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Simplify End-to-End Autonomous Vehicle Development with New NVIDIA Cosmos World Foundation Models

A GIF showing autonomous vehicles.The shift to end-to-end planning models for powering autonomous vehicles (AVs) is increasing the demand for high-quality, physically-based sensor data. These…A GIF showing autonomous vehicles.

The shift to end-to-end planning models for powering autonomous vehicles (AVs) is increasing the demand for high-quality, physically-based sensor data. These models must have a general understanding of multi-modal datasets, along with the relationships between sensor datasets, vehicle trajectories, and driving actions to help with downstream training and validation tasks. By adapting and post…

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Chat With Your Enterprise Data Through Open-Source AI-Q NVIDIA Blueprint

Enterprise data is exploding—petabytes of emails, reports, Slack messages, and databases pile up faster than anyone can read. Employees are left searching for…

Enterprise data is exploding—petabytes of emails, reports, Slack messages, and databases pile up faster than anyone can read. Employees are left searching for answers in a sea of information, as “68% of available data in an organization goes unused,” according to market researcher Gartner1. That’s now possible with today’s availability of AI-Q, an open-source NVIDIA Blueprint that puts your…

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Accelerating AV Simulation with Neural Reconstruction and World Foundation Models

Autonomous vehicle (AV) stacks are evolving from a hierarchy of discrete building blocks to end-to-end architectures built on foundation models. This transition…

Autonomous vehicle (AV) stacks are evolving from a hierarchy of discrete building blocks to end-to-end architectures built on foundation models. This transition demands an AV data flywheel to generate synthetic data and augment sensor datasets, address coverage gaps and, and ultimately, build a validation toolchain to safely develop and deploy autonomous vehicles. In this blog post…

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Simplify LLM Deployment and AI Inference with a Unified NVIDIA NIM Workflow

Decorative image.Integrating large language models (LLMs) into a production environment, where real users interact with them at scale, is the most important part of any AI…Decorative image.

Integrating large language models (LLMs) into a production environment, where real users interact with them at scale, is the most important part of any AI workflow. It’s not just about getting the models to run—it’s about making them fast, easy to manage, and flexible enough to support different use cases and production needs. With a growing number of LLMs, each with its own architecture…

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Building Photorealistic Digital Twins With Siemens Teamcenter Digital Reality Viewer

Modern products often consist of millions of parts and require intricate design and collaboration. The industrial world is facing significant challenges in…

Modern products often consist of millions of parts and require intricate design and collaboration. The industrial world is facing significant challenges in managing their complexity, with traditional visualization tools failing to render these large, multi-CAD assemblies with the true-to-life realism required to fully benefit from digital twins. To address these struggles…

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Accelerate Decision Optimization Using Open Source NVIDIA cuOpt

Businesses make thousands of decisions every day—what to produce, where to ship, how to allocate resources. At scale, optimizing these decisions becomes a…

Businesses make thousands of decisions every day—what to produce, where to ship, how to allocate resources. At scale, optimizing these decisions becomes a computational challenge. Linear programming (LP), mixed-integer programming (MIP), and vehicle routing problems (VRP) provide structure, but solving them fast is where the bottleneck begins. NVIDIA cuOpt brings GPU acceleration to decision…

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Build Efficient AI Agents Through Model Distillation With NVIDIA’s Data Flywheel Blueprint

As enterprise adoption of agentic AI accelerates, teams face a growing challenge of scaling intelligent applications while managing inference costs. Large…

As enterprise adoption of agentic AI accelerates, teams face a growing challenge of scaling intelligent applications while managing inference costs. Large language models (LLMs) offer strong performance but come with substantial computational demands, often resulting in high latency and costs. At the same time, many development workflows—such as evaluation, data curation…

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Introducing Training Cluster as a Service – a new collaboration with NVIDIA

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Italy Drives Industrial Renaissance With NVIDIA AI

Sovereign AI has sparked a new Italian renaissance around industrial transformation. Supporting its design and manufacturing heritage, Italy is among leading European nations in its advanced development of sovereign AI. Italy joins European nations in building domestic AI infrastructure with an ecosystem of NVIDIA Cloud Partners and telecom providers such as Fastweb. Italian telecommunications company
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Sovereign AI Agents Think Local, Act Global With NVIDIA AI Factories

The European Union is investing over $200 billion into AI — but to gain the most value from this investment, its developers must navigate three key constraints: limited compute availability, data-privacy needs and safety priorities. Unveiled at the NVIDIA GTC Paris keynote at VivaTech, a new suite of NVIDIA technologies is making it easier to
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