NVIDIA

NVIDIA Launches AI Foundation Models for RTX AI PCs

NVIDIA NIM Microservices and AI Blueprints Help Developers and Enthusiasts Build AI Agents and Creative Workflows on PC January 6, 2025 — CES — NVIDIA today announced foundation models running locally on NVIDIA RTX™ AI PCs that supercharge digital humans, content creation, productivity and development. These models — offered as NVIDIA NIM™ microservices — are accelerated by […]

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NVIDIA Puts Grace Blackwell on Every Desk and at Every AI Developer’s Fingertips

NVIDIA Project DIGITS With New GB10 Superchip Debuts as World’s Smallest AI Supercomputer Capable of Running 200B-Parameter Models January 6, 2025 — CES — NVIDIA today unveiled NVIDIA® Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the NVIDIA Grace Blackwell platform. Project

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NVIDIA Blackwell GeForce RTX 50 Series Opens New World of AI Computer Graphics

Next Generation of GeForce RTX GPUs Deliver Stunning Visual Realism and 2x Performance Increase, Made Possible by AI, Neural Shaders and DLSS 4 January 6, 2025 — CES — NVIDIA today unveiled the most advanced consumer GPUs for gamers, creators and developers — the GeForce RTX™ 50 Series Desktop and Laptop GPUs. Powered by the

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NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 2 of 3)

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. During this series, we will use Tenyks to build a data-centric pipeline to debug and fix a model trained with the NVIDIA TAO Toolkit. Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline based

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 2 of 3) Read More +

Who Decides Edge AI Winners in Embedded?

In data centers, ML researchers’ vote for Nvidia is what made Nvidia a runaway success in AI training. On the embedded market, who holds the key for edge AI? What’s at stake: Edge AI – or AIOT (Artificial Intelligence of Things) – on the embedded market has been a hot notion among MCU vendors for

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From Generative to Agentic AI, Wrapping the Year’s AI Advancements

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.

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NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 1 of 3)

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In this series, we’ll build a data-centric pipeline using Tenyks to debug and fix a model trained with the NVIDIA TAO Toolkit. ‍Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline tailored for a

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 1 of 3) Read More +

An Easy Introduction to Multimodal Retrieval-augmented Generation for Video and Audio

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Building a multimodal retrieval augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In our previous post, An Easy Introduction

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NVIDIA Unveils Its Most Affordable Generative AI Supercomputer

The Jetson Orin Nano Super delivers up to a 1.7x gain in generative AI performance, supporting popular models for hobbyists, developers and students. NVIDIA is taking the wraps off a new compact generative AI supercomputer, offering increased performance at a lower price with a software upgrade. The new NVIDIA Jetson Orin Nano Super Developer Kit,

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An Easy Introduction to Multimodal Retrieval-augmented Generation

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts, and diagrams—and not just text. This requires a framework that can understand and generate responses

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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