# Simulation / Modeling / Design Published articles for Simulation / Modeling / Design. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs DevFeed: [CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs]() Original publisher: [Read original article]() Author: Jonathan Bentz Published: 2026-09-09T20:24:12Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [GPU](), [NVLink](), [Orchestration]() Tags: [cli](), [cuda](), [cuda-tile](), [developer-tools-techniques](), [gpu](), [nsight-tools-compute](), [nvidia](), [nvlink](), [release](), [simulation-modeling-design](), [top-stories](), [windows]() ### AI overview CUDA Toolkit 13.4 adds Windows on Arm support, preview support for the NVIDIA Rubin GPU architecture, and new GPU-sharing controls through MPS V3. It also introduces CUDA Compute Fabric Transport for data movement across NVIDIA NVLink fabric. ### Source excerpt Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software... ## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:30:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI research agents](), [OpenSSH]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [bionemo](), [claude](), [code](), [developer-tools-techniques](), [drug-discovery](), [gpu](), [microservices](), [nim](), [nvidia](), [science](), [simulation-modeling-design](), [workflow]() ### AI overview A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models. ### Source excerpt Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle... ## How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents DevFeed: [How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-26T20:05:06Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Reinforcement learning](), [codex](), [coding](), [Simulation and Design](), [CUDA]() Tags: [agent](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [architecture](), [codex](), [coding](), [cuda](), [development](), [evaluation](), [framework](), [nvidia](), [omniverse](), [physical-ai](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [skills](), [testing](), [training](), [tutorial](), [workflow]() ### AI overview This tutorial presents an agent-driven COMPASS workflow for training and evaluating cross-embodiment robot navigation policies. It covers asset preparation, smoke testing, residual reinforcement learning, checkpoint evaluation, runtime integration, and optional reconstructed environments using NVIDIA Omniverse NuRec. ### Source excerpt Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to... ## GPU-Accelerated Clustering for Financial Instruments at Scale DevFeed: [GPU-Accelerated Clustering for Financial Instruments at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-21T16:21:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Algorithm](), [Matrix]() Tags: [batch](), [communication](), [data-analytics-processing](), [data-science](), [financial-services](), [gb200](), [gpu](), [memory](), [nemo](), [nemotron](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [workflow]() ### AI overview A GPU-accelerated workflow uses rolling correlation and tail-dependence matrices to cluster financial instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Its adaptive SymNMF-based solver supports soft factor loadings and hard cluster labels, while memory-efficient and distributed implementations scale from single GPUs to one million instruments across multiple nodes. ### Source excerpt Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor... ## Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control DevFeed: [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [AI, ML & Data Engineering]() Tags: [agentic-ai-generative-ai](), [cosmos](), [deployment](), [edge](), [edge-computing](), [featured](), [foundation-models](), [inference](), [jetson](), [latency](), [nvidia](), [on-device](), [physical-ai](), [post-training](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [thor](), [tutorial]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Edge as an on-device robot manipulation policy, serving it on Jetson Thor, running receding-horizon inference, and evaluating it in closed-loop simulation. ### Source excerpt Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... ## Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator DevFeed: [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Agent Skills](), [AI Agent](), [Benchmark](), [Nvidia](), [Open Source](), [Agent Harness](), [Security]() Tags: [agent](), [agent-skill](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [benchmark](), [build-ai-agents](), [codex](), [cursor](), [developer-tools-techniques](), [featured](), [nvidia](), [open-source](), [prompt-injection](), [sandbox](), [security](), [simulation-modeling-design](), [trustworthy-ai]() ### AI overview NVIDIA SkillEvaluator is an open-source evaluation layer for measuring how packaged skills affect AI-agent performance. It compares agent runs with and without a skill, using static validation, embedding-based distinctiveness checks, and live task evaluations in isolated sandboxes. The article reports benchmark results for more than 300 verified skills across over 30 NVIDIA products and describes integrations with Claude Code, Codex, Cursor, Skills.sh, ClawHub, and Hermes Hub. ### Source excerpt AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding... ## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-18T18:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [ALCHEMI](), [AI-assisted coding](), [Agent Skills](), [PyTorch](), [CUDA](), [GPU](), [Python](), [AI Development]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [ai-coding](), [alchemi](), [coding](), [computational-chemistry-materials-science](), [compute](), [cuda](), [developer-tools-techniques](), [featured](), [gpu](), [python](), [pytorch](), [simulation-modeling-design]() ### AI overview This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines. ### Source excerpt Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the... ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## Beyond VLAs: How World Action Models Reshape Robot Manipulation DevFeed: [Beyond VLAs: How World Action Models Reshape Robot Manipulation]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-04T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [World models](), [vlm](), [post-training](), [Cosmos](), [Nvidia](), [NVIDIA Research]() Tags: [agentic-ai-generative-ai](), [cosmos](), [featured](), [nvidia](), [nvidia-research](), [physical-ai](), [post-training](), [robot-manipulation](), [robotics](), [simulation-modeling-design](), [thor](), [vlm](), [world-model](), [zero-shot]() ### AI overview The article explains how World Action Models (WAMs) use video world models as backbones for robot policies, addressing the physical-generalization limitations of vision-language-action models. It discusses post-training WAMs into specialized policies and presents NVIDIA Cosmos 3 as a foundation for building them. ### Source excerpt A central challenge in robotics is building policies that generalize beyond the demonstrations they're trained on. A policy that succeeds in a training scene... ## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-04T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [foundation-models](), [Cosmos](), [model-deployment](), [Reinforcement learning](), [Inference](), [GitHub](), [hugging face](), [Open Source Models & Datasets]() Tags: [accelerate](), [automotive-transportation](), [autonomous](), [benchmarks](), [cosmos](), [customization](), [data](), [developer-tools-techniques](), [developers](), [development](), [diffusion](), [drive](), [driving](), [evaluation](), [featured](), [fine-tuning](), [generate](), [generation](), [github](), [hugging-face](), [inference](), [learning](), [models](), [nvidia](), [reasoning](), [robot-navigation](), [robotics](), [simulation-modeling-design](), [top-stories]() ### AI overview NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows. ### Source excerpt Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data... ## Developing Healthcare Robotics with GPU-Native Medical Physics Simulation DevFeed: [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-28T20:49:21Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Isaac for Healthcare](), [Robotics](), [Reinforcement learning](), [GPU](), [datasets](), [Medical imaging](), [Cosmos](), [Open Source](), [real-time](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [data](), [datasets](), [featured](), [gpu](), [healthcare](), [isaac](), [isaac-for-healthcare](), [isaac-sim](), [medical-imaging](), [nvidia](), [open-source](), [physical-ai](), [physics](), [real-time](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [warp](), [world-model]() ### AI overview The article presents NVIDIA's open source, GPU-accelerated Medical Physics Simulation framework for healthcare robotics. It addresses limited medical robotics data, poor generalization, and slow development by enabling anatomical digital twins, device-anatomy and medical imaging simulation, and GPU-scale reinforcement learning within Isaac for Healthcare, Isaac Sim, and Isaac Lab. ### Source excerpt Unlike autonomous driving or industrial robotics, healthcare robotics can't rely on internet-scale data collection or unlimited real-world experimentation.... ## NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning DevFeed: [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-27T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Ising](), [Quantum Computing](), [vlm](), [Nvidia](), [Open Source](), [Benchmark](), [benchmarking](), [NVFP4](), [DGX Spark]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [benchmark](), [benchmarking](), [data-science](), [dgx-spark](), [featured](), [ising](), [nvfp4](), [nvidia](), [open-source](), [quantum](), [quantum-computing](), [simulation-modeling-design](), [training-ai-models](), [vlm](), [vlms]() ### AI overview NVIDIA Ising Calibration 1.5 is an open-source vision-language model for interpreting quantum-processor diagnostics and recommending calibration actions. The article highlights zero-shot and in-context learning evaluation on QCalEval, plus an NVFP4-quantized version for local deployment. ### Source excerpt NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they... ## Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing DevFeed: [Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-27T00:45:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [CUDA](), [GPU](), [Optimization](), [Nvidia](), [Simulation and Design](), [Hardware]() Tags: [ai](), [computational-chemistry-materials-science](), [compute](), [cuda](), [cuda-x](), [data-center-cloud](), [engineering](), [featured](), [gpu](), [hardware](), [industrial-digitalization-digital-twin](), [manufacturing](), [nvidia](), [optimization](), [physics](), [production](), [simulation-modeling-design]() ### AI overview Applied Materials and NVIDIA are presented as combining materials engineering, semiconductor manufacturing, CUDA-X libraries, GPU-accelerated simulation, physics-based modeling, and AI-driven digital twins in an end-to-end digital development model. The approach spans atomic-scale materials discovery, process development, and factory optimization, with Ginestra used to connect material defects and properties to predicted device performance. ### Source excerpt As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have... ## Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps DevFeed: [Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-20T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [SDKs]() Tags: [3d](), [agent-skill](), [apis](), [apps](), [autonomous-vehicles](), [c](), [featured](), [lidar](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [python](), [robotics](), [simulation-modeling-design]() ### AI overview The article explains how to integrate NVIDIA Omniverse's ovrtx RTX sensor-simulation library into existing applications. It covers using its C and Python SDK to generate camera, lidar, radar, and related sensor outputs from OpenUSD scenes. ### Source excerpt Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... ## Develop Lightweight USD Runtimes Faster with AI Agents DevFeed: [Develop Lightweight USD Runtimes Faster with AI Agents]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-15T21:57:23Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation and Design](), [Code](), [Nvidia]() Tags: [agents](), [ai](), [ai-agents](), [code](), [developer-tools-techniques](), [featured](), [hackathon](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [robotics](), [simulation-modeling-design]() ### AI overview This article presents nanousd-labs, an experimental NVIDIA Omniverse Labs project that uses AI agents and the machine-readable USD Core Specification to generate lightweight, compliant OpenUSD runtimes. It explains how agents generate and validate code against specification-derived tests, enabling runtimes tailored to memory, performance, language, and deployment constraints. ### Source excerpt OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation... ## Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills DevFeed: [Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-14T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [Agent Skills](), [post-training](), [lora](), [Nvidia]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [computer-vision-video-analytics](), [cosmos](), [featured](), [fine-tuning](), [lora](), [physical-ai](), [post-training](), [simulation-modeling-design](), [training-ai-models]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Nano for video question answering with coding-agent skills, LoRA, and TAO AutoML configuration sweeps. ### Source excerpt What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning... ## How to Evaluate General-Purpose Robot Policies for Real-World Deployment DevFeed: [How to Evaluate General-Purpose Robot Policies for Real-World Deployment]() Original publisher: [Read original article]() Author: Brad Nemire Published: 2026-07-12T01:08:17Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [Benchmark](), [foundation-models](), [Simulation and Design](), [Deployment]() Tags: [benchmark](), [benchmarks](), [deployment](), [evaluation](), [featured](), [foundation-models](), [nvidia-research](), [physics](), [robotics](), [simulation-modeling-design](), [testing]() ### AI overview This developer article examines the challenge of rigorously evaluating general-purpose robot policies for real-world deployment. It discusses simulation as a scalable proxy for expensive real-world testing and identifies limitations in current benchmarks, including shared visual sources between training and evaluation, costly Real2sim reconstruction, static task sets, performance saturation, limited failure diagnostics, and uncertainty in success-rate estimates. ### Source excerpt Robotics foundation models have made remarkable progress. Today's best systems can follow natural language instructions to pick, place, sort, and manipulate a... ## Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading DevFeed: [Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-10T18:17:40Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [LLM Techniques](), [Large Language Model](), [GPU](), [Mixture of Experts (MoE)](), [cpu](), [NVLink](), [systems](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [blackwell](), [compute](), [cpu](), [developer-tools-techniques](), [featured](), [gb200](), [gpu](), [grace-cpu](), [large-language-model](), [llm](), [llm-techniques](), [mixture-of-experts-moe](), [nvidia](), [nvl72](), [nvlink](), [simulation-modeling-design](), [training-ai-models](), [vera-rubin](), [work]() ### AI overview This article explains how host offloading in JAX-based large language model training reduces GPU high-bandwidth memory pressure by moving selected activations to pinned host memory and streaming them back during the backward pass. It discusses activation-transfer overlap, NVIDIA Grace Blackwell and GB200 NVL72 systems, and experiments involving Llama 3.1 405B and DeepSeek-V3 671B. ### Source excerpt Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,... ## Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit DevFeed: [Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-10T13:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [cpu]() Tags: [agentic-ai-generative-ai](), [ai-agents](), [bionemo](), [cuda](), [developer-tools-techniques](), [drug-discovery](), [featured](), [gpu](), [healthcare-life-sciences](), [hpc-scientific-computing](), [inference](), [multi-gpu](), [nvidia](), [performance](), [simulation-modeling-design]() ### AI overview The article describes NVIDIA BioNeMo Agent Toolkit accelerations for biomolecular co-folding workflows, focusing on faster MSA generation, inference, serving, and multi-GPU scaling for drug-discovery workloads. ### Source excerpt Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein... ## A Practical Guide to GPU-Initiated Communication for Molecular Dynamics at Scale DevFeed: [A Practical Guide to GPU-Initiated Communication for Molecular Dynamics at Scale]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-09T17:15:04Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [cpu]() Tags: [communication](), [cpu](), [featured](), [gpu](), [gromacs](), [guide](), [kernel](), [latency](), [memory](), [networking-communications](), [performance](), [scalability](), [scale](), [simulation-modeling-design]() ### AI overview A practical guide to improving GROMACS molecular-dynamics scaling by replacing CPU-orchestrated MPI handoffs with GPU-initiated communication through NVSHMEM. ### Source excerpt Molecular dynamics (MD) simulations are among the most demanding workloads in computational science. Using them, researchers can observe atomic behavior in... ## Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T DevFeed: [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-07T17:05:42Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [Model Development](), [Open Source](), [Training AI Models](), [datasets](), [multimodal](), [Deployment](), [Simulation and Design](), [Tooling]() Tags: [ai-foundation-models](), [apache](), [building](), [data](), [deployment](), [developers](), [development](), [evaluation](), [featured](), [humanoid-robots](), [images](), [integrations](), [isaac](), [isaac-sim](), [manufacturing](), [model](), [multimodal](), [nvidia](), [open-source](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [software](), [tooling](), [training](), [workflows]() ### AI overview NVIDIA Isaac GR00T Development Platform unifies humanoid robot development workflows, combining data collection, simulation-based training, evaluation, and deployment. The article highlights the open Isaac GR00T 1.7 vision-language-action model, which accepts language and images and can be adapted to robots, tasks, and environments through post-training. ### Source excerpt As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids... [Next page]()