Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and nodes to scale to more users while reducing…

In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the foundational framework for training massive transformer models at scale.

NVIDIA’s annual “State of AI” reports show how AI is being adopted across industries, what it’s being used for and how companies are achieving ROI, as well as their challenges and goals with the technology. 

ABB Robotics and NVIDIA today announced a breakthrough partnership that brings industrial‑grade physical AI to the factory floor.

CUDA 13.2 arrives with a major update: NVIDIA CUDA Tile is now supported on devices of compute capability 8.X architectures (NVIDIA Ampere and NVIDIA Ada), as well as 10.X, 11.X and 12.

AI is one of the most powerful forces shaping the world today. It is not a clever app or a single model; it is essential infrastructure, like electricity and the internet.

NVIDIA and Thinking Machines Lab announced today a multiyear strategic partnership to deploy at least one gigawatt of next-generation NVIDIA Vera Rubin systems to support Thinking Machines’ frontier model training and platforms delivering customizable AI…

NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new standard for visuals and performance. At GDC 2026, NVIDIA unveiled the latest path…

Agentic code assistants are moving into daily game development as studios build larger worlds, ship more DLCs, and support distributed teams. These assistants can accelerate development by helping…

GDC 2026: AI-powered video generation with ComfyUI’s App View, NVIDIA RTX Video Super Resolution and new NVFP4 models.

NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs centralize compute infrastructure for content creation, AI, engineering and quality assurance, delivering workstation-class performance at data center scale for game studios.

Open source generative AI models are leaving the data center and showing up in machines that work in the physical world. From Orin to Thor, the NVIDIA Jetson family is becoming a common place to run models like NVIDIA Nemotron, Cosmos and Isaac GR00T and…

Rolling coverage from San Jose, including NVIDIA CEO Jensen Huang’s keynote, news highlights, live demos and on‑the‑ground color through March 20.

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and long-context analysis…

A new, open, 120-billion-parameter hybrid mixture-of-experts model optimized for NVIDIA Blackwell addresses the costs of long thinking and context explosion that slow autonomous agent workflows.

See our GDC news for GeForce NOW updates coming soon, and check out the 5 new games, including Monster Hunter Stories 3.

Earlier this month, NVIDIA and Dassault Systèmes announced a partnership that brings together Dassault Systèmes’ Virtual Twin platforms, NVIDIA accelerated computing, AI physics open models and NVIDIA CUDA-X and Omniverse libraries.

Physical AI is rapidly evolving, from next-generation software-defined autonomous vehicles (AVs) to humanoid robots. The challenge is no longer how to run a large language model (LLM)…

Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and workload configurations.

Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across geometries and operating conditions.

The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don’t…