Physics forms the foundation of robotic simulation, enabling realistic modeling of motion and interaction. For tasks like locomotion and manipulation, simulators must handle complex dynamics such as…

AI-native services are exposing a new bottleneck in AI infrastructure: As millions of users, agents, and devices demand access to intelligence, the challenge is shifting from peak training throughput…

NVIDIA cuDF accelerates Apache Spark applications on Google Cloud helps Snap engineers test and deploy new features faster while unlocking significant cost savings.

Nemotron 3 open models unlock fast, private AI agents like OpenClaw; plus, creativity is accelerated with RTX-optimized NVFP4 and FP8 visual generative AI models.

As AI‑native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI.  At NVIDIA GTC 2026, leading operators in the U.S. and Asia showed that this shift is underway, announcing…

NVIDIA and Apple’s collaboration brings native integration of NVIDIA CloudXR 6.0 to visionOS, securely delivering NVIDIA RTX-powered simulators and professional 3D graphics applications — like Immersive for Autodesk VRED on Innoactive’s XR streaming solutions…

The latest open models and frameworks from NVIDIA bring together simulation, robot learning and embedded compute to accelerate cloud-to-robot workflows.

While consumer AI offers powerful capabilities, workplace tools often suffer from disjointed data and limited context. Built with LangChain, the NVIDIA AI-Q blueprint is an open source template that…

Dive into immersive gaming at up to 90 fps on VR devices and play 'Crimson Desert' at launch with GeForce RTX 5080-class power.

NVIDIA Groq 3 LPX is a new rack-scale inference accelerator for the NVIDIA Vera Rubin platform, designed for the low-latency and large-context demands of agentic systems. Co-designed with the NVIDIA…

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

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages have fundamentally different compute…

NVIDIA OpenShell provides tools for controlling autonomous agents in a trusted infrastructure policy layer.

AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud. This includes sensitive information like patient records, market research…

Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime management. From factory automation cells…

In addition, NVIDIA announced at KubeCon Europe a confidential containers solution for GPU-accelerated workloads, updates to the NVIDIA KAI Scheduler and new open source projects to enable large-scale AI workloads.

Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale, developers need models that can understand…

In the AI era, power is the ultimate constraint, and every AI factory operates within a hard limit. This makes performance per watt—the rate at which power is converted into revenue-generating…

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or small molecule. The search space for…

In the current state of automotive radar, machine learning engineers can’t work with camera-equivalent raw RGB images. Instead, they work with the output of radar constant false alarm rate (CFAR)…

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may…