NVIDIA GPUs with Confidential Computing are now used for confidential inference in Apple’s Private Cloud Compute (PCC), as it expands beyond Apple’s data centers to Google Cloud. 

Unveiled during Apple’s annual WWDC gathering for developers from around the globe, NVIDIA GPUs will support server-side inference for Apple Foundation Models, custom-built by Apple and Google, leveraging the technologies behind the Gemini family of models.

NVIDIA is collaborating with Apple and Google to support some of the next-generation Apple Intelligence features, using NVIDIA Blackwell GPUs with Confidential Computing integrated into Private Cloud Compute’s hardware security architecture running on Google Cloud.

Confidential Computing Matters for the Era of AI Experiences 

NVIDIA Confidential Computing provides a hardware-based security layer for accelerated AI workloads. The technology protects data while it’s being processed by isolating workloads in trusted execution environments and enabling systems to cryptographically verify that the infrastructure has not been tampered with before any sensitive data is sent to the server. 

For end users, NVIDIA Confidential Computing means that no one, not even the system’s builders, can look at their data, chats or conversations.

Adoption of NVIDIA Confidential Computing at this scale reflects a broader shift in AI infrastructure: As AI experiences combine on-device and cloud-based processing for their tasks, there’s a need for high-performance, server-side inference while maintaining strong privacy and security guarantees. 

How Confidential Computing Enforces Privacy and Trust

NVIDIA Confidential Computing reflects NVIDIA’s commitment to trustworthy AI and includes these key capabilities:

• Hardware-rooted trust, helping establish that systems are running on genuine, untampered NVIDIA GPUs.
• Encrypted communication paths, helping protect data as it moves between components.
• Remote attestation, enabling software to verify the security state of the platform before releasing sensitive data.
• Support for accelerated AI inference and training, helping organizations run privacy-sensitive workloads without moving away from GPU performance.

These capabilities are increasingly relevant for AI services that need to process sensitive information while maintaining strong user privacy controls.

Learn more about NVIDIA Confidential Computing and NVIDIA AI cybersecurity solutions. 

A year ago at London Tech Week, NVIDIA founder and CEO Jensen Huang and U.K. Prime Minister Keir Starmer made a declaration: the U.K. would be an AI maker, not an AI taker. 

At this year’s event, NVIDIA and its partners are showcasing how that commitment is producing real momentum across the nation’s infrastructure, startups and enterprises. 

U.K. technology leaders are innovating across healthcare and life sciences, coding, agentic AI, inference and more — all running on sovereign AI deployments.

“A year ago, we said the U.K. would be an AI maker, not an AI taker,” said U.K. AI Minister Kanishka Narayan. “Today we’re delivering on that — with sovereign compute powering British startups to push the boundaries of what AI can do, from drug discovery to healthcare to robotics. This is what it looks like when a country backs its own talent with the infrastructure to match. 

“NVIDIA’s decision to invest billions here is a reflection of the strength of what’s being built in Britain,” he added. “We are determined to make sure the next generation of AI breakthroughs happens in this country, and we have everything we need to make it happen.”

Commitment to Compute

Over the past year, the number of AI cloud providers planning to deploy AI infrastructure on U.K. soil has doubled. 

Nebius has announced plans to expand customers and cloud capabilities with three new deployments of advanced NVIDIA AI infrastructure, as the NVIDIA AI Cloud ecosystem partner continues to build out its commercial and AI R&D hub in London. Combined, the deployments are expected to reach 65 megawatts when fully ramped up in 2027.

CoreWeave is building in the U.K. Government’s AI Growth Zones, and seven more NVIDIA AI Cloud ecosystem partners have plans in the pipeline. BT and Nscale announced plans to build sovereign AI data centers across three existing BT sites in the U.K., combining NVIDIA AI infrastructure, Nscale’s full stack and BT’s trusted nationwide connectivity backbone. 

From Fund to Frontier

Central to that sovereign compute story is Isambard-AI — the U.K.’s most powerful computer. Built on 5,400 NVIDIA GH200 Grace Hopper Superchips and running entirely on zero-carbon electricity, it’s the engine behind some of the U.K.’s most ambitious AI research. 

The U.K. government’s Sovereign AI Fund is putting that capability to work by backing homegrown companies and providing the domestic infrastructure needed to scale their ambitions. 

Among its first recipients is Ineffable Intelligence, which recently announced a collaboration with NVIDIA to build the future of reinforcement learning infrastructure. 

Other recipients include four U.K.-based NVIDIA Inception startups, each pushing the AI frontier using Isambard-AI. These startups are:

Cosine Builds Sovereign Coding Platform

Cosine is building an end-to-end sovereign AI coding platform for highly regulated industries such as financial services, critical infrastructure and national security. Using Isambard, Cosine is training a new, large-parameter, mixture-of-experts, multimodal agentic LLM for natively handling data types beyond text and image. 

“Access to Isambard enables the project, full stop,” said Alistair Pullen, cofounder and CEO of Cosine. “We already have the people who know how to do this. We have the data. We have the infrastructure and the training. The thing we’ve never had is this level of compute.”

Cursive Trains Self-Improving AI Systems

Cursive is building self-improving AI systems that learn continuously from real-world data, enabling them to operate autonomously over long periods of time. This is unlocked through new memory-augmented architectures with dramatically larger context windows, currently in development using the Sovereign AI Fund resources. In addition, the team recently adopted the NVIDIA Megatron-LM framework for distributed training at scale.

“The Sovereign AI Fund is more than just processing power — it’s a statement about investing in AI in the U.K.,” said Talfan Evans, cofounder and CEO of Cursive. “Sovereignty is actually now a buying criterion — and it’s a challenge to tap into the resources we uniquely have as U.K. and European companies.”

Doubleword Optimizes Inference to Deliver Abundant Intelligence Tokens

Doubleword, the U.K.’s first dedicated inference lab, optimizes every layer of the AI stack to maximize what it calls “IQ per dollar.” The company deploys open models including NVIDIA Nemotron 3 Super 120B and builds on the NVIDIA Dynamo inference framework. 

On Isambard, Doubleword’s early results achieved 70x faster model cold starts — aka model loading times — and 4x lossless KV cache compression, critical advancements for long-running agentic workloads. The result: inference at 90-95% lower costs than other leading inference providers.

“Sovereign AI is most impactful at the inference layer,” said Meryem Arik, cofounder and CEO of Doubleword. “Inference is when you’re actually getting the value from the model — we want that value created in the U.K., with U.K. compute and U.K. data centers.”

Prima Mente Uses Foundation Models to Study Alzheimer’s and More

Prima Mente builds biological foundation models to identify new biomarkers, subtypes and drug targets of Alzheimer’s, Parkinson’s and ALS. With its Isambard allocation, the company is developing Pleiades 2, a foundation model combining five biological data modalities. 

Achieving nearly 3x speedups in model training with NVIDIA Blackwell GPUs, Prima Mente also uses NVIDIA Parabricks for genomic data processing and NVIDIA Transformer Engine for model optimization.

“Research shows Alzheimer’s might be 25 different subgroups of disease, and we want to help by using AI to identify these subtypes and the biology within the cells as they change,” said Hannah Madan, cofounder of Prima Mente.

Video courtesy of Nebius and Prima Mente.

AI Talent, Policy and Production

NVIDIA’s £2 billion investment in the U.K. startup ecosystem — in collaboration with leading venture capital firms — is bringing new capital and advanced AI infrastructure to major U.K. hubs including London, Oxford, Cambridge and Manchester. 

U.K. membership in the NVIDIA Inception program has increased by 50% over the past year. AI-native companies like Doubleword, Synthesia and PolyAI are scaling globally from U.K. roots. 

At last year’s London Tech Week, NVIDIA announced a collaboration with the U.K Department for Science, Innovation and Technology on 6G and AI skills. The 6G collaboration has seeded testbeds at four U.K. universities. In May, the NVIDIA Deep Learning Institute (DLI) delivered two new courses — added to support the nation’s wireless research community — to participants from over 30 U.K. universities.

Plus, as part of this AI skills collaboration, NVIDIA DLI courses are offered as part of QA’s AI Apprenticeships in England. 

And the NVIDIA Developer Program now includes more than 200,000 U.K. developers. 

The Sovereign AI Forum, which launched last year with seven charter members, convened the country’s AI leadership to turn policy into deployment roadmaps. Over the past year, the Forum has welcomed dozens of participants across government, industry and the startup community — turning policy into deployment roadmaps.

And enterprise AI is moving from pilot to production:

• Apian is building digital twins of two National Health Service hospitals, combining autonomous devices, ground robots, computer vision and robotic simulation.
• Deliverance AI is helping regulated enterprises to run, govern and scale AI agents inside their own environment — through a single control plane. The Agentic Operating System is built for organizations where data sovereignty is non-negotiable.
• Glass Futures has installed an AI-driven digital twin of its glass furnace capable of testing and predicting new, optimal ways to make glass. The digital twin taps into NVIDIA accelerated computing and the NVIDIA PhysicsNeMo framework.
• OneAdvanced is fine-tuning NVIDIA Nemotron 2 Nano 9B with the NeMo AutoModel for its AI-consultation and triage app with sovereign, real world NHS Primary Care patient triage data.
• Orbital Industries has announced codesigned, NVIDIA Vera Rubin DSX AI Factory-compliant AI infrastructure that accelerates time to first token.
• Reading Football Club is partnering with Stelia to establish an AI Centre of Excellence, combining Stelia’s full-stack AI platform with accelerated compute infrastructure from NVIDIA and Lenovo.

It all reflects momentous progress in U.K. AI leadership — and offers a glimpse of where it’s heading.

Join NVIDIA at London Tech Week.

NVIDIA and LG Group are building an AI factory to accelerate LG Group’s next wave of AI-driven businesses, spanning robotics, autonomous driving, data center technologies and GPU cloud services.

The AI factory will provide LG Group with accelerated computing infrastructure to train, simulate, validate and deploy AI-based applications across its key businesses. 

The collaboration brings together NVIDIA’s full-stack, end-to-end AI factory platform with LG Group’s global leadership in consumer electronics, robotics, mobility components, smart spaces and data center technologies.

Together, the companies are connecting AI model development, physical AI data generation, robot simulation and training, edge deployment and factory-scale digital twins into a unified workflow for building physical AI systems. 

Advancing Physical AI and Robotics

The combination of LG’s production technology data and know-how from global manufacturing sites with NVIDIA’s AI infrastructure and digital twin technologies will help enhance AI-driven manufacturing AI competitiveness. The two companies will collaborate to build an autonomous manufacturing ecosystem in which the entire process — from raw material procurement to production, logistics and customer delivery — is connected in real time through data and AI, and establish it as a new global smart factory standard.

LG Electronics is developing home-based robots like CLoiD to help with a wide range of indoor household tasks, enhancing everyday convenience and improving quality of life. 

By integrating the NVIDIA Isaac Sim and NVIDIA Isaac Lab open robotics frameworks into their development workflows, LG can simulate, train and validate these home cobots in physically accurate virtual environments before deployment. 

The company is exploring using the NVIDIA Isaac GR00T open, reasoning vision action language model for both its home robots and modular robotics platforms. The GR00T model will provide LG robots humanlike reasoning and the ability to execute complex tasks. NVIDIA and LG Electronics also plan to jointly develop reference robots, positioning LG’s robots as part of the NVIDIA Isaac GR00T ecosystem.

To help overcome the training data challenge for robotics, LG Electronics is developing a physical AI data factory poised to help Korean and global companies accelerate physical AI projects. By turning compute into data, LG will be providing high-quality training data for robotics and industrial AI projects, using NVIDIA Cosmos world foundation models for synthetic data generation and augmentation.

LG Innotek, harnessing its world-class optical expertise, plans to provide state-of-the-art robotics components, including sensing solutions, specifically optimized for NVIDIA’s development environments and GPU architecture.

LG CNS is building an ecosystem that enables anyone to easily adopt AI robots in manufacturing and logistics sites. By integrating NVIDIA’s robotics technologies including Isaac open robotics frameworks, NVIDIA Cosmos open world models and Isaac GR00T robotic foundation models into its PhysicalWorks industrial robot platform, the company is accelerating the AI transformation of logistics and manufacturing floors.

Building an NVIDIA DSX-Aligned AI Factory Infrastructure 

The two companies will also expand cooperation in the field of next-generation AI factories, which will support the AI era.

Beyond its certification cooperation with NVIDIA on cooling solutions for AI factory thermal management — including cooling distribution units (CDUs) and cold plates — LG Electronics is further elevating its AI factory capabilities through technical collaboration on prefabricated modular design technologies. This initiative aligns with the NVIDIA DSX AI factory platform, enabling the rapid deployment of scalable, high-performance supercomputing infrastructure.

These technologies include CDUs, cold plates and prefab modular design capabilities to help address the power, thermal and deployment requirements of next-generation liquid-cooled AI factories.

In collaboration with LG Electronics and LG Energy Solution, LG Uplus — a telecommunications provider under LG Corp. — plans to build scalable, power-efficient AI factories based on NVIDIA DSX. The effort is expected to combine NVIDIA accelerated computing and AI factory reference architectures with LG’s infrastructure, energy and telecommunications capabilities to support future AI cloud and GPU service opportunities. 

LG CNS plans to build scalable, power-efficient, high-performance AI factories powered by NVIDIA GPUs based on NVIDIA DSX.

LG Uplus plans to build a large-scale AI data center capable of accommodating the latest NVIDIA GPUs.

LG Energy Solution plans to collaborate with NVIDIA on emerging 800 volt-direct-current data center energy solutions, in alignment with NVIDIA’s BESS Self-Qualification guidelines, to keep pace with next-generation GPUs. 

Accelerating Autonomous Driving and Mobility AI

In mobility, LG Electronics works with NVIDIA to align its advanced driver-assistance systems (ADAS) and in-vehicle AI systems with the NVIDIA DRIVE platform. 

The collaboration will focus on aligning sensor, compute and software architectures with the NVIDIA DRIVE Hyperion architecture, supporting LG Electronics’ roadmap for autonomous driving, ADAS and software-defined vehicles. 

LG Electronics also plans to use NVIDIA DRIVE AGX accelerated compute for its future mobility applications, including AI-powered cockpits and edge AI processing. Through this work, LG Electronics aims to strengthen its automotive electronics portfolio and accelerate the development of AI-driven mobility solutions for global manufacturers.

LG Innotek is rapidly cementing its leadership in the autonomous driving market, using its core portfolio of world-class sensing, connectivity and lighting solutions. LG Innotek plans to collaborate with NVIDIA on next-generation components engineered specifically for NVIDIA architecture. 

Advancing Sovereign AI With EXAONE

NVIDIA and LG AI Research are collaborating to advance EXAONE, one of Korea’s leading sovereign AI models and an open model family available to developers, enterprises and researchers. 

LG AI Research used NVIDIA Blackwell GPUs, NVIDIA NeMo framework and NVIDIA Nemotron open datasets to support EXAONE model development, as well as NVIDIA TensorRT-LLM software to build high-performance inference engines for optimized deployment.

LG Group is exploring broader adoption of EXAONE and agentic AI technologies across its businesses through platforms such as ChatEXAONE — LG Group’s EXAONE-based enterprise chatbot service. NVIDIA will help power LG AI Research’s sovereign AI models, so LG Group can accelerate enterprise AI transformation, software-defined operations and productivity across its business portfolio. 

Learn more about the NVIDIA DSX platform.

Featured image courtesy of LG Group.

NVIDIA and Doosan Group are expanding their collaboration to advance new opportunities across physical AI, robotics and AI factory infrastructure, spanning Doosan Robotics, Doosan Bobcat, Doosan Enerbility and Doosan Corporation Electro-Materials BG.

The collaboration will bring together NVIDIA’s full-stack accelerated computing platforms with Doosan Group’s capabilities in industrial automation, power generation and advanced electronics materials to support next-generation AI infrastructure.

Doosan Group’s businesses span several layers of the AI factory ecosystem, from intelligent robotics systems to the full spectrum of large-scale power solutions and advanced electronics materials for AI data center equipment. 

NVIDIA and Doosan will explore how NVIDIA’s physical AI stack, NVIDIA DSX AI factory platform, NVIDIA MGX and accelerated computing platforms can support these areas.

Advancing Physical AI and Robotics

Doosan Robotics is integrating NVIDIA Isaac Sim and NVIDIA Isaac Lab open robotics frameworks, NVIDIA Cosmos open world foundation models, the open source Newton physics engine and NVIDIA Jetson Thor to advance its Agentic Robot OS — an AI-powered platform connecting perception, reasoning, simulation, learning and on-device inference. 

By integrating NVIDIA’s physical AI technologies, Doosan Robotics aims to help industrial robots better perceive, reason and act in complex and dynamic environments. Simulation-to-real workflows, physics calibration and AI reasoning will make collaborative robots more adaptable, task-specialized and ready for scalable deployment. 

The companies are also looking to develop reference use cases for high-value industrial tasks such as depalletizing and sanding, as well as new robot form factors including dual-arm and humanoid platforms.

Built on Agentic Robot OS, these capabilities aim to help Doosan Robotics evolve from a robot arm provider into a full-stack AI-first robotics solution company. The work is part of a broader, Doosan Group-wide direction for physical AI that extends beyond robotics into areas such as construction machinery and power equipment.

Doosan Bobcat also plans to explore integrating NVIDIA physical AI technologies into equipment used across construction, landscaping, agriculture and material handling applications. This work will help accelerate the development of specialized world models that enable Doosan Bobcat’s equipment to perceive diverse operating environments, reason about changing conditions and perform tasks more autonomously. The companies also aim to help establish an industry-standard ecosystem for compact autonomous equipment.

Exploring AI Factory Power Solutions

Doosan Enerbility is exploring opportunities to support NVIDIA AI factories and the NVIDIA DSX AI factory platform through its large-scale power infrastructure portfolio, including gas turbines, steam turbines and small modular reactors, together with Doosan Fuel Cell’s hydrogen fuel-cell systems. These technologies are relevant to AI data centers that require reliable, high efficiency and continuously available power.

Future collaboration could include power supply design for AI factory deployments, optimization of generation equipment and evaluation of low-carbon power sources such as small modular reactors. By aligning AI infrastructure requirements with energy system expertise, Doosan Enerbility could help address the growing power demands of accelerated computing.

Supporting the NVIDIA MGX Ecosystem With Advanced PCB Materials

Doosan Corporation Electro-Materials BG is supporting next-generation AI data center infrastructure through copper clad laminate, or CCL, a key foundational material for printed circuit boards. 

High-performance CCLs are used in printed circuit boards (PCBs) for networking equipment, AI accelerators and AI server motherboards, where low signal loss and high reliability are critical.

NVIDIA MGX provides a modular reference architecture for accelerated systems, helping system manufacturers and ecosystem partners build servers and rack-scale AI factory infrastructure. As AI servers and networking systems increase in performance and bandwidth, advanced PCB materials such as CCL can play an important role in enabling high-speed signal integrity across the data center equipment ecosystem.

Learn more about NVIDIA DSX and MGX.

Featured image courtesy of Doosan Group.

At GTC Taipei at COMPUTEX last week, NVIDIA unveiled RTX Spark, the superchip that reinvents Windows PCs for the era of personal AI agents. On the heels of this announcement, NVIDIA founder and CEO Jensen Huang headed to South Korea, where he introduced RTX Spark to the nation’s passionate gaming community.

Leading game developers — including Korea’s KRAFTON and NC — are already working to bring their titles to RTX Spark-powered systems. 

Designed for local AI, creating and gaming, RTX Spark brings together 30 years of NVIDIA innovation to slim Windows laptops with all-day battery life and small, ultraefficient desktop PCs. 

With the superchip, gamers can play AAA games at 1440p resolution and over 100 frames per second with NVIDIA ray tracing, DLSS and Reflex technologies. In addition, RTX Spark supports all NVIDIA RTX technologies, including the recently announced DLSS 4.5 Ray Reconstruction, which features a second-generation transformer model for realistic image quality. 

RTX Spark Ignites Korea’s Gaming Community

Korea has played a major role in spearheading esports and driving the boom in PC bangs, or internet and gaming cafes. With longstanding collaborations rooted in the country, NVIDIA in October celebrated 25 years of GeForce in Korea with a free festival for gamers, highlighting the rich gaming ecosystem that has been built over decades.   

On Friday, Huang headed to T1 Base Camp — a PC bang owned by T1, one of Korea’s top esports teams. There, he met with T1’s reigning League of Legends World Champion team, including six-time World Champion Lee “Faker” Sang-hyeok to unveil RTX Spark. 

NVIDIA and Riot Games — developer of League of Legends — are collaborating to bring the title as well as VALORANT to RTX Spark, expanding gamers’ access to high-performance gaming on slim laptops. 

To mark the occasion, T1 Base Camp attendees had the chance to win RTX Spark laptops, League of Legends and T1 merch signed by Huang and Faker, as well as GeForce RTX 5090 GPUs. 

Surprising PC-Bang Gamers 

Later, Huang headed to Seoul’s Gangnam district, where he surprised PC-bang gamers with a first look at RTX Spark with KRAFTON and NC. 

At the first stop, Optimum Zone PC, Huang and KRAFTON Chairman Byung-gyu Chang showcased PUBG: BATTLEGROUNDS and Subnautica 2 on RTX Spark to a captivated crowd of gamers. 

Gamers then got the surprise chance to play with the unreleased PUBG Ally, a co-playable character built with NVIDIA ACE technologies on RTX Spark laptops. PUBG Ally resulted from AI research and development at KRAFTON and NVIDIA, part of an initiative to create next-generation game characters that act like teammates and enable more meaningful, immersive engagements with players.

Next, Huang stopped at another PC bang, Portal PC, where he showcased NC’s CINDER CITY and AION 2 on RTX Spark, with support from Taekjin Kim, co-CEO of NC. 

NC and NVIDIA began working together in the early 2000s on the Lineage franchise and have since collaborated to integrate RTX technology into many of NC’s flagship games, including Lineage 2, AION, Blade & Soul, AION 2 and CINDER CITY. 

Gamers at Portal PC were given the chance to play a demo of NC’s highly anticipated open-world massively multiplayer online tactical shooter CINDER CITY on GeForce RTX-powered PCs.  

CINDER CITY will support the DLSS 4.5 Dynamic Multi Frame Generation and Super Resolution features at launch. Plus, gamers will be able to experience the title on slim RTX Spark laptops and compact desktops when the game is released later this year. 

In addition to KRAFTON, NC, and Riot Games, 100+ Windows software providers and game developers are embracing RTX Spark. These partners include NetEase, Remedy Entertainment and XBOX. 

Learn more about RTX Spark and its launch partners.

Home to cutting-edge sovereign AI infrastructure and robotics innovators, as well as one of the world’s most passionate gaming communities, South Korea is one of the world’s centers of AI. NVIDIA founder and CEO Jensen Huang is in Seoul this week to meet the partners and builders behind that work.

Stay tuned here for live updates.

Monday, June 8, 10:00 a.m. PT

From Industrial Leadership to Gaming and AI: Go Korea!

“Thank you for your friendship. Thank you for your partnership. Go Korea!” NVIDIA founder and CEO Jensen Huang said, addressing a reception that brought together roughly 200 partners from every part of the Korea AI ecosystem, corresponding to the five‑layer cake.

“I’m very happy to be here with all of you. This is Korea’s ecosystem,” Huang said. “This is the industrial base. This is the venture investors. This is the young entrepreneurs. We brought them all together. Frankly, next year I hope to see this be 10 times larger — not two times larger, 10 times larger.”

Hosted at the Young Bin Gwan at The Shilla Seoul, the gathering rounded out Huang’s trip, which spotlighted Korea’s place at the intersection of gaming, industry and AI, and the many partnerships shaping what comes next. 

Off of a series of surprise visits to PC bangs and the announcement a week earlier of NVIDIA RTX Spark — a new superchip reinventing Windows PCs — Huang kicked off remarks on gaming and esports, tracing NVIDIA’s origins and Korea’s tech roots back to its earliest bet on computer graphics.

“Almost all great technology started out as toys,” he said. “And we realized that computer games were complicated, because they were trying to reproduce reality. Reproducing reality requires extraordinary algorithms, extraordinary computing technology. And we dreamed from that beginning, we could someday be one of the world’s most important technology companies. That was our dream. That was 33 years ago.”

That dream has “revolutionized the gaming industry,” Huang said. “It transformed an entire generation. It made video games something fun into something worthy to endeavor, to be great at. Now, Korea is the world leader in esports.”

Huang described Korea also as a “world-class leader in heavy industries” — and now in AI. 

“Now we’re sitting in a country where you are world-class at manufacturing, world-class at electronics, world-class at software — and you are now world-class at AI.”

The gathering capped off a week of meetings with partners — including LG Group, SK Group, Hyundai Motor Group, Naver and Doosan — expanding collaborations that support the nation’s AI infrastructure and setting the stage for advancements in agentic AI, physical AI and beyond. 

“You have everything that it takes,” he told the cheering crowd. “We are here to partner with you. I’m here to partner with you.”

Monday, June 8, 12:00 a.m. PT

Building AI Factories at Gigawatt Scale in Korea

NAVER is building a full-stack NVIDIA AI factory in Korea with NVIDIA DSX.

NVIDIA founder and CEO Jensen Huang met with NAVER founder and chairman Haejin Lee while in Korea as NAVER plans to expand its GAK Sejong AI data center to 55 megawatts and beyond to gigawatt scale.

As useful AI increasingly moves to production, AI factories are becoming critical infrastructure for training, post-training and inference. Built with the NVIDIA DSX platform with NVIDIA accelerated computing, NAVER’s AI factories will give Korea a sovereign foundation to create intelligence for enterprises, manufacturers, government organizations and AI cloud customers.

NAVER is also the first Korean company to participate in the NVIDIA Nemotron Coalition, contributing to open model development across pretraining, post-training and reinforcement learning to accelerate global AI innovation. It plans to launch an AI Agent Platform in Korea in the second half of the year, powered by NVIDIA NemoClaw blueprints.

Sunday, June 7, 11:00 p.m. PT

NVIDIA and Hyundai Motor Group

AI is changing how vehicles, factories and robots are built.

NVIDIA founder and CEO Jensen Huang met with Hyundai Motor Group leadership to discuss NVIDIA and HMG’s work across mobility and physical AI.

Sunday, June 7, 9:00 p.m. PT

Build-a-Claw at Seoul National University

The next generation of AI builders brought the energy.

NVIDIA founder and CEO Jensen Huang stopped by Seoul National University for a Build-a-Claw pop-up, packed with students, developers and AI researchers building intelligent agents from the ground up.

“The entire industry, the entire world is changing. Everyone is in the same starting line just like you,” Huang told the crowd. “It’s a great opportunity for you to shape this technology, to apply this technology. It’s brand new technology, so you are the expert.”

Sunday, June 7, 8:00 p.m. PT

NVIDIA and LG Expand Collaboration

Today, NVIDIA and LG Group announced plans to build an AI factory to support LG’s robotics, autonomous driving, data center technologies and GPU cloud services.

NVIDIA founder and CEO Jensen Huang met with LG Group chairman Koo Kwang-mo while in Korea as the companies expand their AI collaboration.

The combination of LG’s production technology data and know-how from global manufacturing sites with NVIDIA’s AI infrastructure and digital twin technologies will help enhance AI-driven manufacturing AI competitiveness. The two companies will collaborate to build an autonomous manufacturing ecosystem in which the entire process — from raw material procurement to production, logistics and customer delivery — is connected in real time through data and AI, and establish it as a new global smart factory standard.

Sunday, June 7, 7:30 p.m. PT

 NVIDIA and SK Partnership

Speaking with reporters in Seoul, NVIDIA CEO Jensen Huang and SK Group Chairman Chey Tae-won outlined an expanded AI partnership. This builds on a multiyear partnership announced today to codevelop memory for four NVIDIA platforms spanning AI infrastructure, personal AI and physical AI.

NVIDIA’s work with SK also extends to AI infrastructure. SK Telecom plans to build a gigawatt-scale AI Cloud in Korea using the NVIDIA DSX platform to support sovereign, physical and agentic AI services.

Sunday, June 6, 10 a.m. PT

First Pitch for the Doosan Bears

On Friday in Seoul, Huang headed to T1 Base Camp — a PC bang owned by T1, one of Korea’s top esports teams. There, he met with T1’s reigning League of Legends World Champion team, including six-time World Champion Lee “Faker” Sang-hyeok to unveil RTX Spark.

Today, Huang headed to Seoul’s Gangnam district, where he surprised PC-bang gamers with a first look at RTX Spark with KRAFTON and NC.

At the first stop, Optimum Zone PC, Huang and KRAFTON Chairman Byung-gyu Chang showcased PUBG: BATTLEGROUNDS and Subnautica 2 on RTX Spark to a captivated crowd of gamers.

Next, Huang stopped at another PC bang, Portal PC, where he showcased NC’s CINDER CITY and AION 2 on RTX Spark, with support from Taekjin Kim, co-CEO of NC.

Read more. 

Friday, June 9, 8:00 a.m. PT

KBBQ With Naver, LG Group, SK Group Execs

To shouts of “Welcome to Korea” from the crowd gathered outside on a Friday night, NVIDIA founder and CEO Jensen Huang visited Seoul’s popular Hongdae district for a sit-down with the heads of some of Korea’s leading technology companies over a meal of Korean BBQ.

Huang joined SK Group chairman Chey Tae-won, LG Group chairman Koo Kwang-mo and Naver chairman Lee Hae-jin for a casual night filled with food and plenty of toasts. “Go Korea, go SK, go LG, go Naver,” Huang said with his glass raised.

Twice during the dinner, Huang stepped outside to pass out snacks to the crowds gathered hoping for a glimpse of the tech leaders inside.

Fittingly, Huang and Chey handed out “HBM Chips,” to cheers from the crowd. HBM references SK Hynix’s leading “high-bandwidth memory,” but in the case of the snack, HBM stands for “honey banana mat (flavor).” Get it?

Thursday, June 4, 10:30 p.m. PT

Touchdown in Seoul

On the heels of GTC Taipei at COMPUTEX, NVIDIA founder and CEO Jensen Huang touched down in Seoul Friday afternoon, greeted by fans and media as his visit got underway.

A key focus of the trip, Huang said: to align the AI supply chain ahead of a busy second half of the year.

“We have a very significant, very large AI infrastructure buildout — already a very successful first half,” Huang told media. “Grace Blackwell, our system, is doing very well, and Vera Rubin is in full production — so we are going to be very busy the second half [of the year].”

Huang also touched on the huge potential for robotics and physical AI in Korea.

“Robotics is going to be the next major sector here in Korea — this is a great opportunity for Korea to invest in AI,” he said.

From memory manufacturing to robotics and gaming, Huang is off to a packed schedule with partners — but not without leaving time to enjoy some Korean fried chicken and BBQ. “It’s all delicious,” Huang said.

June’s forecast with GeForce NOW: 100% chance of gaming.

GeForce NOW is lining up new adventures for the month, from big-name blockbusters to quirky indies ready for the spotlight. Members can dive into fresh worlds, squad up in new playlists and discover “just one more run” favorites — all streaming from the cloud, no downloads or upgrades required. 

Eighteen games are coming this month, starting with the 10 games arriving this week with the highly requested Neverness to Everness. 

A World Beyond

Step into a surreal, supernatural open world in NTE: Neverness to Everness from Hota Studio. Reality bends, streets twist into impossible angles and the uncanny waits around every corner.

Play as an anomaly hunter drawn into a strange metropolis alive with anomalies, cosmic oddities and dreamlike encounters. Explore the city’s districts at street level or from impossible heights, uncovering secrets, side stories and hidden paths woven through the tangled skyline. Combat and exploration blend together as players move between quiet, eerie spaces and sudden bursts of action. 

Every alleyway and rooftop hides something strange and new, from bizarre characters to otherworldly phenomena that reshape the environment in unexpected ways.

Streaming with GeForce NOW lets the game’s distinctive art direction and atmospheric lighting shine, keeping the city’s moody glow, deep shadows and supernatural effects crisp and sharp. 

Whether wandering its streets on a big screen or checking in from a laptop, NTE: Neverness to Everness stays fluid and immersive — powered by the cloud.

June Games Juicing Up the Cloud

Return to the Valley of Mines in Gothic 1 Remake, a faithful rebuild of the original experience that expands the world with more detailed questlines, additional nonplayer character routines and reactions, new traversal abilities and a fully modernized combat system. Step into the role of the Nameless Hero and explore a dangerous prison colony filled with rival factions, ancient magic, deadly creatures and choices that shape the journey ahead. 

Whether revisiting a classic or discovering it for the first time, GeForce NOW makes it easy to jump into the adventure instantly across nearly any device, no downloads required.

Check out what all is available this week:

• Jurassic World Evolution 3 (New release on Xbox, available on Game Pass)
• Fatekeeper (New release on Steam, available June 2)
• House Flipper Remastered Collection (New release on Steam, available June 4)
• Pro Cycling Manager 26 (New release on Steam, available June 4)
• GOALS (New release on Steam, available June 4)
• Gothic 1 Remake (New release on Steam, available June 5)
• NTE: Neverness to Everness (Launcher)
• The Outer Worlds: Spacer’s Choice Edition (Steam and Xbox, available on Game Pass)
• Tomb Raider I-III Remastered (Epic Games Store)
• XCOM: Enemy Unknown (Steam)

And look forward to the games coming throughout the month:

• STARSEEKER: Astroneer Expeditions (New release on Steam, June 11)
• SpaceCraft (New release on Steam, June 11)
• Denshattack! (New release on Steam and Xbox, available on Game Pass, June 17)
• The Adventures of Elliot: The Millennium Tales (New release on Steam, June 18)
• Dark Scrolls (New release on Steam, June 22)
• Monopoly: Star Wars Heroes vs. Villains (New release on Steam and Ubisoft, June 30)
• Farever (Steam)
• FATAL FURY: City of the Wolves (Steam) 

More From May

In addition to the 16 games announced last month, 18 more joined the GeForce NOW library:

• Alchemy Factory (Steam)
• Battlestar Galactica: Scattered Hopes (Steam)
• Blades of Fire (Steam)
• BeamNG.drive (Epic Games Store)
• Directive 8020 (Steam)
• Disco Elysium (Xbox, available on Game Pass)
• Oddsparks: An Automation Adventure (Epic Games Store)
• Ostranauts (Steam)
• Planet Coaster 2 (Epic Games Store)
• PowerWash Simulator 2 (Xbox, available on Game Pass)
• Resident Evil Requiem Demo (Steam)
• Romestead (Steam)
• SPLITGATE: Arena Reloaded (Xbox, available on Game Pass)
• Subnautica 2 (Steam, Epic Games Store and Xbox, available on Game Pass)
• Sunderfolk (Epic Games Store)
• TerraTech Legion (Steam and Xbox, available on Game Pass)
• Warhammer 40,000: Mechanicus II (Steam)
• World of Tanks: HEAT (Steam)

What are you planning to play this weekend? Let us know X or in the comments below.

What makes a robot gripper useful isn’t that it can pick up one object — it’s that it can pick up the next one, and the one after that, with a tool it’s never held before. 

What makes an autonomous vehicle system safe isn’t just that it can reason through a situation — it’s that it can do so quickly enough on the hardware actually installed in the car. 

What makes a virtual agent capable is exposure to as many different environments as possible before it faces the real world. 

At this year’s Computer Vision and Pattern Recognition (CVPR) conference, NVIDIA Research is presenting three papers that address each of these challenges — and share a common theme: training at scale creates systems that generalize across diverse applications.

The three papers cover different challenges in physical AI research: 

• GraspGen-X, the first foundation model for zero-shot grasping, was trained on billions of simulated grasps to work with any gripper it’s shown.
• LCDrive introduces a model that replaces expensive text-based reasoning with compact latent representations, letting autonomous vehicles think faster on embedded hardware.
• NitroGen is a generalized gameplay AI foundation model that harnesses the NVIDIA Isaac GR00T robot foundation model architecture to help train embodied agents in virtual environments across tens of thousands of hours of interaction.

NVIDIA also unveiled at CVPR new physical AI agent skills that help researchers and developers speed the development of autonomous vehicles, robots and vision AI systems.

NitroGen and another NVIDIA-authored paper, PixelDIT, were named best paper finalists at the conference — an accolade given to just 15 of over 4,000 accepted papers at CVPR.

The First Foundation Model for Grasping

Most AI systems for robotic grasping are specialists.

A vision-language-action policy trained for a two-finger gripper only learns to grasp with those two fingers. Similarly, a policy for dextrous grasping will only work for the bespoke multi-fingered gripper it’s trained on. For every new embodiment, the process typically needs to be repeated — requiring new training data, fine-tuning and validation. This constraint means most robotics companies pick a gripper, train for it and stick with it.

GraspGen-X is the first foundation model for grasping built to eliminate this bottleneck. 

Like a large language model that can apply its understanding of language to a new task without retraining, GraspGen-X applies its understanding of geometry and contact to any robotic gripper it encounters. Given the geometry of a new gripper and an unknown object it’s never seen before, the model generates reliable grasp pose proposals to enable the robot to grasp the object.

To get there, the researchers needed a dataset that’s impossible to collect in the real world at scale. They generated 2 billion simulated grasps across thousands of object shapes and synthetic gripper configurations, spanning the diversity of form factors a deployed robot might encounter. 

For robot developers, this foundation model eliminates the need for per-gripper training cycles and can be applied out of the box for several commonly used grippers. GraspGenX can be used in conjunction with curoboV2, a new CUDA-accelerated motion planning library, to achieve these grasp poses in unknown environments. 

Building on the GraspGen research foundation, another paper, Grasp-MPC — presented at ICRA 2026 — advances the next step in the pipeline: moving from grasp generation to closed-loop grasp execution.

Teaching Autonomous Vehicles to Think Faster

In recent years, researchers have found that letting an AI reason — generating intermediate thinking steps before committing to an answer — reliably improves its decision-making. 

For autonomous vehicles, the challenge is doing that reasoning on the hardware inside an actual vehicle. Text-based chain-of-thought reasoning generates words, and every word is a token that takes time to produce. On the processor running inside a car, token count is a real constraint on how fast the system can respond.

LCDrive tackles this problem by replacing words with compressed latent representations. 

Instead of generating human-readable reasoning steps, the system thinks in a compact latent space — states that capture spatial information rather than producing text. The architecture alternates between two kinds of thinking: proposing candidate actions, then predicting what the world will look like if those actions are taken. 

It uses that predicted world state to refine its next step. It’s the same reasoning loop — just in a more computationally efficient form than natural language.

The result: comparable output trajectory quality to text-based reasoning, using roughly half the tokens. 

The model was built on NVIDIA Alpamayo and trained using supervision derived from existing vehicle data.

Embodied Agents Trained in Virtual Worlds

Isaac GR00T — NVIDIA’s open foundation model for humanoid robots — is built on a simple principle: expose a model to enough diverse situations, and it will generalize to ones it hasn’t seen. 

NitroGen extends that principle to virtual environments, using the GR00T architecture to train a foundation model for embodied agents across a breadth of virtual worlds.

Video games offer something that’s hard to build from scratch: structured, varied worlds with defined goals and well-specified success conditions. They’re high-quality training environments, available at scale. 

NitroGen treats them that way — as a training ground for agents that will eventually be trained to handle novel real- or simulated-world situations, like powering a robot that helps with housework based on broad instructions such as, “Put these items away in the pantry.”  

Trained across more than 1,000 games and 40,000 hours of interaction using a model based on GR00T, the resulting agents learn to generalize across environments. The model was evaluated across a range of action role-playing games, platformers, roguelikes and open-world games, demonstrating gameplay behaviors spanning combat, navigation and exploration. 

The same techniques could eventually help enable more adaptive nonplayable characters, AI companions and gameplay systems inside games, as well as broader testing of complex game environments.

In low-data conditions — where an agent has seen only a handful of examples of a new environment — starting with NitroGen gives agents a huge head start, improving performance by up to 52% over previous state-of-the-art methods. 

The model is open source, available on GitHub and Hugging Face. 

Learn more about NVIDIA at CVPR and explore NVIDIA Research’s work in physical AI, computer vision and autonomous systems. Get started with Isaac GR00T and NVIDIA robotics tools. 

At CVPR, NVIDIA is unveiling new physical AI agent skills that help researchers and developers speed the development of autonomous vehicles, robots and vision AI systems.

The core challenge in physical AI research isn’t simply developing stronger models. It’s building a full workflow around them — reconstructing real-world scenes, generating edge-case scenarios, training policies, evaluating behavior and rapidly iterating. Today, these steps are fragmented across separate tools, slowing the pace of experimentation as researchers struggle to piece them together.

Earlier this week, NVIDIA announced NVIDIA Cosmos 3, the open frontier model for physical AI and the world’s first full omnimodel unifying vision reasoning, world and action generation. Leading across the open model public leaderboards central to physical AI, the world foundation model provides core capabilities for physical AI development. NVIDIA physical AI skills pair with Cosmos,  NVIDIA libraries and simulation frameworks to help researchers move from model capabilities to scalable end-to-end workflows faster than ever. 

Advancing Autonomous Vehicle Research Beyond Recorded Miles

For AV researchers, the problem is the “long tail” of driving — rare interactions, unusual road geometry, lighting changes and edge-case behaviors that are difficult to repeatedly collect, but critical for training and validation.

Neural Reconstruction skill demo in OpenClaw, showing a video re-rendered from an elevated virtual sensor viewpoint.

With NVIDIA autonomous vehicle skills, researchers and developers can task AI agents to automate workflows for scene reconstruction from fleet data and generate synthetic scenarios. Neural Reconstruction skills help AI agents turn fleet-captured data into editable 3D scenes for simulation and synthetic data generation, while technologies including NVIDIA Omniverse NuRec, InstantNuRec, Harmonizer and HiGS accelerated renderer help accelerate reconstruction, improve scene realism and generate new views.

InstantNuRec enables fast 3D Gaussian road-scene reconstruction from images without per-scene optimization.

For AV researchers, repeatable simulation helps vary conditions, compare system responses and uncover failure modes across scenarios beyond what can be captured in real-world data. 

NVIDIA AlpaGym, an open source closed-loop reinforcement learning framework, extends that approach by connecting policy rollouts and high-fidelity simulation with agent skills, scaling across thousands of GPUs, to help researchers move through setup, rollout and evaluation. NVIDIA OmniDreams, an action-conditioned generative world model, adds photorealistic rendering to the simulation loop, generating camera frames that respond directly to policy actions in real time.

NVIDIA is also advancing AV research with its most powerful open driving foundation model to date: NVIDIA Alpamayo 2 Super, an open 32-billion-parameter reasoning vision language action (VLA) model that reasons, plans and acts across the full driving stack for safer, scalable level 4 development and deployment. 

Advancing Vision AI Systems for the Real World

For vision AI research, the bottleneck is creating enough controlled examples to study how models behave when visual conditions, object states or temporal events change. Work in zero-shot anomaly detection, synthetic anomaly generation and few-shot defect recognition all run into the same data wall.

New skills for visual inspection generates multiple rare defects on different surfaces.

New NVIDIA Metropolis skills are helping researchers and developers use AI agents to generate synthetic visual scenarios, including anomalies, augment data and support pseudo-labeling. These skills benefit from Cosmos 3’s mixture-of-transformers architecture, which uses a reasoning transformer to analyze observations and feed instructions to a generation tower, helping scale physically grounded virtual worlds.

Researchers building high-accuracy visual inspection models can use the Defect Image Generation skill to create examples of different defects across different surfaces using real images. The workflow combines NVIDIA Isaac Sim for simulation, Cosmos 3 and NVIDIA OSMO for orchestration and vision language reasoning — letting researchers create rare visual cases and assess whether models respond correctly.

New NVIDIA Metropolis VSS Blueprint skills extract insights from massive volumes of video data.

For video AI agents, the NVIDIA Metropolis Blueprint for video search and summarization (VSS), NVIDIA TAO and Video Augmentation skills help extract insights from massive volumes of video data, fine-tune models and automate the build-and-evaluate loop. This gives researchers a more repeatable way to develop reasoning vision AI agents that can detect events, reason over complex scenes, summarize activity and send alerts.

Scaling Robot Learning With Agent-Ready Simulation Workflows

Teaching robots skills like navigating or manipulating comes down to iteration. For researchers, the bottleneck is building enough controlled environments and policy rollouts to understand how robot behavior changes across tasks, settings and embodiments — work that typically means stitching together simulation environments, task variations, policy training and evaluation by hand.

NVIDIA Isaac Sim 6.0 includes agent-friendly skills and connectors to help automate workflows.

With NVIDIA robotics skills, researchers can task AI agents to automate most common development steps across scene preparation, simulation and robot learning with NVIDIA Omniverse libraries, Isaac Sim and Isaac Lab frameworks. Agents can help launch simulation sessions, author scenes, control simulation, capture data and validate environments in Isaac Sim, while Isaac Lab skills support reinforcement learning setup, training, evaluation and custom environment development.

New NVIDIA Isaac mobility skills automate navigation workflows.

Specialized skills extend that workflow to mobility and manipulation. Isaac mobility skills support navigation workflows spanning scene search, USD conversion, environment registration, residual reinforcement learning and policy evaluation, while specialized Isaac Lab agentic workflows help with sim-to-sim and sim-to-real tasks such as environment building, physics tuning, debugging and profiling.

For healthcare robotics, Cosmos-H-Surgical-Simulator advances research by generating realistic surgical robotics data for policy training and evaluation. By learning directly from real surgical data rather than hand-engineered physics models, it helps reduce the sim-to-real gap, supporting the development of autonomous surgical tasks.

Cosmos 3 can further help generate synthetic data and scene variations, then support post-training with embodiment-specific behavior and environment data for tasks ranging from pick-and-place to dexterous manipulation.

NVIDIA Research at CVPR

NVIDIA technologies — including GPUs, open models, simulation frameworks and CUDA-accelerated libraries — were referenced in the majority of accepted CVPR 2026 papers, with adoption across leading global research labs and institutions including Carnegie Mellon University, Stanford University, UC Berkeley, Tsinghua University and Peking University.

NVIDIA researchers are presenting work across computer vision, physical AI, autonomous systems, neural rendering, generative AI and robotics at CVPR, running June 3-7 in Denver. 

NVIDIA’s CVPR presence also includes open research challenges that help benchmark progress in physical AI:

• The AI City Challenge, a premier computer vision competition for smart city applications  now in its tenth year.
• The PAI-AV Reasoning Challenge, a new open benchmark evaluating how well VLA models explain driving decisions using chain-of-causation labels.
• The AlpaSim Closed-Loop End-to-End Driving Challenge, a new open benchmark testing autonomous driving policies in closed-loop simulation on real-world reconstructed scenarios. 

Grid of samples videos from new Robot Sim Dataset as a part of Cosmos 3 dataset release.

NVIDIA is also expanding the research infrastructure behind physical AI with datasets for training, fine-tuning and evaluation. The NVIDIA Physical AI Dataset has surpassed 15 million+ downloads on Hugging Face, while NVIDIA Isaac GR00T X Embodiment Sim has become one of the most-downloaded robotics datasets. New dataset releases include GRAIL, including roughly 50 hours of humanoid-object interaction data, and six synthetic video datasets used to train Cosmos 3 across robotics, physics, digital humans, autonomous driving, warehouse safety and spatial reasoning.

Availability

NVIDIA physical AI agent tools and skills are now openly available through GitHub.

Agent skills and tools for synthetic data generation — Neural Reconstruction, Video Augmentation, Defect Image Generation — are also available to try instantly on NVIDIA Brev as Physical AI Launchables, preconfigured environments that bundle agent skills and tools for faster synthetic data generation and evaluation. Launchables run on hosted NVIDIA H100 Tensor Core GPUs and include free trial credits for researchers.

Learn more about NVIDIA at CVPR and explore NVIDIA Research’s work in physical AI, computer vision and autonomous systems. Get started with Isaac GR00T and NVIDIA robotics tools. 

Accelerated computing has revolutionized industrial engineering, compressing simulation times from weeks to hours. 

Today’s remaining challenges sit in the end-to-end workflow surrounding the simulations: computer-aided design, meshing, simulation setup and debugging, as well as post-processing and generating summary reports of these processes. 

At GTC Taipei at COMPUTEX, NVIDIA and more than a dozen engineering software providers are showcasing how autonomous AI agents automate this entire workflow.

These AI engineers are based on NVIDIA NemoClaw, an open blueprint for building specialized, long-running agents with a secure runtime and frontier models. 

NemoClaw includes a choice of harness — meaning it can be integrated with various orchestration frameworks enterprises use to deploy and coordinate agents, such as OpenClaw and Hermes — as well as a model router and NVIDIA NeMo libraries for customization. 

Users can easily deploy NemoClaw from NVIDIA DGX Spark personal AI supercomputers, as well as through enterprise data centers and cloud service providers. NVIDIA OpenShell — the open source runtime at its core — governs how each agent accesses files, networks and tools, enforcing policy-based security at every layer.

Industrial Engineering Leaders Build AI Agents Across Design, Engineering, Simulation

Industrial software leaders are building AI engineers for computer-aided engineering (CAE) and electronic design automation (EDA) use cases across automotive, aerospace, semiconductors and manufacturing.

Cadence is building an autonomous register-transfer level (RTL) engineer with NemoClaw that orchestrates Cadence Design Systems ChipStack for design and verification. The workflow was featured yesterday in a GTC Taipei keynote demo and is cutting time for RTL verification — a key step in digital circuit design — from weeks to hours.

Dassault Systèmes is actively productizing the 3DEXPERIENCE Agentic Platform to operate long-running and autonomous agents for design, simulation and manufacturing operations, in a secured environment powered by NVIDIA NemoClaw and OpenShell.  

Siemens is integrating NVIDIA NemoClaw and OpenShell into Fuse EDA AI Agent, a purpose-built autonomous agent that plans and orchestrates domain-scoped multi-tool workflows across semiconductor, 3D integrated circuit and printed circuit board system design.

Synopsys is collaborating with NVIDIA to apply agents to end-to-end engineering workflows with NVIDIA NemoClaw. Ansys Icepak, part of the Synopsys portfolio, is being demoed on the COMPUTEX show floor this week, used within a NemoClaw-based autonomous AI engineer to mesh, simulate and optimize GPU electronics cooling designs.

Image courtesy of Synopsys.

Startups Extend the Reach of Agentic AI

In addition, cutting-edge startups are building AI engineers for their workflows — all using NVIDIA NemoClaw.

Flexcompute is applying OpenShell to its Tidy3D and PhotonForge agents for multiphysics co-packaged optics design. Flexcompute’s autonomous AI workflow combines optical, electrical and thermal simulation to explore thousands of design variants overnight, producing higher-performing components with lower energy consumption. NVIDIA is using Flexcompute technology for the design and optimization of advanced optical and photonic devices.

Video courtesy of Flexcompute.

Luminary is building a long-running AI engineer using NemoClaw to dramatically reduce the time and complexity of training AI physics models by autonomously orchestrating data generation, machine learning model selection, and training and re-training loops.

Video courtesy of Luminary.

Neural Concept is deploying an agent for electric motor design. The workflow chains electromagnetic, structural and noise, vibration and harness simulations in a multistep engineering pipeline. Watch the full demo.

Video courtesy of Neural Concept.

nTop, the geometry engine behind JetZero’s blended-wing-body aircraft program, is using NVIDIA NemoClaw to run autonomous design workflows that compress days of geometry iteration into hours.

Video courtesy of nTop.

PhysicsX is partnering with the Microsoft Surface team to build an electronics thermal simulation agent that compresses weeks of manual CAE workflows into automated, AI-driven design cycles. Bringing together the PhysicsX platform, Microsoft Discovery and NVIDIA NemoClaw, the agent automates the full thermal simulation lifecycle for consumer devices such as Microsoft Surface laptops — from mesh sensitivity analysis and simulation data generation, through physics AI model training and optimization-loop execution, to continuous accuracy monitoring across the design exploration process.

Video courtesy of PhysicsX.

P-1 AI is building Archie, an AI mechanical and electrical engineer that already works with data center cooling and critical power systems, and will soon work for automotive, aerospace and national security use cases. In a workflow representative of its work with Daikin Applied Americas, Archie synthesizes requirements, selects components, runs design trade studies and produces engineering artifacts to help industrial manufacturers scale engineering capacity.

Video courtesy of P-1 AI.

SimScale is adopting NVIDIA NemoClaw to build autonomous simulation agents for hundreds of cross-industry engineering use cases, including noise, vibration and harshness analysis, automating workflows that previously required multiple engineers working over several weeks. 

Video courtesy of SimScale.

Synera is building an engineering agent for injection molding — a manufacturing process used to efficiently mass-produce identical parts by injecting molten material, usually plastic, into a custom mold — with Autodesk Moldflow, NVIDIA OpenShell with OpenClaw, as well as Nemotron models.

Video courtesy of Synera.

Learn more about NVIDIA technologies for CAE and watch NVIDIA founder and CEO Jensen Huang’s GTC Taipei keynote in replay.

The agentic AI moment has arrived, but delivering on its promise requires more than good models. It also takes fast hardware, secure runtimes, a responsive data layer and models tuned for long-running reasoning. NVIDIA and Microsoft are bringing that full stack to developers across Windows devices, Azure cloud and local deployments.

At Microsoft Build, NVIDIA founder and CEO Jensen Huang joined Microsoft chairman and CEO Satya Nadella’s keynote via livestream from Taipei to discuss the expanded partnership: NVIDIA RTX Spark and DGX Station for Windows, NVIDIA GPU-accelerated Microsoft Fabric, NVIDIA open models on Microsoft Foundry, the NVIDIA OpenShell secure runtime in GitHub Copilot and the next generation of NVIDIA-powered AI factories.

Reinventing Windows for Agents: From RTX Spark to DGX Station for Windows

NVIDIA and Microsoft are reimagining Windows PCs for the age of AI agents. With RTX Spark laptops and small desktops, and DGX Station for Windows deskside AI supercomputers, developers can build, tune and run agents natively on Windows.

RTX Spark is a new beginning, powering the world’s first Windows PCs purpose-built for personal agents, with 1 petaflop of AI performance, up to 128GB of unified memory, all-day battery life, and full AI and graphics performance unplugged. Bringing over 30 years of NVIDIA innovation, including CUDA, RTX, DLSS and TensorRT, systems arrive this fall from Microsoft Surface, ASUS, Dell, HP, Lenovo and MSI.

DGX Station for Windows is the most powerful deskside AI supercomputer for building and running agents on Windows enterprise applications and workflows. Powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip with up to 748GB of coherent memory and 20 petaflops of FP4 performance, it runs frontier models of up to 1 trillion parameters for always-on enterprise agents. Systems are expected from ASUS, Dell, GIGABYTE, HP, MSI and Supermicro in Q4. Both products run NVIDIA OpenShell, a secure-by-design runtime for autonomous agents.

Read more in this Microsoft blog: “Introducing a powerful new chapter for Windows PCs, accelerated by NVIDIA RTX Spark”

Powering Agentic Workflows at Enterprise Scale With NVIDIA Open Models on Microsoft Foundry

Agentic AI runs on a system of models. With NVIDIA, Anthropic and OpenAI models — plus Hermes special agents — now on the hosted agents in Foundry Agent Service, enterprises can bring agentic systems to life on Azure with built-in identity and governance. Anthropic’s Claude models now run natively on NVIDIA GB300 Blackwell Ultra systems on Azure, with customer availability in the weeks ahead.

NVIDIA Nemotron 3 Ultra, a new open frontier reasoning model for long-running agents across coding, research and enterprise workflows, is available this month on Foundry managed compute, alongside Nemotron 3.5 ASR for speech recognition and Nemotron 3.5 Content Safety. Developers can compose Nemotron alongside frontier and local models, optimizing cost and quality for each workflow.

NVIDIA’s open model portfolio on Foundry now spans agentic, physical and scientific AI. NVIDIA Cosmos 3, the first fully open omnimodel for physical AI, brings vision reasoning, world simulation and action generation. NVIDIA Earth-2 AI weather models are available through Microsoft Planetary Computer Pro and Foundry for enterprise forecasting and risk analysis.

NVIDIA Agent Toolkit and NVIDIA NemoClaw blueprints give developers an open source platform to build production agents on Foundry. NVIDIA CUDA-X libraries including cuDF, cuOpt, AI-Q and NeMo are now accessible to agents as domain-specific skills.

Learn more in this Build breakout session: “Orchestrate Special Agents with NVIDIA Nemotron Models on Microsoft Foundry.”

Accelerating Enterprise Data Warehouses for the AI Era

Data fuels agentic AI, and fast access to it is critical. 

NVIDIA accelerated computing is now built into Microsoft Fabric Data Warehouse, with Microsoft’s internal benchmarking delivering SQL execution up to 6x faster than the CPU-powered baseline and up to 7x faster than three other leading cloud data warehouse providers for high-concurrency workloads. 

The enterprise data layer can now keep pace with AI agents that continuously query and reason over data, the result of years of deep engineering collaboration between NVIDIA and Microsoft, from research to production.

Read more in this Microsoft blog: “Microsoft Build 2026: Building agentic apps with Microsoft Fabric and Microsoft Databases”

Advancing Physical AI and Autonomous Systems

Physical AI is the next frontier for agents. 

Microsoft is integrating NVIDIA’s open source physical AI skills and tools with Azure and its Physical AI Toolchain. Developers get a unified platform, powered by Cosmos 3’s mixture-of-transformers architecture, to simulate, train and deploy autonomous systems, including robots, autonomous vehicles and industrial systems that can perceive, reason, plan and act in the physical world. Cosmos 3 ranks first among open models on key benchmarks for vision reasoning, world generation and action generation.

Enhancing Azure Local and Foundry Local With NVIDIA RTX PRO 6000 Blackwell Server Edition and Nemotron Models

Agentic AI is moving beyond the cloud. 

Microsoft is bringing Foundry Local on Azure Local to the NVIDIA RTX PRO 6000 Blackwell Server Edition platform. Paired with the NVIDIA Nemotron open model family, enterprises can run high-performance AI workloads where their data resides, whether in on-premises, hybrid or sovereign environments, without sacrificing performance or governance. 

Foundry Local on Azure Local now supports multinode deployments and the vLLM runtime, scaling inference for manufacturing, energy, sovereign data centers and other latency-sensitive scenarios.

Learn more in these Microsoft blogs: “Build, deploy and govern sovereign AI with Foundry Local on Azure Local” and “Scale On-Prem AI with Foundry Local on Azure Local.” 

Bringing Secure Agent Development to GitHub Copilot With NVIDIA OpenShell

As agents move from coding assistance to autonomous execution, they need real capability without real credentials. 

NVIDIA OpenShell, now integrated into GitHub Copilot, solves this: Each agent runs isolated in its own sandboxed container, and every outbound call is evaluated against policy before it can reach files, networks or credentials. Policies are written as code, versioned in the repository and updatable on the fly. OpenShell is open source under Apache 2.0, model-agnostic and spans on-premises, hybrid and cloud environments.

Learn more in this Build lightning session: “Secure Agent Workflows with GitHub Copilot and NVIDIA OpenShell.”

Fairwater Wisconsin Goes Live, Validated for NVIDIA Vera Rubin

Microsoft’s Fairwater Wisconsin AI factory is now live, ahead of schedule, running hundreds of thousands of NVIDIA Grace Blackwell systems as a single AI factory, and connected with a similar AI factory in Georgia to deliver a scalable and distributed AI system for the most demanding frontier models. Through joint engineering on power, cooling, NVIDIA Spectrum-X Ethernet and the new Multipath Reliable Connection (MRC) transport protocol, Microsoft’s Fairwater AI data center designs are optimizing token economics.  

In addition, Microsoft has already validated the NVIDIA Vera Rubin platform, now in full production, for deployment across Azure data centers. 

Vera Rubin slots in alongside Blackwell with no retrofits, delivering up to 10x inference throughput per megawatt and reducing cost per agentic token by an order of magnitude. Built-in NVIDIA Confidential Computing protects models and data as agents reason at scale. The NVIDIA Dynamo inference framework extends those gains into software, accelerating model cold starts on AKS and bringing Kubernetes-native distributed inference orchestration via NVIDIA Grove.

Read more in this Microsoft blog: “Scaling multi-node LLM inference with NVIDIA Dynamo-Grove on AKS (Part 4)”

Explore the full lineup of NVIDIA sessions, demos and hands-on labs at Microsoft Build.

Financial institutions have spent years building AI: fraud models, credit models, recommendation engines and risk systems. While this sprawl of task-specific models has been effective, it’s also constrained by siloed systems. 

Siloed systems prevent institutions from developing a unified understanding of consumers’ financial behavior. As enterprise datasets keep growing, so does the gap between what institutions know and what their AI can reason over — creating a major opportunity for the industry to build intelligence using proprietary data.

NVIDIA’s 2026 State of AI in Financial Services report shows 65% of institutions now use AI, with nearly 90% deploying or assessing it and almost all maintaining or increasing spend. But as AI scales, so does complexity, and fragmented model architectures become the limiting factor.

Leading firms are tackling this challenge by rethinking the architecture itself. Where the industry once relied on statistical and machine learning algorithms purpose-built for each line of business, transformer-based transaction foundation models now make it possible to learn a single, unified representation of consumer behavior trained entirely on proprietary data.

Transaction foundation models are large-scale AI systems trained on billions of financial events — such as payments, transfers, product interactions and behavioral signals — that transform raw data into intelligence, helping firms better serve their customers.

The shift is structural. A traditional fraud model evaluates isolated signals. A foundation model interprets behavior in context where timing, device, location and prior activity shape meaning. More importantly, it brings the power of transformer architectures to tabular data, extracting signals previously invisible to traditional algorithms.

A payment at midnight means something different when it’s the fourth in 10 minutes, on an unfamiliar device, in a city the customer’s never transacted from before. That contextual depth improves performance across tasks, not just within them.

In collaboration with NVIDIA, Revolut built PRAGMA — a family of transformer-based foundation models trained on 24 billion events across 26 million user records spanning over 100 countries. Powered by NVIDIA’s full AI stack — including NVIDIA Hopper GPUs, the NVIDIA cuDF library and NVIDIA Nemotron open models — running on Nebius cloud, a single foundation model outperforms strong task-specific models across domains like credit scoring, fraud detection and product recommendations while reducing reliance on handcrafted features. 

“We move from weeks, or even in some cases months, in feature engineering to no time required for it at all,” said Tadas Kriščiūnas, head of group credit data science at Revolut.

Any institution can now adopt this approach using NVIDIA’s new Build Your Own Transaction Foundation Model developer example, which enables teams to start building transformer embeddings on tabular transaction data — integrating into existing pipelines without rebuilding from scratch.

The Cost of Fragmentation

The problem isn’t today’s models, it’s the trajectory. Every new use case adds another model. Every new market needs retraining. Models that can’t share context leave value on the table.

Mastercard is developing a proprietary large tabular foundation model for payments, trained on billions of anonymized transactions today and designed to scale to hundreds of billions across additional datasets including fraud, authorization, chargeback, merchant location and loyalty data.

Built with capabilities from NVIDIA, AWS and Databricks — including the NVIDIA NeMo AutoModel open library, part of NVIDIA NeMo framework, and accelerated computing — the model is intended to reduce reliance on a multitude of AI models across markets, customers and use cases. Early testing shows it outperforming standard machine learning techniques, with promising applications in cybersecurity, fraud detection, loyalty, personalization, portfolio optimization and analytics. 

Adyen has also deployed transaction foundation models at scale, processing $1 trillion in payments. Using reinforcement learning, Adyen maximizes conversion and minimizes risk for merchants. 

“Even fractional improvements like a 0.1% uplift in authorization can translate to massive incremental gross merchandise value and substantial cost reductions,” said Dhruv Ghulati, principal AI product manager at Adyen.

Semantic Layer for Agentic Commerce 

Forty-two percent of financial firms are already using or assessing agentic AI. As these systems begin to execute transactions — like managing subscriptions, routing payments and making purchases — the nature of financial behavior is changing.

Stripe is using the NVIDIA and AWS platform to build foundation models that understand the full context of transactional behavior rather than reacting to individual signals — blocking close to $112 billion in fraud last year and delivering an average 38% reduction in fraud rates. 

Transaction data is the proprietary history that competitors can’t replicate. The data already exists. The architecture is proven. The infrastructure is ready.

Scaling Through Ecosystem Partners

The Build Your Own Transaction Foundation Model developer example is available for customers to run on Amazon Web Services (AWS), deployed with Amazon SageMaker HyperPod, as well as Nebius AI Cloud — powered by NVIDIA accelerated computing. 

Nebius AI Cloud supports the full transaction foundation model lifecycle — from deployment of the developer example through multi-node training to managed inference on Token Factory — powered by NVIDIA accelerated computing.

Financial services firms can also work with services partners EXL, GFT IT Consulting and Thoughtworks to apply the developer example to their specific use cases.

EXL is integrating transaction foundation models into its EXLerate.ai platform to unify siloed financial data into a scalable, enterprise intelligence layer powered by proprietary transaction data. In collaboration with NVIDIA, EXL is using this architecture to help financial institutions accelerate model development, enhance contextual decisioning and operationalize agentic AI at scale.

Thoughtworks is helping financial institutions operationalize transaction foundation models within complex banking environments, integrating them into payment, servicing and risk while establishing the necessary governance and AI operating models. The company will be showcasing a demo and presentation on transaction foundation models at the upcoming AWS Summit in New York City on Wednesday, June 17.

GFT IT Consulting is integrating transaction foundation models into its flagship solutions: Wynxx, an agentic AI platform used by over 100 financial institutions for secure AI adoption in areas like credit risk, and Smaragd, a compliance engine that reduces false positives by up to 75% for major banks.

Join NVIDIA at Money20/20 Europe from June 2-4 to learn how transaction foundation models are powering the next generation of AI in financial services.

Explore the Build Your Own Transaction Foundation Model developer example on build.nvidia.com.

Agentic AI is getting physical.

At COMPUTEX on Tuesday, NVIDIA announced NVIDIA JetPack 7.2 and NVIDIA NemoClaw support on NVIDIA Jetson.

JetPack 7.2 brings agentic AI skills, Yocto project support, NVIDIA CUDA 13 on NVIDIA Jetson Orin, a substantial performance gain on Jetson AGX Orin 32GB module and Multi-Instance GPU (MIG) support on NVIDIA Jetson Thor.

The launch coincides with the GTC Taipei Build-a-Claw event, bringing the popular hands-on event from GTC San Jose to Taiwan, one of the world’s premier global technology hubs.

The release lands NemoClaw, NVIDIA’s agentic AI framework, on the production-grade Jetson stack — taking agentic AI from servers and workstations into the physical world, across robotics, inspection and industrial automation.

“Agentic AI is here, and Jetson’s programmability and high performance enable developers to instantly deploy physical AI agents in production at the edge,” said Deepu Talla, vice president of robotics and edge computing at NVIDIA. “With purpose-built skills for agentic development and workflows, developers can accelerate time to market, cut total cost of ownership and deploy at scale — all on a memory-optimized platform.”

Jetson is already a multi-generation platform — Orin, Thor and beyond — powering edge AI in robotics, autonomous systems, industrial inspection and medical devices. JetPack 7.2 builds on that foundation; NemoClaw extends it.

Three layers ship in this release. JetPack 7.2 at the base — operating system (OS), compute, deterministic performance. A new layer of agent skills in the middle, automating developer tasks. And NemoClaw at the top.

JetPack 7.2 brings major upgrades to the Jetson software foundation. Yocto-based OS support gives industrial customers a leaner, more customizable Linux foundation — important for memory-bound deployments. CUDA 13 on Jetson Orin brings the latest compute stack to existing devices. MIG plus real-time kernel on Jetson Thor lets developers reserve dedicated GPU resources for deterministic workloads, like robot perception systems that can’t pause for unrelated AI inference. Jetson AGX Orin 32GB also gets a performance boost to 241 TOPS of AI compute, up 20% above its original spec.

The middle layer — agent skills — accelerates the work of building a Jetson-based system itself. Jetson agent skills now include Linux customization, memory optimization, model benchmarking and similar developer tasks. These are now available as agent-deployable skills, developed from NVIDIA documentation and design guides. The result: a task that used to take weeks resolves in days.

At the top, NemoClaw deploys to Jetson with a single command. The pairing lands agentic AI on a production-grade robotics and vision AI stack, accelerating task automation for industrial systems. Developers can go further with NVIDIA Metropolis VSS blueprint skills, adding visual reasoning agents that watch, interpret and act on what they see.

Agentic AI already arriving with Jetson

The Jetson platform is already in deployment across fields such as robotics, industrial automation, drones, healthcare devices, agricultural machinery, humanoid systems and more.

Solomon uses NVIDIA NemoClaw to coordinate AI agents on a humanoid robot, integrating reasoning, perception, sensor fusion, locomotion and manipulation into a single workflow. With Solomon’s active perception technology, powered by NVIDIA’s open source foundation model, the robot can understand tasks, optimize positioning for picking and adapt dynamically. All this enables reliable and autonomous operations in complex environments.

Advantech is building and deploying an agentic factory brain within its own manufacturing facilities to enable AI-native operations using NVIDIA NemoClaw, NVIDIA Nemotron 3 and NVIDIA Jetson Thor. The platform automates robot fleet management, intelligent defect detection and autonomous decision-making to drive next-generation industrial operations. Across industries, the builds are already shipping.

Rebotnix makes smart city cameras with agentic reasoning capabilities for faster city-level decision-making.

Spingence builds manufacturing defect agents to identify root causes and process improvement recommendations through analytics and knowledge reasoning.

And ANIWEAVE and Avalanche Computing are partnering to transform real estate spaces into immersive 3D touring experiences with AI-powered conversational agents.

More AI, less memory

SandStar uses NVIDIA Jetson Orin NX and NemoClaw to power AI vending machines and smart retail operations with AI vision, LLM-driven interaction, standard operating procedure monitoring and store optimization across 30+ countries. By achieving nearly 40% memory optimization, SandStar reports it migrated from 16GB to 8GB devices, significantly reducing deployment costs while maintaining high performance.

NoTraffic develops AI-powered Intelligent Traffic Management Systems that analyze real-time traffic conditions and dynamically optimize signal operations. NoTraffic reports it optimized CUDA library overhead through static compilation and targeted kernel pruning. These optimizations reduced memory usage by 29%, improving efficiency and streamlining the perception stack for faster real-time inference.

GROOVE X, maker of the LOVOT companion robot, is using a variety of AI accelerators on Jetson modules to offload CPU and GPU workload and reduce memory footprint.

Yocto-based JetPack 7.2 in production

Hexagon Robotics is integrating NVIDIA Jetson Thor to power safer and more autonomous humanoid robots with real-time AI, high-speed sensor processing and multimodal data fusion. Combined with Yocto-based OS customization for better reproducibility and safety, these humanoid robots operate more reliably in demanding environments such as manufacturing, logistics and construction.

Zipline uses NVIDIA Jetson Orin NX in its autonomous delivery drones to enable real-time sensor fusion, environmental awareness and safe navigation for rapid medical, food and retail deliveries around the world. Zipline uses Yocto to build its custom operating system which is designed for high-performance onboard AI processing while optimizing for reliability, efficiency and a lower memory footprint.

1X (maker of the Neo Humanoid) and Universal Robots are planning to adopt Yocto-based JetPack 7.2 in their production deployments.

Yocto ecosystem partners

Balena, Konsulko Group, Neurealm, Peridio, RidgeRun and Wind River provide Linux distro products, engineering services and long-term support that help customers ship production-grade Yocto-based deployments faster.

AAEON, ASUS, Avermedia, Connect Tech and YUAN have validated Yocto OS with their production edge computing systems to accelerate customer deployment.

What’s next

NemoClaw started in the data center. Now it runs in a retail store, a humanoid robot on a factory floor, a traffic system at a busy intersection. The era of physical AI agents has just begun.

Developers can start their agentic AI journey from the Jetson software page.

Watch NVIDIA founder and CEO Jensen Huang’s keynote and learn more at NVIDIA GTC Taipei.

See notice regarding software product information.

The NVIDIA AI Cloud ecosystem is accelerating the global buildout of AI factory infrastructure. Partners are expanding capacity to meet growing demand from enterprises, startups, nations, AI labs and developers scaling agentic AI applications. 

NVIDIA AI Clouds are a growing ecosystem of purpose-built clouds serving the exploding token demand behind today’s most popular AI applications. These AI clouds have been co-designed with NVIDIA’s full-stack AI infrastructure to meet surging demand for AI from enterprises, startups and nations looking for new vendors and regional capacity. 

They combine NVIDIA accelerated computing, networking and AI software to help partners support training, fine-tuning, inference, agentic AI, physical AI and sovereign AI deployments. Specific configurations vary by partner and workload.

AI cloud partners choose NVIDIA for the best economics — lowest token cost, best throughput per watt — to run frontier and open source AI. Built with NVIDIA accelerated computing, networking and AI software, these clouds bring AI factories closer to where data, developers, users and industries are, helping customers train, tune and run agentic AI applications at scale. The ecosystem spans nearly every geography, supporting regional and sovereign AI capacity for frontier model builders, enterprises, startups, software providers and national AI programs.

“Every company and every country needs AI factory infrastructure to turn data into intelligence,” said Jensen Huang, founder and CEO of NVIDIA. “NVIDIA AI Clouds bring full-stack AI factories closer to the regions, industries and developers building the next generation of AI, from model training to real-time inference and AI agents that will transform how people and organizations work.”

Broad AI Cloud Ecosystem

AI cloud providers, telcos, sovereign AI builders and vertically integrated infrastructure providers are building AI factories with NVIDIA to serve customers across frontier AI, enterprise AI, telecommunications, developer clouds and national AI programs.

Regional growth is accelerating across Southeast Asia, Australia and the Americas, with NVIDIA AI Clouds now reaching six continents following the addition of Cassava in Africa and Claro in South America.

NVIDIA AI Clouds are pairing large-scale AI factory buildouts with demand from leading AI labs, enterprises, governments and digital service providers. Partners including CoreWeave, Firmus, IREN, Nebius and Nscale are expanding AI infrastructure to support frontier model development, enterprise AI, agentic applications and high-volume inference.

Across regions, NVIDIA AI Clouds are bringing AI factories closer to local industries and sovereign AI ecosystems. Partners including Firebird, GMI Cloud, Indosat Ooredoo Hutchison, Lambda, Naver Cloud, Sharon AI, Yotta and YTL are supporting emerging AI companies, national AI initiatives, financial services, telecommunications, manufacturing, education, healthcare and developer ecosystems.

For governments and regulated industries, regional AI clouds can support sovereign controls and local compliance requirements. For developers and enterprises, they can reduce friction in accessing accelerated infrastructure for AI agents, enterprise copilots, digital workers and other AI services that must run close to users and data.

Firmus Expands AI Factory Footprint Across Australia and Asia-Pacific

Firmus Technologies is expanding its AI factory footprint across South Australia and Southeast Asia, building energy-efficient infrastructure to support growing demand for large-scale training, inference and agentic AI workloads.

Through Project Southgate, Firmus is developing AI factories in Tasmania, Melbourne, South Australia and New South Wales, with an emphasis on renewable power, advanced cooling and modular infrastructure that can bring capacity online faster. The company has also deployed AI infrastructure in Singapore through a partnership with ST Telemedia Global Data Centres.

Firmus is using NVIDIA’s accelerated computing and reference architecture as part of its buildout, with NVIDIA DSX helping streamline AI factory design, deployment and operations.

Engineered in alignment with the NVIDIA DSX platform, the liquid-cooled Firmus HyperCube is designed to fast-track modular AI Factory builds and optimize for low cost per token. Firmus is innovating across the AI factory supply chain, including cooling and energy.

“AI agents are creating a new class of industrial-scale demand for tokens, and Asia-Pacific needs AI factories that can be built faster, liquid-cooled more efficiently and operated at gigawatt scale,” said Tim Rosenfield, co-CEO of Firmus. “Together with NVIDIA, Firmus is building liquid-cooled, AI infrastructure designed to deliver AI tokens as efficiently and rapidly as possible for the region’s most important customers.”

CoreWeave Advances Physical AI and Next-Generation AI Factories

CoreWeave is expanding its NVIDIA AI Cloud platform to support the next wave of agentic AI, physical AI and frontier model workloads. 

An early adopter of NVIDIA Vera Rubin and the NVIDIA Vera CPU, CoreWeave is also among the first to adopt NVIDIA Spectrum-X Ethernet Photonics, helping provide the networking foundation for million-GPU AI factories. CoreWeave is extending its platform for robotics and physical AI workflows, including using NVIDIA Cosmos 3, the latest frontier world foundation model, to help teams generate synthetic data, fine-tune models and accelerate robotics data flywheels. Leading AI labs, including Anthropic, build on CoreWeave’s infrastructure to support frontier models at scale.

“AI factories are becoming the foundation for the agentic era,” said Michael Intrator, cofounder, chairman and CEO of CoreWeave. “Together with NVIDIA, CoreWeave is building the full-stack cloud infrastructure that gives AI labs, enterprises and developers the performance, scale and reliability they need to turn frontier models, AI agents and physical AI systems into production applications.”

Nebius Builds an Open Physical AI Workbench for Agentic Workflows

Nebius is expanding its NVIDIA AI Cloud with a full-stack platform for training, inference and physical AI development.

An early adopter of NVIDIA Vera Rubin, Nebius is building integrated AI infrastructure from silicon to software, including its Nebius AI Cloud, Token Factory inference layer and new Physical AI Workbench. The workbench brings technologies including NVIDIA Cosmos 3, NVIDIA Isaac Sim and Isaac GR00T into composable workflows that can be assembled by AI agents, helping robotics and autonomous systems teams move faster from simulation and synthetic data to training and evaluation.

“Developers should be able to build AI systems without spending weeks wiring together infrastructure,” said Arkady Volozh, founder and CEO of Nebius. “With NVIDIA, Nebius is creating an AI cloud where AI  agents can compose the tools, data and compute needed to accelerate AI workloads — from robotics and life sciences to the enterprise — from experimentation to production.”

NVIDIA Exemplar Cloud Momentum

Since NVIDIA introduced Exemplar Cloud last year, six NVIDIA Cloud Partners have achieved Exemplar Cloud status: CoreWeave, Crusoe, Lambda, Nebius, Vultr and YTL. The growing roster reflects increasing demand for AI cloud infrastructure that can deliver consistent performance, reliability and efficiency for production AI workloads.

These providers are helping raise the performance bar across the AI cloud ecosystem, giving enterprises, developers and AI labs more validated options for scaling training, inference and agentic AI services.

Engineered for AI Factory Economics

As AI shifts from model development to reasoning and high-volume inference, the measure of infrastructure is no longer just capacity announced but also the economics of token output driven by platform utilization, uptime, long asset life and the breadth and depth of useful AI agents people can put to work. 

Built on NVIDIA full-stack AI factory platforms, AI Clouds help partners optimize infrastructure for these measures.

Cost per token is the total cost of ownership metric that directly accounts for hardware performance, software optimization, ecosystem support and real-world utilization. NVIDIA delivers the lowest cost per token in the industry, driven by delivered token throughput, software optimization and full-stack codesign across compute, networking, memory and storage.

DSX Helps AI Clouds Bring Capacity Online Faster

NVIDIA AI Clouds are adopting the NVIDIA DSX platform to design, build and operate AI factories.

DSX brings together validated reference designs, simulation, software and ecosystem technologies to help cloud providers bring capacity online faster, operate more efficiently and maximize revenue.

DSX Sim helps teams model and validate AI factories before deployment. DSX Flex helps AI factories dynamically adapt workloads to grid conditions. DSX MaxLPS helps power-constrained AI factories maximize compute within a fixed power budget, enabling up to 40% more GPUs. DSX OS helps automate lifecycle management and operations at scale.

DSX helps AI Clouds reduce deployment risk, improve resiliency, deliver more tokens per watt and achieve the lowest cost token.

As factories move from isolated automation to plant-wide intelligence, manufacturers need AI systems that can connect live machine signals, quality systems, work instructions and operational alerts into a unified decision layer. 

Today at GTC Taipei at COMPUTEX, NVIDIA announced the NVIDIA Factory Operations Blueprint (FOX) — a reference design for building an autonomous factory manager agent that continuously monitors and reasons across the real-time data and orchestrates a fleet of speciality agents and machines to quickly resolve issues at scale. 

FOX helps developers build secure, centralized factory manager agents for orchestrating and optimizing specialized industrial AI agents for quality control, material transport and worker safety. Built with NVIDIA NemoClaw, AI-Q Blueprint and NVIDIA Nemotron open models, the blueprint provides a customizable foundation for connecting factory systems, automating model development and running intelligent operations at scale.

The blueprint is optimized to run on NVIDIA DGX Station, the ultimate deskside AI supercomputer companion for factory managers. 

DGX Station is powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, featuring 20 petaflops of FP4 performance and 748GB of coherent memory, and is capable of running large AI models up to 1 trillion parameters, making it ideal for developing and running powerful AI agents locally. 

The superchip features the NVIDIA Blackwell Ultra GPU connected to a high-performance NVIDIA Grace CPU using the NVIDIA NVLink-C2C interconnect to deliver best-in-class system communication and performance, ideal for lightning-fast interactions between NemoClaw and AI models.

Key capabilities of the FOX blueprint include:

• Connecting factory systems and agents: FOX integrates with industrial data sources, machines, applications and robot fleets, and can connect to specialized agents from leading software developers through standard application programming interfaces and agent skills.
• Automating AI model training: Using NVIDIA TAO skills, factory manager agents can automate the full model-training lifecycle — identifying accuracy gaps, sourcing or synthetically generating training data, fine-tuning models and redeploying them into production.
• Operating intelligent factory workflows: Visual inspection, process compliance and material transport agents can be managed with NVIDIA open models and blueprints, including the NVIDIA Metropolis Blueprint for video search and summarization (VSS). Real-time factory data can also be visualized in an operational twin built with NVIDIA Omniverse libraries.

Taiwan manufacturers Advantech, Foxconn, Pegatron and Wistron  are the first to deploy autonomous factory manager agents using the NVIDIA FOX blueprint and NemoClaw.

Foxconn, the world’s largest electronics manufacturer, is using the FOX blueprint and NemoClaw to build MoMClaw, a manufacturing operations multi-agent system. 

Running alongside a live production work, MoMClaw connects sensors, machine signals and other digital systems with hundreds of specialized agents in a single agentic layer — giving plant managers and operators real-time answers and action plans through a natural language interface with NVIDIA OpenShell privacy controls and safety guardrails. With MoMClaw, Foxconn projects an 80% improvement in root cause analysis time, a 15% increase in labor productivity and a 10% decrease in machine failure rates.

Pegatron is using the FOX blueprint and NemoClaw to build a factory manager agent that orchestrates specialized agents for material transport, AI inspection, standard operating procedure guidance and machine-to-machine coordination. With the factory manager agent, Pegatron can orchestrate robot utilization more efficiently, eliminating the need for expensive standby equipment, with an estimated 15% reduction in asset redundancy costs.

Advantech has introduced the AI Factory Brain, an intelligent multi-agent system led by a factory manager agent built with the FOX blueprint and NemoClaw. Advantech has deployed the factory manager agent in its own factories to autonomously manage energy across HVAC and lighting specialized agents and projects to cut energy consumption by 10%.

Wistron is adopting the FOX blueprint and using NVIDIA Cosmos, NVIDIA Nemotron open models and the NVIDIA Metropolis VSS blueprint to build surface-mount technology agents that analyze and orchestrate production-line operations, enabling real-time root-cause analysis and quality control. 

To monitor manufacturing operations, improve quality, verify standard operating procedures and improve worker safety, companies including DeepHow, Overview AI, Roboflow and Spingence are building specialized agents powered by NVIDIA AI and the NVIDIA VSS blueprint:

• DeepHow is using the Metropolis VSS Blueprint and Cosmos 3 to develop a standard operating procedure agent for Foxconn that supports assembly of Bianca boards for NVIDIA GB300 servers. Running on NVIDIA RTX PRO Servers, the agent accurately understands complex assembly motions to help improve first-pass yield by 3%, minimizing rework and production waste.

• Spingence is using the NVIDIA Defect Image Generation skill, NVIDIA Cosmos open vision language model and NVIDIA TAO Toolkit for fine-tuning to develop a factory manager agent for Cooler Master that connects automated optical inspection and model-building agents, achieving 99.6% defect recall, reducing defect escapes by 78% and increasing inspection capacity by 3x.
• Overview AI is using an NVIDIA agent skill for defect image generation and NVIDIA Cosmos to help Amphenol improve manufacturing efficiency with its Advanced GenAI Toolkit. The toolkit generates synthetic defect data and deploys visual inspection AI models 12x faster, reducing time to first inference to under 30 minutes across more than 300 products.  
• Roboflow is using NVIDIA Cosmos to develop a model-building agent for Corning Fiber Optics that generates synthetic defect images when training data is limited, delivering near-perfect detection rates and demonstrates the potential to reduce daily manual image review.

Sign up to be notified when the NVIDIA Factory Operations Blueprint is available.  

Metropolis VSS blueprint 3 is now generally available, including skills that allow external agents — such as Claude Code, Codex, Hermes and NemoClaw — to access VSS components and rapidly build and operate video analytics AI agents. 

Watch NVIDIA founder and CEO Jensen Huang’s keynote and learn more at NVIDIA GTC Taipei.

Taiwan is home to more than 500 NVIDIA ecosystem partners. More than 1 million NVIDIA MGX rack components for NVIDIA Vera Rubin infrastructure come together in Taiwan, from across 25 factory sites.

As Vera Rubin ramps into full production to power agentic AI factories worldwide, that ecosystem spans the full supply chain — from key wafer and chip partners such as TSMC, SPIL, Kinsus, KYEC and UMTC, to manufacturing and systems leaders including Foxconn, Pegatron, Quanta Cloud Technology (QCT), Wistron and Inventec.

But these partners are doing more than building AI factories. They’re applying accelerated computing, simulation, AI agents and physical AI to their own operations, creating a model for how AI can make advanced manufacturing faster, more efficient and adaptive.

Taiwan’s Manufacturing Leaders Build the Future of AI, With NVIDIA AI

Across chipmaking, server assembly and factory operations, Taiwan’s manufacturing leaders are applying NVIDIA technologies to reshape how AI infrastructure is designed, built, tested and scaled. 

TSMC is applying NVIDIA CUDA-X libraries and AI models across computational lithography, transistor and process simulation, advanced process control, yield analysis, fab operations and inspection. NVIDIA cuLitho improves cost-effectiveness or cycle time by 20-50% over CPU-based computational lithography at the same cost of ownership, while the NVIDIA cuEST library improves semiconductor material simulation by 50x on average, cuML library, Metropolis platform and TAO Toolkit help accelerate material simulations, improve process control and strengthen rare-defect inspection.

Foxconn is using the new NVIDIA Factory Operations Blueprint and NemoClaw blueprints to build MoMClaw, its manufacturing operations management agent, connecting sensor and machine signals with specialized agents that give plant managers and operators real-time answers and action plans through a natural language interface with NVIDIA OpenShell privacy controls and safety guardrails.

Foxconn estimates an 80% speed up in root-cause analysis time, a 15% increase in labor productivity and a 10% decrease in machine failure rates.

Foxconn also uses DeepHow’s SOP Verification vision AI system using NVIDIA Cosmos and the NVIDIA Metropolis Blueprint for video search and summarization (VSS) to gain greater visibility into complex manufacturing processes, resulting in improved manufacturing efficiency and boosting first pass yield by 3%. The company is also applying NVIDIA Isaac Teleop, Isaac Sim, Isaac Lab and ROS 2 to wheeled humanoid robots operating in its factories, supporting precision assembly tasks such as pick and place, dual-arm collaboration and force-controlled screw fastening.

Foxconn’s $1.4 billion AI cloud supercomputing center in Taiwan — powered by 10,000 NVIDIA GPUs — is being built with the NVIDIA GB300 NVL72 hybrid cooling architecture.

Quanta Cloud Technology (QCT) is using NVIDIA Omniverse-based digital twins to accelerate factory planning, giving engineering, operations and logistics teams shared access to design data for faster layout feedback, optimized workflows and improved space utilization.

QCT is also working with its subsidiary Techman Robot on a physical AI developer kit that uses QuantaGrid systems for data generation and model training. Techman Robot is using NVIDIA Jetson Thor and the Isaac GR00T platform to support the development of its next-generation robots, including the TM Xplore I humanoid, for advanced industrial tasks such as server fan assembly.

Wistron is using the NVIDIA Omniverse DSX Blueprint, the NVIDIA PhysicsNeMo framework and Cadence Reality DC Design to simulate burn-in environments for stress-testing across global manufacturing sites and to optimize AI server manufacturing. 

Running on Wistron’s NVIDIA AI infrastructure with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, NVIDIA Omniverse and NVIDIA Metropolis libraries, these workflows speed layout analysis by as much as 70% and cut facility power demand by 20% through dynamic rack optimization.

Pegatron is adopting the NVIDIA Omniverse DSX Blueprint, developing simulation-ready assets, and connecting design data, thermal simulation, digital twins and physical qualification — accelerating the design and deployment of AI factories. 

Pegatron is also using NVIDIA’s Defect Image Generation physical AI agent skill with NVIDIA Cosmos world foundation models and Isaac Sim to generate synthetic defect data, reducing AI visual inspection deployment time by 67% and operational effort by 10%.

Inventec is using the Defect Image Generation agent skill in its Observation Agent to generate synthetic defect data for automated optical inspection. In notebook cosmetic inspection, internal validation produced more than 10,000 synthetic defect images and showed the potential to reduce real-world data collection and manual labeling by about 30%, shorten AI deployment time by about 25% and improve anomaly detection by about 10%.

As NVIDIA Vera Rubin ramps into full production, Taiwan’s manufacturing leaders are showing how AI infrastructure becomes part of its own manufacturing engine — using accelerated computing, simulation, agents and physical AI to build the next generation of AI systems.

Watch the GTC Taipei keynote from NVIDIA founder and CEO Jensen Huang and explore physical AI sessions.

Personal agents are exploding in popularity, with open source projects like OpenClaw and Hermes seeing rapid adoption by AI developer communities on GitHub. Built to adapt to individual preferences and workflows, these agents can interact with applications, generate content, automate repetitive processes and manage multi-step tasks — all while running locally on device.

Today at NVIDIA GTC Taipei at COMPUTEX, NVIDIA unveiled NVIDIA RTX Spark — a new class of Windows PCs purpose-built for personal agents — alongside a wave of updates that expand local agents across the broader NVIDIA RTX and DGX ecosystems. 

Running agents securely and privately requires hardware that’s up to the task. RTX Spark’s 1 petaflop of AI compute and 128GB of unified memory can meet the computing demand of on-device agents, offering a new class of computer that goes from tool to teammate. Designed for AI, creating and gaming, RTX Spark brings NVIDIA’s 30 years of technology innovation to slim Windows laptops with all-day battery life and ultraefficient desktop PCs.

NVIDIA’s partnership with Windows scales from personal to enterprise solutions. Also introduced at the show was NVIDIA DGX Station for Windows, the ultimate AI deskside supercomputer for professionals, bringing a data-center-class GPU and CPU for inference in a desktop system equipped with Windows for manageability, security and compatibility. 

Other announcements include:

• The NVIDIA OpenShell runtime is coming to Windows, built on Microsoft’s new security primitives for agents — providing developers an easy-to-deploy package for secure, on-device agents. Hermes Agent and OpenClaw will also integrate OpenShell and the Microsoft security primitives into their new Windows applications.
• The NVIDIA NemoClaw blueprint is expanding across NVIDIA’s full local AI lineup — GeForce RTX, RTX PRO, RTX and DGX Spark, and DGX Station — with new streamlined installers and support for Hermes Agent.
• 2x inference performance on top agentic models with multi-token prediction in llama.cpp and vLLM, as well as new multi-GPU optimizations for llama.cpp and ComfyUI.
• H Company is releasing computer-use tools — including new models and an upcoming desktop agent harness — optimized for RTX and DGX PCs.
• Adobe is rearchitecting its Photoshop and Premiere apps, Blender is adding NVIDIA DLSS 4.5 Ray Reconstruction, and NVIDIA unveiled RTX Video Frame Generation, which will be coming to ComfyUI. All these updates arrive this fall with RTX Spark.
• The NVIDIA Broadcast 2.2 update brings Studio Voice feature optimizations and Elgato Stream Deck support. NVIDIA Project G-Assist also adds Stream Deck integration.

Local Agentic AI: Personal, Private and Fast on Windows RTX PCs

Broad agent adoption has been limited by the inability to run agents securely and privately on users’ primary PCs.

NVIDIA and Microsoft are partnering to address this challenge by delivering a robust, secure Windows platform for on-device agents.

The collaboration begins with a strong foundation — new Windows security primitives and the NVIDIA OpenShell runtime — to ensure agents run safely and under full user control.

The new Windows primitives deliver identity, containment, policy and end-to-end security capabilities to build and run agents natively. NVIDIA OpenShell provides additional policy capabilities for the user to define what agents can and cannot do, the ability to intelligently route queries to local models based on the user’s privacy policies, and the ability to disguise personal information in queries sent to cloud models.

This robust security and privacy layer is being adopted by leading agent developers such as Hermes Agent and OpenClaw in their new Windows apps. These new apps will make it easy and secure for users to access powerful on-device agents that can execute tasks in Windows applications, reason through cross-app workflows, generate images and video, code plug-ins and apps, and semantically search local files.

Powering agents on local devices requires both robust security and performant hardware. RTX Spark features up to 1 petaflop of AI compute and 128GB of unified memory to meet the processing demands of on-device agents.

NVIDIA is also accelerating the local open model ecosystem these agents rely on. 

NVIDIA collaborated with the llama.cpp community to enable features and optimizations such as multi-token prediction (MTP) — a speculative decoding technique where a smaller draft model proposes multiple tokens at a time that the target model verifies in a single pass. This coupled with other optimizations such as programmatic dependent launch delivers 2x performance on Qwen 3.6 and 3.5 27B, and a 1.6x performance boost on Qwen 3.6 and 3.5 35B. These updates are available via the llama.cpp webUI and LM Studio.

For AI enthusiasts running multi-GPU rigs, NVIDIA collaborated with the open source community to enhance two of the most popular local AI tools:

• llama.cpp adds tensor parallelism for up to 2x memory and 1.8x compute on two equivalent GPUs.
• ComfyUI gains a new classifier-free guidance method for up to 2x performance on two equivalent GPUs, plus the option to split model chains across GPUs to take advantage of the combined memory.

NVIDIA is also expanding agent capabilities with H Company. H Company’s computer-use harness lets agents navigate a PC by seeing the screen and operating a mouse and keyboard just like a user, even in apps with no application programming interfaces, and is coming soon to RTX and DGX PCs with local model support. 

NVIDIA has collaborated with H Company to quantize its state-of-the-art Holo Computer Use models, as well as accelerate its harness — driving a 2x speedup on NVIDIA GPUs while reducing memory consumption by 35%. The models are available for download now, and the Holo Desktop app will be available soon. 

Agent Optimizations for Linux

For developers who need always-accessible local agents, NVIDIA DGX Spark is the most capable personal agent AI computer for developers who need a Linux environment — unifying large memory, fast compute and compatibility with the NVIDIA CUDA ecosystem.

This month’s DGX Spark OS release brings the most streamlined out-of-the-box experience with a streamlined NemoClaw installer, along with faster inference on the top agentic models. 

NemoClaw is now available for all NVIDIA RTX and DGX PCs on Linux and the Windows Subsystem for Linux. Safely deploy local agents on Linux with new streamlined installers, delivering automatic sandboxing and added support for Hermes Agent. 

NVIDIA has collaborated with vLLM to optimize inference for agents, with optimizations in vLLM and new optimized NVFP4 checkpoints for Qwen 3.6 35B. The updates deliver 2.6x performance on DGX Spark compared with the previously available NVFP4 checkpoints from Unsloth, and include kernel improvements as well as mixed precision, and CUDA Graph support for MTP.

Read the vLLM blog for a full walkthrough of serving NVFP4 mixture-of-expers models on DGX Spark — from unified memory tuning to a working NVIDIA Nemotron 3 Super reference setup.

Delivering Powerful Creative Experiences With Adobe

NVIDIA is partnering with Adobe to rearchitect Adobe Premiere and Photoshop for RTX Spark. Firefly-powered Generative Fill in Photoshop and Generative Extend in Premiere are among the hundreds of accelerated tools that deliver creative power, precision and control. RTX Spark takes these capabilities further, delivering up to 2x faster AI, editing, coloring and effects across creative workflows.

Adobe Premiere will feature a new video pipeline that taps into RTX Spark’s unified memory, Blackwell GPU and TensorRT software, delivering real-time performance for editing and color correction, GPU-accelerated AI performance and more efficient rendering of complex timelines. In addition, Adobe’s Substance 3D Painter and Stager will run natively on RTX Spark for smoother and more responsive 3D texturing and scene creation workflows.

Adobe’s next-generation Photoshop engine will be optimized for GPU-accelerated compositing, enabling live filters, high dynamic range and modern natural brushing. The AI-native pipeline is built to harness the full power of RTX Spark, including TensorRT.

Adobe will further extend Premiere and Photoshop to allow users to create, edit and design with Windows agents, providing creators with a collaborative teammate to accelerate their workflows.

Updates to Adobe’s creative apps like Premiere, Photoshop and Substance are expected to start rolling out alongside RTX Spark availability.

New Tools and App Updates for Creators

New NVIDIA platform updates and partner app optimizations are rolling out across the broader RTX ecosystem — some shipping today and others arriving with RTX Spark this fall.

NVIDIA Broadcast 2.2 graduates Studio Voice — an AI feature that makes any microphone sound studio-quality — out of beta starting today. Studio Voice now runs on GeForce RTX 3060 GPUs and above with improved performance. The application also gets Elgato Stream Deck integration and configurable keyboard shortcuts. 

Project G-Assist also adds Stream Deck support via the Elgato MCP Server, letting users enable AI assistant capabilities for their stream setup.

In addition, Blender Cycles is integrating DLSS 4.5 Ray Reconstruction as a new denoiser, turning the path-tracing viewport into an interactive, real-time viewer. This lets 3D artists navigate around a scene while seeing near-final render quality, transforming the lighting and look-development workflow. The update will be released with Blender 5.3 this fall, alongside RTX Spark.

Also launching with RTX Spark, RTX Video Frame Generation is a new AI effect that doubles or quadruples video frame rate in real time — ideal for enhancing the 15-20 frames-per-second (fps) outputs that AI models typically generate. It arrives as a Python wheel and a ComfyUI node, letting AI artists generate videos faster at low fps and then interpolate up to smooth playback rates.

#ICYMI: The Latest From RTX AI Garage

Read the full NVIDIA RTX Spark announcement for details on the superchip, NVIDIA’s work with Windows on agents, and partner laptop and small desktops.

ASUS ProArt creator laptops now ship with Black Forest Labs’ FLUX.2 Klein 4B — a distilled image model preinstalled through the MuseTree app, optimized with the NVFP4 format and NVIDIA TensorRT for RTX software development kit. Creators get an up to 2.5x speedup and 560% memory reduction, with the first-run experience going straight from unbox to generating images locally — no model downloads or ComfyUI setup required.

The NVIDIA AI for Media software development kit is introducing updates, including new LipSync NVIDIA NIM microservices optimized for French, German and Spanish. The Active Speaker Detection NIM microservice also adds multi-camera support with cross-video speaker correlation.

Check out the latest RTX AI Garage blog post on Hermes Agent and self-improving AI on RTX PCs and DGX Spark.

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