GH200 Grace Hopper Superchips interconnected with...

9 MIN READ

Nov 09, 2023

Enabling Greater Patient-Specific Cardiovascular Care with AI Surrogates

A Stanford University team is transforming heart healthcare with near real-time cardiovascular simulations driven by the power of AI. Harnessing...

8 MIN READ

Oct 13, 2023

Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance

Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across...

11 MIN READ

Oct 12, 2023

Workshop: Model Parallelism: Building and Deploying Large Neural Networks

Learn how to train the largest neural networks and deploy them to production.

1 MIN READ

Aug 31, 2023

Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL

Graph neural networks (GNNs) have emerged as a powerful tool for a variety of machine learning tasks on graph-structured data. These tasks range from node...

7 MIN READ

Jun 06, 2023

Develop Physics-Informed Machine Learning Models with Graph Neural Networks

NVIDIA Modulus is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine...

6 MIN READ

Jan 17, 2023

New Course: Introduction to Graph Neural Networks

Learn the basic concepts, implementations, and applications of graph neural networks (GNNs) in this new self-paced course from NVIDIA Deep Learning Institute.

1 MIN READ

Jan 09, 2023

Optimizing Large-Scale Sparse Volumetric Data with NVIDIA NeuralVDB Early Access

Building on the past decade’s development of OpenVDB, the introduction of NVIDIA NeuralVDB is a game-changer for developers and researchers working with...

4 MIN READ

Decorative image of two graph neural networks on black backgrounds.

Nov 04, 2022

Explainer: What Are Graph Neural Networks?

GNNs apply the predictive power of deep learning to rich data structures that depict objects and their relationships as points connected by lines in a graph.

1 MIN READ

Oct 04, 2022

Optimizing Fraud Detection in Financial Services with Graph Neural Networks and NVIDIA GPUs

Fraud is a major problem for many financial services firms, costing billions of dollars each year, according to a recent Federal Trade Commission report....

22 MIN READ