Positive<\/p>\n
+0.0%<\/p>\n
Planned Korea infrastructure expansion with NAVER and Brookfield<\/p>\n
Jul 23<\/p>\n
AI research partnership<\/a><\/p>\n Positive<\/p>\n -0.9%<\/p>\n Joint KAIST research lab with $300 million collaboration<\/p>\n Jul 20<\/p>\n Agent toolkit expansion<\/a><\/p>\n Positive<\/p>\n +0.2%<\/p>\n Added Omniverse libraries for simulation-ready 3D agent workflows<\/p>\n Jul 16<\/p>\n National AI infrastructure<\/a><\/p>\n Positive<\/p>\n -2.4%<\/p>\n Japan AI factory planned with 27,500 Rubin GPUs<\/p>\n Jul 15<\/p>\n Physical AI expansion<\/a><\/p>\n Positive<\/p>\n -2.4%<\/p>\n Japanese robotics and manufacturing leaders adopted physical AI stack<\/p>\n Pattern Detected<\/p>\n AI-tagged history was mostly divergent, with three negative reactions to positive announcements and an average move of -1.1%.<\/p>\n electronic design automation (eda)<\/p>\n technical<\/p>\n “central to electronic design automation (EDA) and scientific simulation”<\/p>\n Electronic design automation (EDA) is the set of computer tools and software used to create, test and prepare complex electronic circuits and chips before they are built. Think of it as digital blueprints and virtual factories that let engineers design, simulate and catch mistakes early, which shortens development time and reduces costly errors. For investors, EDA matters because it underpins semiconductor productivity and time-to-market, affecting costs, competitiveness and the pace of new product launches.<\/p>\n register-transfer level (rtl)<\/p>\n technical<\/p>\n “Chip design depends on specialized register-transfer level (RTL) coding”<\/p>\n A register-transfer level (RTL) is a way engineers describe how a digital chip moves and processes data by showing how information flows between storage points called registers and the logic that transforms it. Think of it like a flowchart for an electronic device that specifies the steps and timing for moving and changing data. RTL matters to investors because it is the core design stage that determines a chip\u2019s performance, power use, and how long and costly it will be to bring the product to market.<\/p>\n density functional theory (dft)<\/p>\n technical<\/p>\n “enabling density functional theory (DFT) and post-DFT methods”<\/p>\n A computational method used by scientists and engineers to predict how electrons are distributed in atoms, molecules and materials, which controls chemical reactions and physical properties. Like a virtual lab test or design tool, it lets researchers simulate new drugs, batteries or materials before building them, speeding development, cutting costs and reducing technical risk\u2014information that can influence project timelines, capital needs and a company\u2019s valuation.<\/p>\n preconditioners<\/p>\n technical<\/p>\n “its modern, composable solvers and preconditioners help developers”<\/p>\n Agents, treatments, or procedures used to prepare a patient, cells, or tissue before a main medical therapy (for example, cell or gene therapy). Like tiling soil before planting, preconditioners change the biological environment so the subsequent treatment can work more effectively or safely; they matter to investors because they can affect clinical trial outcomes, regulatory review, manufacturing complexity, development costs, and time to market.<\/p>\n AI-generated analysis. How Rhea-AI works<\/a>. Not financial advice.<\/p>\n
News Summary:<\/p>\n NVIDIA expands NVIDIA Agent Toolkit with re-architected NVIDIA PhysicsNeMo libraries and updated NVIDIA CUDA-X libraries, enabling software developers to build autonomous AI engineers with AI physics skills, accelerated solvers and quantum chemistry capabilities.NVIDIA Nemotron 3 Ultra leads among open models in agentic register-transfer level coding with the ACE-RTL agent from NVIDIA Research, helping enterprises build customizable AI agents for chip design and verification.Cadence, Siemens, Synopsys and other industry leaders are using NVIDIA accelerated computing and agentic AI technologies to advance autonomous engineering workflows across chip design, verification, packaging and systems. <\/p>\n LONG BEACH, Calif., July 26, 2026 (GLOBE NEWSWIRE) — NVIDIA today announced an expansion of NVIDIA Agent Toolkit for engineering, now adding NVIDIA PhysicsNeMo<\/a>\u2122 and CUDA-X<\/a>\u2122 libraries as agent-ready tools and skills built to transform how the world designs and develops products.<\/p>\n Building the next generation of chips and systems<\/a> requires teams to connect physics, simulation and performance analysis across increasingly complex design cycles. A new class of autonomous AI engineers is emerging to help take on that complexity \u2014 using specialized tools, running simulations and generating high-fidelity data to help scale chip design, verification, packaging and systems.\u00a0<\/p>\n Now included in NVIDIA Agent Toolkit<\/a>, NVIDIA has re-architected PhysicsNeMo into a set of agent-friendly libraries and added new and updated CUDA-X libraries to support complex engineering work. PhysicsNeMo provides AI physics skills for training and deploying models, while CUDA-X libraries bring accelerated solvers and quantum chemistry capabilities into agentic engineering workflows.<\/p>\n \u201cEngineering has reached an inflection point. AI can now work with tools of physics, simulation and design,\u201d said Timothy Costa, vice president and general manager of computational engineering at NVIDIA. \u201cWith NVIDIA Agent Toolkit, developers can build agentic engineers that reason using physics, run complex simulations and generate high-fidelity data to become a new engine for innovation in chip and system design.\u201d<\/p>\n NVIDIA Agent Toolkit Adds AI Physics and Accelerated Computing Skills for Engineering Agents Key capabilities include:<\/p>\n AI physics skills: NVIDIA PhysicsNeMo libraries help agents train and deploy customizable AI physics models for complex design and simulation tasks, turning model architectures into callable tools for engineering workflows.Iterative sparse solvers: New NVIDIA cuISS (CUDA Iterative Sparse Solvers) library accelerates large sparse linear systems in physics-based and engineering simulations. Designed for flexibility and performance on GPUs, its modern, composable solvers and preconditioners help developers build scalable, production simulation engines for agentic engineering workflows.\u00a0Direct sparse solvers: NVIDIA cuDSS<\/a> (CUDA Direct Sparse Solvers) accelerates large, complex sparse linear systems central to electronic design automation (EDA) and scientific simulation. It delivers high performance and numerical robustness for critical workloads like device, circuit and system simulations with scalability to multi-GPU and multi-node deployments in production environments.Quantum chemistry: NVIDIA cuEST<\/a> (CUDA Electronic Structure Theory) brings high-accuracy quantum chemistry simulations to device-relevant scales, enabling density functional theory (DFT) and post-DFT methods to be integrated into production workflows at scale. cuEST brings production value to customers by supporting a wide range of modern functionals and making increasingly large ground-state and excited-state simulations manageable on NVIDIA GPUs. <\/p>\n NVIDIA Nemotron 3 Ultra Open Model Advances Agentic Coding for Chip Design With ACE-RTL<\/a> \u2014 an agent for designing hardware from NVIDIA Research<\/a> \u2014 NVIDIA <\/a>Nemotron<\/a>\u2122 3<\/a> Ultra leads among open models in agentic RTL coding on the comprehensive verilog design problems benchmark<\/a> across RTL coding tasks.<\/p>\n This represents how Nemotron 3 Ultra offers industry-leading accuracy and efficiency and can be post-trained on proprietary data \u2014 deployed locally or on premises \u2014 giving enterprises greater control, customization and data privacy as they build AI agents for chip design.<\/p>\n Developers can get started with Nemotron 3 Ultra using Cadence\u2019s harness; Synopsys\u2019 fully autonomous, long-running agents for design verification and analog and mixed-signal workflows; Siemens\u2019 Questa One smart verification agentic toolkit; as well as on Hugging Face.<\/p>\n Software Leaders Build Autonomous AI Engineers With NVIDIA Cadence<\/a> is using NVIDIA Nemotron, accelerated computing and CUDA-X libraries with the recently launched Cadence AuraStack AI Super Agent and the Cadence Millennium M2000 platform to autonomously drive advanced packaging and printed circuit board (PCB) design from exploration through signoff, delivering up to 20x faster multiphysics performance. This joins Cadence\u2019s complete portfolio of silicon design super agents which collectively cover the chip design workflow end to end, from architecture through manufacturing signoff.<\/p>\n In addition, the\u00a0collaboration<\/a> extends from agentic design to the underlying compute as Cadence\u2019s portfolio of EDA and system design automation tools, including Cadence Jasper, a formal verification platform, is being optimized for the NVIDIA Vera CPU<\/a> to help engineering teams validate advanced chip designs faster.<\/p>\n Synopsys is using the NVIDIA Agent Toolkit, NVIDIA NIM\u2122 microservices, Nemotron open models, the NVIDIA NeMo\u2122 Gym library and NVIDIA NemoClaw\u2122 blueprints with Synopsys AgentEngineer to build secure, accelerated agentic workflows across chip and system design. Leveraging Ansys Icepak, Synopsys\u2019 agentic workflow autonomously executes simulation setup, and pre- and post-processing for complex GPU cooling design optimization. Synopsys is developing NVIDIA cuISS use cases to accelerate simulation workloads.<\/p>\n The collaboration extends from agentic workflows to the underlying compute platform as Synopsys VCS, a high-performance functional verification solution used to simulate and validate complex chip designs before fabrication, is being optimized for the NVIDIA Vera CPU to help improve verification throughput.<\/p>\n Siemens<\/a> is using NVIDIA NeMo Gym, Nemotron open models and CUDA-X libraries with the Siemens Fuse EDA AI Agent<\/a> to orchestrate multi-tool and multi-agent workflows across semiconductor, 3D-IC, PCB and system design, from conception through signoff. In Siemens Solido Characterization Suite, these agentic AI workflows are delivering more than 10x faster library characterization while reducing token costs by more than 10x.<\/p>\n Samsung is using NVIDIA cuLitho<\/a> and CUDA-X libraries to achieve up to 20x greater performance for computational lithography and applying NVIDIA PhysicsNeMo to perform chip-scale thermal-stress analysis with numerical solver-level accuracy across domains containing up to 10 billion cells.<\/p>\n ChipAgents<\/a> is using NVIDIA Agent Toolkit to build domain-specific AI agents for chip design and verification. The team is fine-tuning NVIDIA Nemotron models for complex end-to-end semiconductor design and verification workflows including debug, formal verification, coverage and more.<\/p>\n Silvaco is using NVIDIA accelerated computing to scale high-accuracy 3D optical simulation in the Silvaco Victory Device. Running on 32 NVIDIA GPUs interconnected by NVIDIA NVLink<\/a>\u2122 technology, it completed a 3.2-billion-mesh-node photonic edge coupler simulation in under four hours, a workload beyond the practical limits of CPU-based simulation.<\/p>\n Keysight is harnessing NVIDIA cuDSS to accelerate electromagnetic simulations by up to 10x, while Samsung, Synopsys and TSMC are integrating NVIDIA cuEST into its GPU-accelerated pipeline to achieve up to a 50x speedup for key quantum-chemistry workloads.<\/p>\n Learn more by joining\u00a0NVIDIA at DAC<\/a>.<\/p>\n About NVIDIA
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\n 07\/26\/2026 – 08:45 PM
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NVIDIA Agent Toolkit helps developers build specialized engineering AI assistants connected to domain-specific tools, models and data. With the addition of NVIDIA PhysicsNeMo and CUDA-X libraries, these agents can now use AI physics skills, accelerated solvers and quantum chemistry capabilities for chip, system and industrial engineering.\u00a0<\/p>\n
Chip design depends on specialized register-transfer level (RTL) coding, which demands high accuracy, deep domain expertise and flexibility over deployment.\u00a0<\/p>\n
Industrial engineering leaders are already using the new and expanded NVIDIA Agent Toolkit components to develop autonomous AI engineers.<\/p>\n
NVIDIA<\/a> (NASDAQ: NVDA<\/a>) is the world leader in AI and accelerated computing.<\/p>\n
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