How NVIDIA Uses engineering

Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare…

Learn how NVIDIA NeMo Switchyard routes AI agent workloads across models using tuning-free and tunable routers that balance model capability, cost, and latency.

NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users…

Knowledge workers are increasingly integrating AI agents into their workflows. Agents that function as “digital coworkers” offer clear benefits. For example…

Deploying an AI coding assistant in a regulated, sovereign, or source-sensitive environment, often comes with challenges. Three common issues are: the source…

Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation.

Building a great AI agent isn’t just about choosing the right models. The harness is the architecture surrounding the model. How it renders context…

Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware…

As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have…

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale.

The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore a focused question: What techniques can improve reasoning accuracy when…

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning…

Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these…

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are…

Spectrum is one of the most valuable assets in wireless communications. Over the last 30 years, telecom operators in the US have spent more than $240B to…

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context…

NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such…

AI companions in games have long been constrained by fixed dialogue. PUBG Ally is a different kind of system. Built by KRAFTON for PUBG: BATTLEGROUNDS…

AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files…

Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy.

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected.

NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium.

Transformer architectures are the backbone of many modern large language and generative AI models. As these models grow in size, training runs consume more GPU…

AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how…

NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single click.

AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale.

AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster.

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…

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

NVIDIA CompileIQ tackles one of the hardest problems in performance engineering: finding the compiler options that unlock the best performance for a specific…

Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code…

The path from a trained AI model to production should be smooth, but rarely is. Many teams invest weeks fine-tuning models, only to discover that exporting to a…

An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool calls, and subsequent user turns return…

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting.

Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a…

The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation…

The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential…

In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground…

AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools.

AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots…

Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents.

The development of socially acceptable nuclear reactors requires that they are safe, clean, efficient, economical, and sustainable.

The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as…

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and…

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak…

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…

AI is evolving, and reasoning models are increasing token demand, placing new requirements on every layer of AI infrastructure. More than ever…

Artificial intelligence is token-driven. Every prompt, reasoning step, and agent interaction generates tokens. Over the past year, token consumption has grown…

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

NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new standard for visuals and performance.