Modern LLM serving is hard to tune because each deployment is a stack of interacting choices: model backend, tensor-parallel shape, prefill/decode split, worker counts, scheduler settings…

Announced at GTC Taipei at COMPUTEX, NVIDIA OpenShell brings secure agents to Windows with 2x inference performance on llama.cpp — plus, Adobe rebuilds its apps with performance and memory enhancements, and Blender adds NVIDIA DLSS 4.5 Ray Reconstruction…

The new, open NVIDIA world foundation model brings vision reasoning, multimodal generation and action prediction together to help robots, autonomous vehicles and vision AI agents think before acting in the real world.

Semiconductor and electronics manufacturing leaders are using NVIDIA AI to speed manufacturing from fabs to factory floors as they ramp up the production of NVIDIA Vera Rubin NVL72 infrastructure for agentic AI factories.

The FOX blueprint, a reference design for building an autonomous factory manager agent, delivers total factory visibility for better, faster decision-making.

Fast-growing ecosystem helps enterprises, startups, nations, AI labs and developers scale agentic AI applications.

AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale faster, operate more efficiently…

Each wave of AI has created a new scaling law. Pretraining scaled intelligence through larger datasets, more parameters, and massively parallel GPU systems. Post-training scaled usefulness through…

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…

Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world…

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and…

With NVIDIA JetPack 7.2 and NVIDIA NemoClaw support, Jetson is agentic-ready, giving developers a production-grade stack to deliver next-level intelligence to robotics, inspection and industrial automation.

Financial institutions have spent years building AI: fraud models, credit models, recommendation engines and risk systems. While this sprawl of task-specific models has been effective, it’s also constrained by siloed systems. 

The rise of autonomous, long-running AI agents has introduced a new class of compute demand, namely tasks that maintain large context windows, spawn concurrent subagents…

As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized memory and performance. NVIDIA JetPack 7.2…

AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster. But combining internal data with public sources poses security…

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing…

At Microsoft Build, NVIDIA founder and CEO Jensen Huang joined Microsoft chairman and CEO Satya Nadella's keynote via livestream from Taipei to discuss the expanded partnership.

Showcased at GTC Taipei at COMPUTEX, autonomous AI engineers compress weeks of simulation work into just hours.

New physical AI agent skills, powered by NVIDIA Cosmos 3, help researchers accelerate data generation, simulation, policy training and evaluation for autonomous system development.

New NVIDIA Research breakthroughs show how training at scale — across gripper types, driving scenarios and virtual worlds — creates AI that generalizes to diverse applications.