developer.nvidia.comadded August 2026
Nvidia Developer Blogby Elizabeth Goodman3d ago
Preparing CAD assets for robotics simulation requires more than converting geometry to OpenUSD: developers must configure and validate materials, collision geometry, joints, and other physics...
Nvidia Developer Blogby Jiwei Liu3d ago
The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition with a system built around a simple idea of making an agent’s harness smaller, clearer, and easier to verify.
Nvidia Developer Blogby Elizabeth Goodman4d ago
How we taught robots to assemble GB300 tester trays and what it taught us about robot learning, mechanical intelligence, and good old-fashioned engineering The NVIDIA Grace Blackwell GB300...
Nvidia Developer Blogby Avi Alkobi4d ago
AI factories are some of the most complex operations in the world, combining GPUs, CPUs, switches, DPUs, and SuperNICs alongside schedulers, orchestration services, security controls, and a rapidly...
Nvidia Developer Blogby Tanya Lenz4d ago
Supply chain problems are expanding across more SKUs, lanes, and constraints than ever before, while energy grids are balancing more distributed sources in real time.
Nvidia Developer Blogby Quynh L. Nguyen4d ago
Observatories and telescopes, lasers and X-ray light sources, and other high-throughput instruments generate image data faster than CPU-bound pipelines can process it to support timely decisions.
Nvidia Developer Blogby Ashwath Aithal5d ago
Bitwise determinism makes large-scale pretraining easier to debug, validate, and resume reproducibly.
Nvidia Developer Blogby Elizabeth Goodman5d ago
GPU applications increasingly need networking and data movement to behave like first-class GPU-controlled operations rather than host-driven services.
Nvidia Developer Blogby Elizabeth Goodman5d ago
GPU-accelerated Kubernetes clusters depend on compatible versions across dozens of components, each on its own release cycle: host kernels, GPU drivers, container runtimes, networking, storage...
Nvidia Developer Blogby Elizabeth Goodman5d ago
GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator alongside a throughput-oriented background...
Nvidia Developer Blogby Tanya Lenz10d ago
AI agents are becoming a standard part of development workflows, but general-purpose agents weren’t built with specialized infrastructure software such as NVIDIA DOCA in mind.
Nvidia Developer Blogby Luca Spindler10d ago
Adding AI models to local applications requires a portable model format, a reliable runtime, and acceleration that works across target systems.
Nvidia Developer Blogby Elizabeth Goodman11d ago
Automatic speech recognition must handle how people actually speak, not only the languages and styles that dominate pretraining data.
Nvidia Developer Blogby Tanya Lenz11d ago
Generative recommender (GR) systems are emerging as a powerful new approach for large-scale personalization.
Nvidia Developer Blogby Harish Arora11d ago
AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI workloads.
Nvidia Developer Blogby William Markito Oliveira11d ago
An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching the same content again.
Nvidia Developer Blogby Tanya Lenz12d ago
Parallel work, model-family isolation, reversible changes, and GPU-backed validation shaped an open source project designed around coding agents NVIDIA TensorRT Model Connect is an open source...
Nvidia Developer Blogby Elizabeth Goodman12d ago
Vision-language models have made it possible to build visual AI agents that understand video at production scale.
Nvidia Developer Blogby Tanya Lenz13d ago
To understand where agentic AI stands today, consider the last seismic shift in technology: the rise of the internet in the 90s. It was new and full of possibilities.
Nvidia Developer Blogby Alex Watson13d ago
AI agents can be given a goal, write code, use tools, and keep working as new information becomes available.
top 14%