Industrial AI is transforming how factories operate, innovate and scale.
The convergence of AI, simulation and digital twins is poised to unlock new levels of productivity, flexibility and insight for manufacturers worldwide — and NVIDIA’s collaboration with Siemens is bringing these technologies directly to factory and shop floors, making advanced automation more accessible.
Matthias Loskyll, head of virtual control and industrial AI at Siemens Factory Automation, joined the NVIDIA AI Podcast to discuss how Siemens’ work with NVIDIA is reshaping manufacturing, as the industry hits a turning point.
Manufacturing companies are facing a shortage of skilled labor, widening skills gaps as experts retire and increased demand for resilient, efficient production.
At the same time, AI advancements offer ways to automate tasks previously deemed too complex or variable for traditional programming — and digital twins open a path to designing and optimizing safe, efficient interactions between AI-powered robots and smart spaces.
Siemens’ Inspekto, an AI-driven visual quality inspection system, enables even small manufacturers to automate defect detection in their production lines. Inspekto can be trained in under an hour using as few as 20 product samples, making it ideal for fields like electronics and metal forming.
Meanwhile, automaker Audi is using industrial AI in its car body shops, where 5 million welds are made daily. Training AI models to automate weld-spot inspection and integrating them with Siemens’ Industrial AI Suite helped Audi achieve up to 25x faster inference directly on the shop floor, where the defects can be addressed.
Siemens is creating an AI-driven vision software enabling robots to handle arbitrary, previously unseen objects. The company is also developing Industrial Copilots with NVIDIA NIM microservices to bring generative AI-powered assistance directly to shopfloor operators and service technicians. Loskyll noted that the Industrial Copilots will run on premises to keep sensitive production data secure while enabling rapid troubleshooting and process optimization.
To learn more about the latest in industrial AI, watch the COMPUTEX keynote by NVIDIA founder and CEO Jensen Huang. Hear more from Siemens at NVIDIA GTC Paris, running June 10-12.
Time Stamps
1:00 – Overview of NVIDIA’s collaboration with Siemens.
5:00 – Challenges faced by manufacturing companies.
15:00 – How Inspekto makes automated visual quality inspection more accessible.
24:00 – How Audi achieved up to 25x faster inference with Siemens’ Industrial AI Suite.
37:00 – Future directions with industrial copilots and AI-enhanced robotics.
Yum! Brands, the parent company of KFC, Taco Bell, Pizza Hut and Habit Burger & Grill, is partnering with NVIDIA to streamline order taking, optimize operations and enhance service across its restaurants. Joe Park, chief digital and technology officer at Yum! Brands, Inc. and president of Byte by Yum!, shares how the company is further accelerating AI deployment.
Roboflow’s mission is to make the world programmable through computer vision. By simplifying computer vision development, the company helps bridge the gap between AI and people looking to harness it. Cofounder and CEO Joseph Nelson discusses how Roboflow empowers users in manufacturing, healthcare and automotive to solve complex problems with visual AI.
Agentic AI enables developers to create intelligent multi-agent systems that reason, act and execute complex tasks with a degree of autonomy. Jacob Liberman, director of product management at NVIDIA, explains how agentic AI bridges the gap between powerful AI models and practical enterprise applications.
Agentic AI is redefining scientific discovery and unlocking research breakthroughs and innovations across industries. Through deepened collaboration, NVIDIA and Microsoft are delivering advancements that accelerate agentic AI-powered applications from the cloud to the PC.
At Microsoft Build, Microsoft unveiled Microsoft Discovery, an extensible platform built to empower researchers to transform the entire discovery process with agentic AI. This will help research and development departments across various industries accelerate the time to market for new products, as well as speed and expand the end-to-end discovery process for all scientists.
Microsoft Discovery will integrate the NVIDIA ALCHEMI NIM microservice, which optimizes AI inference for chemical simulations, to accelerate materials science research with property prediction and candidate recommendation. The platform will also integrate NVIDIA BioNeMo NIM microservices, tapping into pretrained AI workflows to speed up AI model development for drug discovery. These integrations equip researchers with accelerated performance for faster scientific discoveries.
In testing, researchers at Microsoft used Microsoft Discovery to detect a novel coolant prototype with promising properties for immersion cooling in data centers in under 200 hours, rather than months or years with traditional methods.
Advancing Agentic AI With NVIDIA GB200 Deployments at Scale
Microsoft is rapidly deploying tens of thousands of NVIDIA GB200 NVL72 rack-scale systems across its Azure data centers, boosting both performance and efficiency.
Azure’s ND GB200 v6 virtual machines — built on a rack-scale architecture with up to 72 NVIDIA Blackwell GPUs per rack and advanced liquid cooling — deliver up to 35x more inference throughput compared with previous ND H100 v5 VMs accelerated by eight NVIDIA H100 GPUs, setting a new benchmark for AI workloads.
These innovations are underpinned by custom server designs, high-speed NVIDIA NVLink interconnects and NVIDIA Quantum InfiniBand networking — enabling seamless scaling to tens of thousands of Blackwell GPUs for demanding generative and agentic AI applications.
Microsoft chairman and CEO Satya Nadella and NVIDIA founder and CEO Jensen Huang also highlighted how Microsoft and NVIDIA’s collaboration is compounding performance gains through continuous software optimizations across NVIDIA architectures on Azure. This approach maximizes developer productivity, lowers total cost of ownership and accelerates all workloads, including AI and data processing — all while driving greater efficiency per dollar and per watt for customers.
NVIDIA AI Reasoning and Healthcare Microservices on Azure AI Foundry
Building on the NIM integration in Azure AI Foundry, announced at NVIDIA GTC, Microsoft and NVIDIA are expanding the platform with the NVIDIA Llama Nemotron family of open reasoning models and NVIDIA BioNeMo NIM microservices, which deliver enterprise-grade, containerized inferencing for complex decision-making and domain-specific AI workloads.
Developers can now access optimized NIM microservices for advanced reasoning in Azure AI Foundry. These include the NVIDIA Llama Nemotron Super and Nano models, which offer advanced multistep reasoning, coding and agentic capabilities, delivering up to 20% higher accuracy and 5x faster inference than previous models.
Healthcare-focused BioNeMo NIM microservices like ProteinMPNN,RFDiffusion and OpenFold2 address critical applications in digital biology, drug discovery and medical imaging, enabling researchers and clinicians to accelerate protein science, molecular modeling and genomic analysis for improved patient care and faster scientific innovation.
This expanded integration empowers organizations to rapidly deploy high-performance AI agents, connecting to these models and other specialized healthcare solutions with robust reliability and simplified scaling.
Accelerating Generative AI on Windows 11 With RTX AI PCs
Generative AI is reshaping PC software with entirely new experiences — from digital humans to writing assistants, intelligent agents and creative tools. NVIDIA RTX AI PCs make it easy to get it started with experimenting with generative AI and unlock greater performance on Windows 11.
At Microsoft Build, NVIDIA and Microsoft are unveiling an AI inferencing stack to simplify development and boost inference performance for Windows 11 PCs.
NVIDIA TensorRT has been reimagined for RTX AI PCs, combining industry-leading TensorRT performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to the more than 100 million RTX AI PCs.
Announced at Microsoft Build, TensorRT for RTX is natively supported by Windows ML — a new inference stack that provides app developers with both broad hardware compatibility and state-of-the-art performance. TensorRT for RTX is available in the Windows ML preview starting today, and will be available as a standalone software development kit from NVIDIA Developer in June.
Generative AI is transforming PC software into breakthrough experiences — from digital humans to writing assistants, intelligent agents and creative tools.
NVIDIA RTX AI PCs are powering this transformation with technology that makes it simpler to get started experimenting with generative AI and unlock greater performance on Windows 11.
NVIDIA TensorRT has been reimagined for RTX AI PCs, combining industry-leading TensorRT performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs.
Announced at Microsoft Build, TensorRT for RTX is natively supported by Windows ML — a new inference stack that provides app developers with both broad hardware compatibility and state-of-the-art performance.
For developers looking for AI features ready to integrate, NVIDIA software development kits (SDKs) offer a wide array of options, from NVIDIA DLSS to multimedia enhancements like NVIDIA RTX Video. This month, top software applications from Autodesk, Bilibili, Chaos, LM Studio and Topaz Labs are releasing updates to unlock RTX AI features and acceleration.
AI enthusiasts and developers can easily get started with AI using NVIDIA NIM — prepackaged, optimized AI models that can run in popular apps like AnythingLLM, Microsoft VS Code and ComfyUI. Releasing this week, the FLUX.1-schnell image generation model will be available as a NIM microservice, and the popular FLUX.1-dev NIM microservice has been updated to support more RTX GPUs.
Those looking for a simple, no-code way to dive into AI development can tap into Project G-Assist — the RTX PC AI assistant in the NVIDIA app — to build plug-ins to control PC apps and peripherals using natural language AI. New community plug-ins are now available, including Google Gemini web search, Spotify, Twitch, IFTTT and SignalRGB.
Accelerated AI Inference With TensorRT for RTX
Today’s AI PC software stack requires developers to compromise on performance or invest in custom optimizations for specific hardware.
Windows ML was built to solve these challenges. Windows ML is powered by ONNX Runtime and seamlessly connects to an optimized AI execution layer provided and maintained by each hardware manufacturer.
For GeForce RTX GPUs, Windows ML automatically uses the TensorRT for RTX inference library for high performance and rapid deployment. Compared with DirectML, TensorRT delivers over 50% faster performance for AI workloads on PCs.
TensorRT delivers over 50% faster performance for AI workloads on PCs than DirectML. Performance measured on GeForce RTX 5090.
Windows ML also delivers quality-of-life benefits for developers. It can automatically select the right hardware — GPU, CPU or NPU — to run each AI feature, and download the execution provider for that hardware, removing the need to package those files into the app. This allows for the latest TensorRT performance optimizations to be delivered to users as soon as they’re ready.
TensorRT performance optimizations are delivered to users as soon as they’re ready.
TensorRT, a library originally built for data centers, has been redesigned for RTX AI PCs. Instead of pre-generating TensorRT engines and packaging them with the app, TensorRT for RTX uses just-in-time, on-device engine building to optimize how the AI model is run for the user’s specific RTX GPU in mere seconds. And the library’s packaging has been streamlined, reducing its file size significantly by 8x.
TensorRT for RTX is available to developers through the Windows ML preview today, and will be available as a standalone SDK at NVIDIA Developer in June.
Developers looking to add AI features or boost app performance can tap into a broad range of NVIDIA SDKs. These include NVIDIA CUDA and TensorRT for GPU acceleration; NVIDIA DLSS and Optix for 3D graphics; NVIDIA RTX Video and Maxine for multimedia; and NVIDIA Riva and ACE for generative AI.
Top applications are releasing updates this month to enable unique features using these NVIDIA SDKs, including:
LM Studio, which released an update to its app to upgrade to the latest CUDA version, increasing performance by over 30%.
Topaz Labs, which is releasing a generative AI video model to enhance video quality, accelerated by CUDA.
Chaos Enscape and Autodesk VRED, which are adding DLSS 4 for faster performance and better image quality.
Bilibili, which is integrating NVIDIA Broadcast features such as Virtual Background to enhance the quality of livestreams.
NVIDIA looks forward to continuing to work with Microsoft and top AI app developers to help them accelerate their AI features on RTX-powered machines through the Windows ML and TensorRT integration.
Local AI Made Easy With NIM Microservices and AI Blueprints
Getting started with developing AI on PCs can be daunting. AI developers and enthusiasts have to select from over 1.2 million AI models on Hugging Face, quantize it into a format that runs well on PC, find and install all the dependencies to run it, and more.
NVIDIA NIM makes it easy to get started by providing a curated list of AI models, prepackaged with all the files needed to run them and optimized to achieve full performance on RTX GPUs. And since they’re containerized, the same NIM microservice can be run seamlessly across PCs or the cloud.
NVIDIA NIM microservices are available to download through build.nvidia.com or through top AI apps like Anything LLM, ComfyUI and AI Toolkit for Visual Studio Code.
During COMPUTEX, NVIDIA will release the FLUX.1-schnell NIM microservice — an image generation model from Black Forest Labs for fast image generation — and update the FLUX.1-dev NIM microservice to add compatibility for a wide range of GeForce RTX 50 and 40 Series GPUs.
These NIM microservices enable faster performance with TensorRT and quantized models. On NVIDIA Blackwell GPUs, they run over twice as fast as running them natively, thanks to FP4 and RTX optimizations.
The FLUX.1-schnell NIM microservice runs over twice as fast as on NVIDIA Blackwell GPUs with FP4 and RTX optimizations.
AI developers can also jumpstart their work with NVIDIA AI Blueprints — sample workflows and projects using NIM microservices.
NVIDIA last month released the NVIDIA AI Blueprint for 3D-guided generative AI, a powerful way to control composition and camera angles of generated images by using a 3D scene as a reference. Developers can modify the open-source blueprint for their needs or extend it with additional functionality.
New Project G-Assist Plug-Ins and Sample Projects Now Available
NVIDIA recently released Project G-Assist as an experimental AI assistant integrated into the NVIDIA app. G-Assist enables users to control their GeForce RTX system using simple voice and text commands, offering a more convenient interface compared to manual controls spread across numerous legacy control panels.
Developers can also use Project G-Assist to easily build plug-ins, test assistant use cases and publish them through NVIDIA’s Discord and GitHub.
The Project G-Assist Plug-in Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy to start creating plug-ins. These lightweight, community-driven add-ons use straightforward JSON definitions and Python logic.
New open-source plug-in samples are available now on GitHub, showcasing diverse ways on-device AI can enhance PC and gaming workflows. They include:
Gemini: The existing Gemini plug-in that uses Google’s cloud-based free-to-use large language model has been updated to include real-time web search capabilities.
IFTTT: A plug-in that lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights or smart shades, or pushing the latest gaming news to a mobile device.
Discord: A plug-in that enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay.
Explore the GitHub repository for more examples — including hands-free music control via Spotify, livestream status checks with Twitch, and more.
Companies are adopting AI as the new PC interface. For example, SignalRGB is developing a G-Assist plug-in that enables unified lighting control across multiple manufacturers. Users will soon be able to install this plug-in directly from the SignalRGB app.
SignalRGB’s G-Assist plug-in will soon enable unified lighting control across multiple manufacturers.
Starting this week, the AI community will also be able to use G-Assist as a custom component in Langflow — enabling users to integrate function-calling capabilities in low-code or no-code workflows, AI applications and agentic flows.
The G-Assist custom component in Langflow will soon enable users to integrate function-calling capabilities.
Enthusiasts interested in developing and experimenting with Project G-Assist plug-ins are invited to join the NVIDIA Developer Discord channel to collaborate, share creations and gain support.
Each week, the RTX AI Garageblog series features community-driven AI innovations and content for those looking to learn more about NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.
“ICRA has played a pivotal role in shaping the direction of robotics and automation, marking key milestones in the field’s evolution and celebrating achievements that have had a lasting impact on technology and society,” said Dieter Fox, senior director of robotics research at NVIDIA. “The research we’re contributing this year will further advance the development of autonomous vehicles and humanoid robots by helping close the data gap and improve robot safety and control.”
Generative AI for Scalable Robotic Learning
NVIDIA-authored papers showcased at ICRA give a glimpse into the future of robotics. They include:
DreamDrive: This 4D spatial-temporal scene generation approach creates realistic, controllable 4D driving scenes using video diffusion and 3D Gaussian splatting for autonomous vehicles.
DexMimicGen: This system can generate large-scale bimanual dexterous manipulation datasets from just a few human demonstrations.
HOVER: A unified neural controller for humanoid robots that seamlessly transitions between locomotion, manipulation and other modes.
MatchMaker: This pipeline automates generation of diverse 3D assembly assets for simulation-based training, enabling robots to learn insertion tasks without manual asset curation.
SPOT: This learning framework uses SE(3) pose trajectory diffusion for object-centric manipulation, enabling cross-embodiment generalization.
Electricity. The Internet. Now it’s time for another major technology, AI, to sweep the globe.
NVIDIA founder and CEO Jensen Huang took the stage at a packed Taipei Music Center Monday to kick off COMPUTEX 2025, captivating the audience of more than 4,000 with a vision for a technology revolution that will sweep every country, every industry and every company.
“AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories,” Huang said. “These factories are essentially what we build today.”
“They’re not data centers of the past,” Huang added. “These AI data centers, if you will, are improperly described. They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable, and these things are called tokens.”
NVIDIA CUDA-X Everywhere: After showing a towering wall of partner logos, Huang described how companies are using NVIDIA’s CUDA-X platform for a dizzying array of applications, how NVIDIA and its partners are building 6G using AI, and revealed NVIDIA’s latest work to accelerate quantum supercomputing.
NVIDIA CEO Jensen Huang called out NVIDIA partners across Taiwan, and the world, throughout his talk.
“The larger the install base, the more developers want to create libraries, the more libraries, the more amazing things are done,” Huang said, describing CUDA-X’s growing popularity and power. “Better applications, more benefits to users.”
More’s coming, Huang said, describing the growing power of AI to reason and perceive. That leads us to agentic AI — AI able to understand, think and act. Beyond that is physical AI — AI that understands the world. The phase after that, he said, is general robotics.
All of this has created demand for much more computing power. To meet those needs, Huang detailed the latest NVIDIA innovations from Grace Blackwell NVL72 systems to advanced networking technology, and detailed huge new AI installations from CoreWeave, Oracle, Microsoft, xAI and others across the globe.
“These are gigantic factory investments, and the reason why people build factories is because you know, you know the answer,” Huang said with a grin. “The more you buy, the more you make.”
Building AI for Taiwan: It all starts in Taiwan, Huang said, highlighting the key role Taiwan plays in the global technology ecosystem. But Taiwan isn’t just building AI for the world; NVIDIA is helping build AI for Taiwan. Huang announced that NVIDIA and Foxconn Hon Hai Technology Group are deepening their longstanding partnership and are working with the Taiwan government to build an AI factory supercomputer that will deliver state-of-the-art NVIDIA Blackwell infrastructure to researchers, startups and industries – including TSMC.
“Having a world-class AI infrastructure here in Taiwan is really important,” Huang said.
NVIDIA NVLink Fusion: And moving to help its partners scale up their systems however they choose, Huang announced NVLink Fusion, a new architecture that enables hyperscalers to create semi-custom compute solutions with NVIDIA’s NVLink interconnect.
This technology aims to break down traditional data center bottlenecks, enabling a new level of AI scale and more flexible, optimized system designs tailored to specific AI workloads.
“This incredible body of work now becomes flexible and open for anybody to integrate into,” Huang said.
Blackwell Everywhere: And the engine now powering this entire AI ecosystem is NVIDIA Blackwell, with Huang showing a slide explaining how NVIDIA offers “one architecture,” from cloud AI to enterprise AI, from personal AI to edge AI.
DGX Spark: Now in full production, this personal AI supercomputer for developers will be available in a “few weeks.” DGX Spark partners include ASUS, Dell, Gigabyte, Lenovo and MSI.
DGX Station: DGX Station is a powerful system with up to 20 petaflops of performance powered from a wall socket. Huang said it has the capacity to run a 1 trillion parameter model, which is like having your “own personal DGX supercomputer.”
NVIDIA RTX PRO Servers: Huang also announced a new line of enterprise servers for agentic AI. NVIDIA RTX PRO Servers, part of a new NVIDIA Enterprise AI Factory validated design, are now in volume production. Delivering universal acceleration for AI, design, engineering and business, RTX PRO Servers provide a foundation for NVIDIA partners to build and operate on-premises AI factories.
NVIDIA AI Data Platform: The compute platform is different, so the storage platform for modern AI is different. To that end, Huang showcased the latest NVIDIA partners building intelligent storage infrastructure with NVIDIA RTX 6000 PRO Blackwell Server Edition GPUs and the NVIDIA AI Data Platform reference design.
Physical AI: Agents are “essentially digital robots,” Huang said, able to “perceive, understand and plan.” To speed up the development of physical robots, the industry needs to train robots in a simulated environment. Huang said that NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics.
Huang introduced new tools to speed the development of humanoid robots: The Isaac GR00T-Dreams blueprint will help generate synthetic training data. And the Isaac GR00T N1.5 Humanoid Robot Foundation Model will power robotic intelligence.
Industrial Physical AI: Huang said that companies are in the process of building $5 trillion worth of factories worldwide. Optimizing the design of those factories is critical to boosting their output. Taiwan’s leading manufacturers — TSMC, Foxconn, Wistron, Pegatron, Delta Electronics, Quanta, GIGABYTE and others — are harnessing NVIDIA Omniverse to build digital twins to drive the next wave of industrial physical AI for semiconductor and electronics manufacturing.
NVIDIA Constellation: Lastly, building anticipation, Huang introduced a dramatic video showing NVIDIA’s Santa Clara office launching into space and landing in Taiwan. The big reveal: NVIDIA Constellation, a brand new Taiwan office for NVIDIA’s growing Taiwan workforce.
In closing, Huang emphasized that the work Taiwanese companies are doing has changed the world. He thanked NVIDIA’s ecosystem partners and described the industry’s opportunity as “extraordinary” and “once in a lifetime.”
“We are in fact creating a whole new industry to support AI factories, AI agents, and robotics, with one architecture,” Huang said.
Quantum computing promises to shorten the path to solving some of the world’s biggest computational challenges, from scaling in-silico drug design to optimizing otherwise impossibly complex, large-scale logistics problems.
Integrating quantum hardware into state-of-the-art AI supercomputers — forming accelerated quantum supercomputers — helps speed the scaling of today’s quantum processors into helpful devices for solving these complex challenges.
At the COMPUTEX trade show, NVIDIA underscored how its work with partners across the Taiwan supercomputing ecosystem is advancing quantum computing toward accelerated quantum supercomputers.
Leading hardware developers are working with NVIDIA to equip quantum researchers with tools to make significant contributions in the field.
Atlantic Quantum, the University of Edinburgh,, the University of Oxford, Quantum Circuits Inc., QuEra Computing and Yale University anticipate receiving NVIDIA Grace Hopper Superchips from Supermicro to explore and refine the intersections between AI supercomputing and quantum computing.
Compal announced its CGA-QX platform, built using the NVIDIA CUDA-Q platform, to accelerate the simulation of quantum optimization problems. The platform has been adopted by the Taiwanese National Science and Technology Council and made available to researchers from universities across Taiwan.
Quanta has employed NVIDIA CUDA-Q to experiment with physical quantum hardware, using the platform’s state vector simulations to verify and validate existing quantum processors. This allows Quanta to understand the details of noise in its systems and assess how well they work for use cases of interest.
NVIDIA is also working with supercomputing centers to advance accelerated quantum supercomputing.
NCHC is enabling over 20 companies working in quantum computing — collaborating under what’s known as the National Quantum Team. NVIDIA CUDA-Q is being used at the center to explore quantum solutions for applications ranging from machine learning to chemistry.
In Japan, AIST’s ABCI-Q — the world’s most powerful supercomputer dedicated to quantum workloads — integrates an NVIDIA supercomputer with more than 2,000 NVIDIA H100 GPUs with quantum processors from Fujitsu, QuEra Computing and OptQC.
Increased availability of these quantum-AI platforms is poised to accelerate the breakthroughs researchers can make in quantum computing — including developing new error correcting codes, integrating quantum processors within AI supercomputing and simulating low noise designs of quantum hardware.
Leading healthcare organizations across the globe are using agentic AI, robotics and digital twins of medical environments to enhance surgical precision, boost workflow efficiency, improve medical diagnoses and more.
Physical AI and humanoid robots in hospitals have the potential to automate routine tasks, assist with patient care and address workforce shortages.
This is especially crucial in places where challenges to optimal healthcare services are paramount. Such challenges include hospital overcrowding, an aging population, rising healthcare costs and a shortage of medical professionals, all of which are affecting Taiwan, as well as many other regions and countries.
At the COMPUTEX trade show in Taipei, NVIDIA today showcased how leading Taiwan medical centers are collaborating with top system builders to integrate smart hospital technologies and other AI-powered healthcare solutions that can help reduce these issues and save millions of lives.
Cathay General Hospital, Chang Gung Memorial Hospital (CGMH), National Taiwan University Hospital (NTUH) and Taichung Veterans General Hospital (TCVGH) are among the top centers in the region pioneering healthcare AI innovation.
Deployed in collaboration with leading system builders such as Advantech, Onyx, Foxconn and YUAN, these solutions tap into NVIDIA’s agentic AI and robotics technologies, including the NVIDIA Holoscan and IGX platforms, NVIDIA Jetson for embedded computing and NVIDIA Omniverse for simulating virtual worlds with OpenUSD.
CGMH Boosts AI-Powered Medical Imaging
With an average of 8.2 million outpatient visits and 2.4 million hospitalizations a year, CGMH estimates that a third of the Taiwanese population has sought treatment at its vast network of hospitals in Taipei and seven other cities.
The organization is pioneering smart hospital innovation by enhancing surgical precision and workflow efficiency through advanced, AI-powered colonoscopy workflow solutions, developed in collaboration with Advantech and based on the NVIDIA Holoscan platform, which includes the Holoscan SDK and the Holoscan Sensor Bridge running on NVIDIA IGX.
NVIDIA Holoscan is a real-time sensor processing platform for edge AI compute, while NVIDIA IGX offers enterprise-ready, industrial edge AI purpose-built for medical environments.
Using these platforms, CGMH is accelerating AI integration in its colonoscopy diagnostics procedures. Deployed in gastrointestinal consultation rooms, the AI-powered tool collects colonoscopy streams to train a customized model built on Holoscan and provides real-time colonic polyps identification and classification.
Colonoscopy tools at CGMH. Image courtesy of CGMH.
CGMH serves nearly 50 AI agent models that daily help the hospital analyze medical imaging, improving diagnostic accuracy, throughput and real-time inference at scale. For example, NVIDIA Triton-powered AI sped newborn examination record processing by 10x.
Cathay General Hospital Improves Diagnostics With AI
Cathay General Hospital, a Taipei-based healthcare center that provides hospital management and medical services, has worked with Onyx and software provider aetherAI to develop an AI-assisted colonoscopy system that highlights lesions, detects hard-to-spot polyps and issues alerts to help physicians with diagnoses.
Polyp detection during colonoscopy. Image courtesy of aetherAI and Onyx.
Powered by a compact, plug-and-play AI BOX device — built with the NVIDIA Jetson AGX Xavier module — the AI system is trained on over 400,000 high-quality, physician-annotated images collected from patients with diverse and severe lesions over four years.
Studies have shown the system can achieve up to 95.8% accuracy and sensitivity while improving adenoma detection rates by as much as 30%. These enhancements assist physicians in reducing diagnostic errors and making more informed treatment decisions, ultimately contributing to improved patient outcomes.
NTUH Detects Liver Tumors, Cardiovascular Risks With AI
In the 100+ years since its founding, NTUH has nurtured countless professionals in medicine and is renowned for its trusted clinical care. The national teaching hospital is now adopting AI imaging to more quickly, accurately diagnose patients.
NTUH’s HeaortaNet model, trained on more than 70,000 axial images from 200 patients, automates CT scan segmentation of the heart, including the aorta and other arteries, in 3D, enabling rapid analysis of risks for cardiovascular disease. The model, which achieves high segmentation accuracy for the pericardium and aorta, significantly reduced data processing time per case from an hour to about 0.4 seconds.
In addition, NTUH collaborated with the Good Liver Foundation and system builder YUAN to develop a diagnostic-assistance system for liver cancer detection during ultrasounds. It taps into an NVIDIA Jetson Orin NX module and a deep learning model trained on more than 5,000 annotated ultrasound images to identify malignant and benign liver tumors in real time.
YUAN and NTUH’s liver cancer detection system turns an ultrasound device into an AI-assisted diagnostic tool. Image courtesy of YUAN.
NVIDIA DeepStream and TensorRT SDKs accelerate the system’s deep learning model, ultimately helping clinicians detect tumors earlier and more reliably. In addition, NTUH is using NVIDIA DGX to train AI models for its system that detects pancreatic cancer from CT scans.
TCVGH Streamlines Multimodal Imaging and Clinical Documentation Workflows With AI
Taichung Veterans General Hospital (TCVGH), a medical center and a teaching hospital administered by the Veterans Affairs Council in Taipei, has partnered with Foxconn to build physical and digital robots to augment staffing, improving clinician productivity and patient experiences.
Foxconn developed an AI system that can analyze medical images and spot signs of breast cancer earlier than traditional methods, using NVIDIA Hopper GPUs, NVIDIA DGX systems and the MONAI framework. By tapping into clinical data and multimodal AI imaging, the system creates 3D virtual breast models, quickly highlighting areas of concern in scans to help radiologists make faster, more confident decisions.
Foxconn is also working with TCVGH to build smart hospital solutions like the AI nursing collaborative robot Nurabot and tapping into NVIDIA Omniverse to create real-time digital twins of hospital environments, including nursing stations, patient wards and corridors. These digital replicas serve as high-fidelity simulations where Jetson-powered service robots can be trained to autonomously deliver medical supplies throughout the hospital, ultimately improving care efficiency.
AI nursing collaborative robot Nurabot. Image courtesy of Foxconn.
In addition, TCVGH has developed and deployed its Co-Healer system, which integrates the Taiwanese native large language model TAIDE-LX-7B to streamline clinical documentation processes with agentic AI.
Co-Healer, built on the NVIDIA Jetson Xavier NX module, processes and helps summarize medical documents — such as nursing progress notes and health education materials — and supports medical exam preparation by providing students with instant access to nursing guidelines and patient-specific protocols for clinical procedures and diagnostic tests. This helps healthcare workers alleviate burnout while giving patients a clearer understanding of their diagnoses.
Learn more about the latest AI advancements in healthcare at NVIDIA GTC Taipei, running May 21-22 at COMPUTEX.
Researchers across Taiwan are tackling complex challenges in AI development, climate science and quantum computing. Their work will soon be boosted by a new supercomputer at Taiwan’s National Center for High-Performance Computing that’s set to deliver over 8x more AI performance than the center’s earlier Taiwania 2 system.
NCHC also plans to deploy a set of NVIDIA DGX Spark personal AI supercomputers and a cluster of NVIDIA HGX systems in the cloud.
Researchers from academic institutions, government agencies and small businesses in Taiwan will be able to apply for access to the new system to accelerate innovative projects.
“The new NCHC supercomputer will drive breakthroughs in sovereign AI, quantum computing and advanced scientific computation,” said Chau-Lyan Chang, director general of NCHC. “It’s designed to empower Taiwan’s technological autonomy, fostering cross-domain collaboration and global AI leadership.”
Developing Local Language Models for Sovereign AI
The new supercomputer will support projects like Taiwan AI RAP, a generative AI application development platform. Taiwan AI RAP aims to support the rapid development of AI products by offering startups, researchers and enterprises access to customized models that reflect local cultural and linguistic nuances.
Among the platform’s offerings are models created by Taiwan’s Trustworthy AI Dialogue Engine, or TAIDE — a public sector initiative to build Taiwanese large language models (LLMs) for tasks including natural language processing, intelligent customer service and translation.
Collaborators providing text, images, audio and video data for the initiative include local governments, news organizations and public departments such as the Ministry of Education and Ministry of Culture.
To support the creation of sovereign AI applications, TAIDE currently offers developers access to a collection of Llama3.1-TAIDE foundation models. The team is building additional sovereign AI LLM services using NVIDIA Nemotron models.
A professor at National Tainan University is using the TAIDE model to power a conversational AI robot that speaks Taiwanese and English with elementary and middle school students. It’s been used by over 2,000 students, teachers and parents to date. Another professor tapped the model to generate high-quality educational materials, shortening lesson preparation time for teachers.
In healthcare, research teams in Taiwan used the TAIDE model to develop an AI chatbot with retrieval-augmented generation that helps case managers deliver timely, accurate medical information to patients with major injuries and illnesses. And the Epidemic Prevention Center at Taiwan’s Centers for Disease Control is training the model to generate news summaries to support the tracking and prevention of how diseases spread.
Speeding Scientific Research in Climate and Beyond
In climate research, NCHC supports researchers using the NVIDIA Earth-2 platform to advance atmospheric science. These researchers are tapping Earth-2’s CorrDiff AI model to sharpen the precision of coarse-resolution weather models, and DeepMind’s GraphCast model in NVIDIA PhysicsNeMo for global weather forecasting.
They’re also adopting the NVIDIA NIM microservice for FourCastNet, an NVIDIA model that predicts global atmospheric dynamics of weather and climate variables, and using NVIDIA GPUs to accelerate the simulation of numerical weather prediction models.
With the new supercomputer, the researchers will be able to run more complex simulations and accelerate the pace of AI training and inference.
Advancing Quantum Innovation
NCHC researchers are also advancing quantum research using the NVIDIA CUDA-Q platform and NVIDIA cuQuantum library targeting applications in quantum machine learning, chemistry, finance, cryptography and more.
The research institute has developed Quantum Molecular Generator, a tool that generates valid chemical molecules, using quantum circuits and the CUDA-Q platform. It’s also created cuTN-QSVM, an open-source tool built on the cuQuantum library that accelerates large-scale quantum circuit simulations.
The tool enables researchers to tackle more complex problems, offering linear scalability and supporting hybrid quantum computing systems to help accelerate the development of large-scale quantum algorithms.
NCHC researchers recently used cuTN-QSVM to perform a record-breaking 784-qubit simulation for a quantum machine learning algorithm. The institute also plans to build a hybrid quantum-accelerated computing system by integrating NVIDIA DGX Quantum systems.
Learn more about Taiwan’s National Center for High-Performance Computing at NVIDIA GTC Taipei, taking place May 21-22.
Watch the COMPUTEX keynote by NVIDIA founder and CEO Jensen Huang.
NVIDIA is highlighting significant momentum for its new Grace CPU C1 this week at the COMPUTEX trade show in Taipei, with a strong showing of support from key original design manufacturer partners.
The expanding NVIDIA Grace CPU lineup, including the powerful NVIDIA Grace Hopper Superchip and the flagship Grace Blackwell platform, is delivering significant efficiency and performance gains for major enterprises tackling demanding AI workloads.
As AI continues its rapid advancement, power efficiency has become a critical factor in data center design for applications ranging from large language models to complex simulations.
The NVIDIA Grace architecture is directly addressing this challenge.
NVIDIA Grace Blackwell NVL72, a rack-scale system integrating 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell GPUs, is being adopted by major cloud providers to accelerate AI training and inference, including complex reasoning and physical AI tasks.
The NVIDIA Grace architecture now comes in two key configurations: the dual-CPU Grace Superchip and the new single-CPU Grace CPU C1.
The C1 variant is gaining significant traction in edge, telco, storage and cloud deployments where maximizing performance per watt is paramount.
The Grace CPU C1 boasts a claimed 2x improvement in energy efficiency compared with traditional CPUs, a vital advantage in distributed and power-constrained environments.
Leading manufacturers like Foxconn, Jabil, Lanner, MiTAC Computing, Supermicro and Quanta Cloud Technology support this momentum, developing systems using the Grace CPU C1’s capabilities.
In the telco space, the NVIDIA Compact Aerial RAN Computer, which combines the Grace CPU C1 with an NVIDIA L4 GPU and NVIDIA ConnectX-7 SmartNIC, is gaining traction as a platform for distributed AI-RAN, meeting the power, performance and size requirements for deployment at cell sites.
NVIDIA Grace is also finding a home in storage solutions, with WEKA and Supermicro deploying it for its high performance and memory bandwidth.
ExxonMobil is using Grace Hopper for seismic imaging, crunching massive datasets to gain insights on subsurface features and geological formations.
Meta is deploying Grace Hopper for ad serving and filtering, using the high-bandwidth NVIDIA NVLink-C2C interconnect between the CPU and GPU to manage enormous recommendation tables.
High-performance computing centers such as the Texas Advanced Computing Center and Taiwan’s National Center for High-Performance Computing are using the Grace CPU in their systems for AI and simulation to advance research.
Learn more about the latest AI advancements at NVIDIA GTC Taipei, running May 21-22 at COMPUTEX.
“Our collaboration with NVIDIA represents a significant advancement in semiconductor process simulation,” said Jeff Wu, fellow and director for the technology computer-aided design division at TSMC. “The computational acceleration from CUDA-X libraries and NVIDIA Grace Blackwell will expedite process development by simulating complex manufacturing processes and device behaviors at lower cost.”
NVIDIA cuLitho and Blackwell speed up lithography by up to 25x. GPU acceleration enables leading lithography providers and semiconductor manufacturers such as TSMC to predict and correct lithography issues before production at an unprecedented speed.
Earlier this month, electronic design automation (EDA) software and services provider Cadence announced its Millennium M2000 platform, built exclusively on NVIDIA Blackwell for the EDA market. The M2000 is a scalable turnkey solution for deploying NVIDIA Grace Blackwell and CUDA-X libraries with a fully accelerated portfolio of Cadence design tools.
Cadence is also one of the first to adopt NVIDIA NVLink Fusion, enabling custom silicon scale-up to meet the requirements of demanding workloads for model training and agentic AI inference. By adopting NVLink Fusion, Cadence allows hyperscalers to optimize and validate across the entire design spectrum.
This month, Cadence announced the Millennium M2000 AI Supercomputer to transform silicon, system and drug design. Based on the NVIDIA Blackwell platform, options include the NVIDIA GB200 NVL72 system for tackling massive system-on-a-chip, 3D-IC, and subsystem implementation and signoff using Cadence Cerebrus AI Studio and Cadence multiphysics system analysis tools, as well as the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPU for smaller chip designs and simulations.
“Our collaboration with NVIDIA has always been about pushing the boundaries of what’s possible in both electronic design automation and system design and analysis,” said Michael Jackson, corporate vice president and general manager of the system design and analysis group at Cadence. “The Millennium M2000 platform, built exclusively on NVIDIA Blackwell, isn’t just about faster simulation — it’s about redefining the infrastructure for AI-driven innovation, enabling what was previously impossible.”
Siemens is harnessing the parallel processing power of the NVIDIA CUDA-X libraries and the groundbreaking performance of the Grace Blackwell platform to significantly accelerate its Calibre platform.
This integration enables unprecedented speed and accuracy in critical semiconductor manufacturing steps, including optical proximity correction with nanometer precision, comprehensive physical verification, robust design for manufacturability analysis, thorough reliability verification and seamless integration and automation across the design-to-manufacturing flow.
“Leveraging NVIDIA CUDA-X and Grace Blackwell in our Calibre platform enables faster, more efficient optical proximity correction without sacrificing accuracy for advanced semiconductor nodes,” said Mike Ellow, CEO of Siemens EDA. “This is especially important as chip complexity continues to grow.”
Additionally, Synopsys, a leading EDA software and services provider, is using NVIDIA CUDA-X libraries and Blackwell for its EDA tools, including Synopsys PrimeSim, Proteus, S-Litho, Sentaurus Device and QuantumATK. By integrating with CUDA-X libraries, Synopsys achieved new benchmark results for Sentaurus Device, QuantumATK, and S-Litho on the NVIDIA B200, demonstrating a 12x, 15x and 20x scale-up, respectively, versus comparable CPU infrastructure.
In addition, Synopsys recentlyannounced at NVIDIA GTC that they project Synopsys PrimeSim to run 30x faster and Synopsys Proteus to run 20x faster on NVIDIA Blackwell platforms.
“Synopsys has a long history of collaborating with NVIDIA on accelerating our EDA solutions to maximize the capabilities of engineering teams. Building on our industry-first approach, Synopsys is leveraging NVIDIA’s Blackwell architecture across our TCAD, computational lithography and atomistic simulation products to unlock unprecedented performance gains,” said Sanjay Bali, senior vice president of strategy and product management at Synopsys. “By integrating NVIDIA’s CUDA-X libraries and Blackwell architecture into our industry-leading simulation solvers, we’ve achieved transformative speedups and redefined how EDA is enabling semiconductor manufacturing innovation.”
Semiconductor process control equipment manufacturer KLA and NVIDIA have worked together for over a decade to advance KLA’s physics-based AI with optimized high-performance computing solutions that tap into GPUs and the CUDA ecosystem.
The value of process control in semiconductor manufacturing is increasing due to AI-driven trends, such as more complex designs, accelerated product cycles, higher value wafer volumes and growing advanced packaging demand. KLA’s industry-leading inspection and metrology systems capture and process images by running complex AI algorithms to find the most critical semiconductor defects at lightning-fast speeds.
KLA is looking forward to evaluating the NVIDIA RTX PRO 6000 Blackwell Server Edition with CUDA-X libraries for certain markets to further accelerate inference workloads powering the semiconductor chip manufacturing process.
By embedding NVIDIA Blackwell into EDA, manufacturing and process control, NVIDIA is helping the semiconductor industry deliver the next generation of high-performance chips faster.
Learn more about the latest AI advancements at NVIDIA GTC Taipei, running May 21-22 at COMPUTEX.
