Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and services they already use. Many of these workflows already depend on OpenUSD scenes, simulation-ready (SimReady) assets, Blender-based workflows, CAD pipelines, or domain-specific app stacks. The challenge is how to provide applications and…
OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation...OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation assets, and real-world telemetry into a shared, physically accurate view of the world. Until now, building a USD implementation has typically required adapting a large existing codebase— even for teams that need a specific memory footprint…
What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning...What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning models to production video tasks, developers often lose days to data formatting, container setup, training scripts, baseline evaluation, and hyperparameter sweeps before they even know whether post-training improves accuracy.
Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these...Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these events with brute-force Monte Carlo sampling—running a model repeatedly with randomly drawn inputs to estimate the probability of rare outcomes—can require an excessive volume of model iterations, especially when each sample comes from an…
Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,...Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states, communication buffers, and intermediate activations all compete for GPU high-bandwidth memory (HBM). As model size, sequence length, and batch size grow, HBM capacity often becomes the primary scaling bottleneck. This post explains how…
Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein...Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein design. Increasingly, they’re driven end-to-end by AI agents. For an agent to run that pipeline well, every step needs to be fast and scalable: Multiple Sequence Alignment (MSA) generation, co-folding inference, serving, and multi-GPU scale-out.
Molecular dynamics (MD) simulations are among the most demanding workloads in computational science. Using them, researchers can observe atomic behavior in...
As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids...As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids remains complex, and today’s development pipelines are still highly fragmented. As a result, developers spend significant time configuring robotics infrastructure before they can focus on building robot capabilities.
NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such...NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such as cameras and lidar. It is used to reconstruct dynamic scenes captured by autonomous vehicle (AV) and robotics platforms into simulation-ready digital environments that can be rendered, replayed, and analyzed inside NVIDIA Omniverse and…
AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files,...AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files, and iterate on results. But science isn’t software engineering. There is no test suite that turns green when a hypothesis is correct; discovery is iterative, uncertain, and grounded in the physical world. You can’t take a general coding…
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...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 hours and more engineering iteration time. Accelerating transformers is therefore not just a performance optimization, but directly affects how quickly teams can experiment and how large a model they can afford to train.
Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language...Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language model) and Evo 2 (a DNA language model) capture statistical regularities of biological sequences. These transfer well to a wide range of downstream tasks, including structure prediction, variant effect, and functional annotation.
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...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 produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.
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...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, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks. NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning…
Developers can now use NVIDIA CUDA Tile programming within large existing C++ GPU codebases to develop highly optimized GPU kernels using tile-based...
High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions,...High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions, and the high cost of expert annotation. As a result, training reliable 3D medical imaging models is frequently bottlenecked by small, narrow, and hard‑to‑share datasets, limiting model robustness and generalization. To help teams overcome…
For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,...For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragments—single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Now…
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