Explainer: What Is Retrieval-Augmented Generation aka RAG?
Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.
There are some useful intrinsic functions in the NVIDIA GPU instruction set that are not included in standard graphics APIs. Updated from the original 2016 post...
Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human emergent abilities on a...
Large language models (LLMs) are a class of generative AI models built using transformer networks that can recognize, summarize, translate, predict, and...
Getting Started with Large Language Models for Enterprise Solutions
Large language models (LLMs) are deep learning algorithms that are trained on Internet-scale datasets with hundreds of billions of parameters. LLMs can read,...
By using descriptor types, you can bind resources to shaders and specify how those resources are accessed. This creates efficient communication between the CPU...
Recently, NVIDIA unveiled Jetson Generative AI Lab, which empowers developers to explore the limitless possibilities of generative AI in a real-world setting...
NVIDIA offers a large suite of tools for graphics debugging, including NVIDIA Nsight System for CPU debugging, and Nsight Graphics for GPU debugging. Nsight...
New Video: Representing Data with OpenUSD Custom Schemas
Custom schemas in Universal Scene Description, known as OpenUSD or USD, are pivotal for developers seeking to represent and encode sophisticated virtual worlds....
Software-Defined Broadcast with NVIDIA Holoscan for Media
The broadcast industry is undergoing a transformation in how content is created, managed, distributed, and consumed. This transformation includes a shift from...
