NVIDIA recently released cuEmbed, a high-performance, header-only CUDA library that accelerates embedding lookups on NVIDIA GPUs. If you��re building recommendation systems, embedding operations are likely consuming significant computational resources. Embedding lookups present a unique optimization challenge. They��re memory-intensive operations with irregular access patterns.
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As modern enterprise and cloud environments scale, the complexity and volume of network traffic increase dramatically. NetFlow is used to record metadata about the traffic flows traversing a network device such as a router, switch, or host. NetFlow data, essential for understanding network traffic, can be effectively modeled as graphs where edges capture properties such as connection duration and��
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Large language models (LLMs) often struggle with accuracy when handling domain-specific questions, especially those requiring multi-hop reasoning or access to proprietary data. While retrieval-augmented generation (RAG) can help, traditional vector search methods often fall short. In this tutorial, we show you how to implement GraphRAG in combination with fine-tuned GNN+LLM models to achieve��
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In the latest round of MLPerf Inference �C a suite of standardized, peer-reviewed inference benchmarks �C the NVIDIA platform delivered outstanding performance across the board. Among the many submissions made using the NVIDIA platform were results using the NVIDIA GH200 Grace Hopper Superchip. GH200 tightly couples an NVIDIA Grace CPU with an NVIDIA Hopper GPU using NVIDIA NVLink-C2C��
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Generative AI models have a variety of uses, such as helping write computer code, crafting stories, composing music, generating images, producing videos, and more. And, as these models continue to grow in size and are trained on even more data, they are producing even higher-quality outputs. Building and deploying these more intelligent models is incredibly compute-intensive��
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Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges extend beyond the typical concerns of neural network forward and backward computations, encompassing issues such as bandwidth-intensive graph feature gathering and sampling, and the limitations of single GPU capacities.
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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 with the open-source release of Mistral Mixtral 8x7B. The strong relative performance of the Mixtral model has raised much interest and numerous questions about MoE and its use in LLM architectures. So, what is MoE and why is it important?
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Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing intricate relationships in graphs, powering applications from social networks to chemistry. They shine particularly in scenarios like node classification, where they predict labels for graph nodes, and link prediction, where they determine the��
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The NVIDIA PyG container, now generally available, packages PyTorch Geometric with accelerations for GNN models, dataloading, and pre-processing using cuGraph-Ops, cuGraph, and cuDF from NVIDIA RAPIDS, all with an effortless out-of-the-box experience.
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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 NVIDIA NVLink technology through NVIDIA DGX Cloud and running on Amazon Elastic Compute Cloud (Amazon EC2). This is a game-changing technology for cloud computing. The NVIDIA GH200 NVL32, a rack-scale solution within NVIDIA DGX Cloud or an��
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A Stanford University team is transforming heart healthcare with near real-time cardiovascular simulations driven by the power of AI. Harnessing physics-informed machine learning surrogate models, the researchers are generating accurate and patient-specific blood flow visualizations for a non-invasive window into cardiac studies. The technology has far-reaching scope��
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Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. According to one study, by 2025 graph technologies will be used in 80% of data and analytics innovations, which will help facilitate rapid decision making across organizations. When working with graphs containing��
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Learn how to train the largest neural networks and deploy them to production.
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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 classification and link prediction to graph classification. They also cover a wide range of applications such as social network analysis, drug discovery in healthcare, fraud detection in financial services, and molecular chemistry.
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NVIDIA PhysicsNeMo is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine learning (physics-ML) models. PhysicsNeMo is available as OSS (Apache 2.0 license) to support the growing physics-ML community. The latest PhysicsNeMo software update, version 23.05, brings together new capabilities��
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Learn the basic concepts, implementations, and applications of graph neural networks (GNNs) in this new self-paced course from NVIDIA Deep Learning Institute.
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Building on the past decade��s development of OpenVDB, the introduction of NVIDIA NeuralVDB is a game-changer for developers and researchers working with extremely large and complex datasets. The pre-release version of NVIDIA NeuralVDB brings AI and GPU optimization to OpenVDB, delivering up to a 100x reduction in memory footprint for smoke, clouds, and other sparse volumetric data.
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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.
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Fraud is a major problem for many financial services firms, costing billions of dollars each year, according to a recent Federal Trade Commission report. Financial fraud, fake reviews, bot assaults, account takeovers, and spam are all examples of online fraud and harmful activity. Although these firms employ techniques to combat online fraud, the methods can have severe limitations.
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