The global buildout of AI infrastructure reached a new milestone today — Firebird, an emerging AI cloud, launched the CIS region’s largest AI factory in Armenia, establishing a new AI computing hub powered by NVIDIA accelerated computing and Dell Technologies high-performance AI infrastructure. 

Nikol Pashinyan, prime minister of the Republic of Armenia; Zhaslan Madiyev, deputy prime minister of the Republic of Kazakhstan; and David Allen, U.S. chargé d`affaires, a.i. in Armenia, attended the AI factory opening ceremony. 

AI factories are the foundational infrastructure for the AI era, providing the computing capacity needed to train, fine-tune and deploy AI models for every domain at scale.

Building the Infrastructure to Create Intelligence at Home

While AI services are available globally, countries also need the capacity to develop and run AI for their own languages, industries and national priorities. Firebird’s AI factory brings that capacity to Armenia, giving developers, startups, enterprises, universities and public institutions the compute to build and scale AI at home.

Firebird plans to deploy more than 70,000 NVIDIA Rubin and Blackwell GPUs and 300 megawatts of AI infrastructure capacity in Armenia by the end of 2027, accelerating the country’s development as a center for AI research, advanced computing and innovation. 

“Our ambition for what we are building in the next 2 years or so is roughly 2 gigawatts of capacity around the world. We’re very focused on merging into frontier markets,” said Alexander Yesayan, co-founder of Firebird.

At this scale, energy efficiency is essential. Built on the NVIDIA DSX platform, this AI factory integrates accelerated computing, networking, power and cooling as one codesigned system. Firebird’s AI factory is designed from the ground up to turn compute into revenue. With DSX, it can run up to 40% more GPUs on the same footprint, producing more tokens per dollar and extracting more value from every megawatt of capacity.

A Magnet for Global AI, a Catalyst for Local Innovation

Firebird’s ambitions extend beyond a single site. With NVIDIA’s support, the company is pursuing an approximately 2-gigawatt AI infrastructure roadmap spanning Armenia, Kazakhstan and additional markets. 

Firebird also announced that NVIDIA intends to invest in the company, following an earlier investment by CoreWeave this year. These investments will help Firebird expand its global infrastructure and operational footprint, and support its efforts to establish the largest and most advanced compute clusters across frontier markets.

Delivered in just over six months, the Armenia AI factory demonstrates Firebird’s ability to turn ambitious infrastructure plans into operational AI capacity with exceptional speed.

Schneider Electric provides the power infrastructure supporting Firebird’s AI factory in Hrazdan, helping Firebird meet its accelerated deployment schedule by rapidly delivering and setting up critical systems, including medium- and low-voltage switchgear, three-phase uninterruptible power supply systems and rack enclosures. This keeps the power buildout moving at the pace of the compute and provides a reliable foundation to bring NVIDIA accelerated computing online at scale.

To support the facility’s thermal needs, Vertiv provided a cooling architecture combining chilled-water technology, advanced controls and Vertiv TrimCooler technology for efficient heat rejection. Vertiv’s iCOM CWM Chilled Water Manager centrally coordinates cooling resources, improving visibility, efficiency and responsiveness as demand shifts with AI workloads.

Early demand is coming from AI-native companies including Perplexity, which is working with Firebird to access high-performance AI infrastructure for its AI agent platform and answer engine. 

As AI becomes essential infrastructure worldwide, Firebird’s expansion can help make the CIS region a magnet for global companies building and running AI — and a catalyst for local developers, researchers and enterprises. 

Powered by NVIDIA’s total AI factory platform — reference architecture, accelerated computing, networking and AI software — and deployed on Dell PowerEdge servers, the new Firebird AI factory will help Armenia’s builders turn energy into intelligence and connect their innovations to the global AI economy.

August is here, bringing 26 new games for GeForce NOW members. 

Command the seas in World of Warships: Legends and discover what’s next in the GeForce NOW library, starting with the eight newly added games this week. 

In addition, GeForce NOW is at the QuakeCon gaming conference this week in Grapevine, Texas, with hands-on experiences awaiting attendees.

Return to QuakeCon 

Visit the NVIDIA booth at QuakeCon to experience GeForce RTX 5080-powered Ultimate cloud gaming in action, with demos showcasing stunning visuals at up to 5K 120 frames per second on an ultrawide display, as well as seamless gameplay on the Lenovo Legion Go S handheld device.

Conference attendees can see how thousands of PC games, including fan-favorite Bethesda titles, can move effortlessly across laptops, Macs, handhelds, mobile devices, TVs and more — letting members pick up where they left off on nearly any supported screen.

Gamers not at the show can try out Ultimate cloud gaming in action with a day pass and jump into Bethesda titles from any device.

All Games on Deck

World of Warships: Legends drops anchor on GeForce NOW this week, bringing free-to-play naval combat. Captains can command destroyers, cruisers and battleships across massive multiplayer battles while exploring the latest update, featuring the Pacific Hammer Campaign, a new line of U.S. destroyers and more.

Chart a course straight into the latest content across devices today without any installs needed, and check out all the games available this week:

• Big Walk (New release on Steam Aug. 4)
• Beacon Pines (Free on Epic Games Store Aug. 6)
• Sovereign Tower (New release on Steam Aug. 6)
• Expeditions: Samurai (New release on Steam Aug. 7)
• The Adventures of Elliot: The Millennium Tales (Xbox, available on the Microsoft Store)
• The Incident at Galley House (Steam)
• Machine Party (Steam)
• World of Warships: Legends (Steam)

And look forward to the games coming throughout the month:

• Pax Autocratica (New release on Steam Aug. 10)
• Car Wash Simulator (New release on Steam Aug. 10)
• Clawed (New release on Steam Aug. 13)
• Hell Let Loose: Vietnam (New release on Steam Aug. 13)
• Sandustry (New release on Steam and Xbox, available on Game Pass, Aug. 13)
• The Sinking City 2 (New release on Steam Aug. 18)
• Mortal Shell II (New release on Steam Aug. 20)
• Gallipoli (New release on Steam, Aug. 20)
• Aliens: Fireteam Elite 2 (New release on Steam Aug. 25)
• Resonance: A Plague Tale Legacy (New release on Steam and Xbox, available on Game Pass, Aug. 27)
• Breathedge 2 (New release on Steam Aug. 30)
• World of Warships: Legends (Steam)
• High on Life (Steam)
• High on Life 2 (Steam and Xbox, available on Game Pass)
• Paradise Killer (Steam)
• Misery (Steam)
• Pratfall (Steam)
• Starvester (Steam)

Extra Joy From July

In addition to the dozen games announced last month, 15 more joined the GeForce NOW library. 

• Breath of Fire IV (Steam)
• Call of Duty: Black Ops 6 (Ubisoft Connect)
• CloverPit (Xbox, available on Game Pass)
• Dinoblade (Steam)
• Dino Crisis (Steam)
• Dino Crisis 2 (Steam)
• Esports Manager 2026 (Steam)
• Funnel Runners (Steam)
• Granblue Fantasy: Relink – Endless Ragnarok Demo (Steam)
• Halo: Campaign Evolved (Steam and Xbox, available on Game Pass)
• Mistfall Hunter (Steam)
• Onimusha: Way of the Sword DEMO (Steam)
• Pathogenic (Steam)
• Sudden Attack Zero Point (Steam)
• The Life and Suffering of Prince Jerian (Steam)

Mistfall Hunter didn’t make it this month. Stay tuned to GFN Thursday for the latest updates.

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

Share a game screenshot or clip that perfectly sums up your day so far.

— NVIDIA GeForce NOW (@NVIDIAGFN) August 4, 2026

Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse.

In July, NVIDIA joined more than 200 companies and organizations in signing “Open Weights and American AI Leadership,” an open letter arguing that AI leadership will be measured not by any single frontier model but by whether an open ecosystem reaches every sector. 

Open models, which anyone can download, inspect, modify and run on their own infrastructure, are what make that possible. Nowhere is that more crucial than in physical AI, where every deployment is a specialization problem.

Physical AI has to understand and predict consequences, not just appearances. 

To make this possible, world models learn how physical environments behave, what may happen next and which following actions make sense. They can generate physically grounded world and action data, simulate future states and provide a foundation that teams can specialize for a robot, autonomous vehicle or vision AI system.

Open world models are already being used to generate training data, test policies and specialize physical AI systems. NVIDIA Cosmos 3 brings these capabilities together in an open model family, with leading benchmark results and adoption across robotics, autonomous vehicles and vision AI.

And NVIDIA Omniverse libraries, part of NVIDIA Agent Toolkit, provides prebuilt capabilities for building simulation-ready worlds that physical AI teams can use to train, test and validate systems before real-world deployment.

World Models Are the Foundation of Physical AI

The data behind physical AI is difficult and expensive to collect at the scale required. Rare events and long-tail scenarios can be especially difficult to reproduce safely and repeatedly. 

World models enable:

• More useful data by learning physical relationships from large-scale multimodal scenarios.

• More diverse environments that vary in weather, lighting, objects and trajectories.

• A better foundation to build on and adapt to a particular robot, vehicle, sensor configuration, task or operating environment. 

A general model hasn’t seen a team’s particular robot, sensors or operating environment. Closing that gap requires access to model weights, a license that permits adaptation and the tools needed for post-training. 

NVIDIA Cosmos world foundation models are available under the Linux Foundation’s OpenMDW 1.1 license, enabling teams to post-train models on their own data and hardware. Specialization is where openness becomes a practical technical requirement.

Specializing a model is only part of the workflow. Teams also need environments to generate data, run simulations and test behavior. 

Omniverse libraries help developers build simulation-ready environments, while OpenUSD provides the open framework for composing, reusing and exchanging complex 3D data across digital twins, simulations and synthetic data generation workflows. Together, Omniverse and OpenUSD cut the duplicated work that can otherwise pile up every time assets, sensor configurations or environmental conditions change.

Cosmos 3: The Frontier Model

NVIDIA Cosmos 3 — a frontier open physical AI foundation omni-model built on a mixture-of-transformers architecture — combines vision reasoning, world generation and action prediction, letting developers use one model family to understand scenes, generate synthetic data, simulate future states and build specialized world action models.

Developers can use Cosmos 3 as a vision language model, as a physics-grounded world simulator that predicts future world states and generates large-scale synthetic data, or as the backbone for world action models, instead of assembling and maintaining a separate model for each capability.

The family includes Cosmos 3 Super (64B) for high-fidelity world modeling, Cosmos 3 Nano (16B) for efficient reasoning and post-training, and Cosmos 3 Edge (4B) for on-device vision reasoning and robot policy deployment. Lightweight enough to run on edge GPUs, Cosmos 3 Edge can be deployed across NVIDIA RTX GPUs, NVIDIA DGX systems and NVIDIA Jetson, including Jetson Thor platforms.

Across benchmark evaluations, Cosmos 3 ranks No. 1 on Artificial Analysis for open weights text-to-image and image-to-video generation, on PAI-Bench for world generation and in the image-to-video category of Physics-IQ. For robot policy, it ranks No. 1 on RoboLab. Cosmos 3 Super is also the highest-ranked open model on VANTAGE-Bench for vision understanding.

In addition to Cosmos, NVIDIA’s physical AI stack includes Isaac GR00T for robotics, Alpamayo for autonomous vehicles and Metropolis for vision AI. 

How Developers Are Putting Cosmos 3 to Work

Across industries, developers are building on NVIDIA Cosmos for physical AI applications: Doosan Robotics, LG Electronics, Samsung Electronics and Skild AI in robotics; Li Auto, Xiaomi and Afari in autonomous vehicles; and Centific, Fogsphere, Linker Vision, Milestone Systems and Yuan for vision AI agents powering industrial AI and smart spaces applications.

The NVIDIA Cosmos Coalition extends this work by bringing together world model builders, AI developers and physical AI leaders to contribute models, research and evaluation methods. NVIDIA recently expanded the coalition to Japan, where robotics and manufacturing leaders intend to join and develop open world models for factories, logistics, agriculture, construction, healthcare and transportation.

Together, these implementations and collaborations are establishing open world models as an adaptable foundation for physical AI across robots, autonomous vehicles and vision AI systems.

Get Plugged In

Learn more about world models, OpenUSD and physical AI development by exploring these resources:

• Explore the open Cosmos 3 model collection and datasets on Hugging Face and GitHub.

• Read the Cosmos 3 technical report for full architecture details and evaluations.

• Read the Cosmos 3 technical blog.

• Tune in to the Cosmos Labs livestreams.

• Learn about the NVIDIA Cosmos Coalition.

NVIDIA is participating in the U.S. National Science Foundation’s (NSF) State and Regional Artificial Intelligence Infrastructure Hubs program, an effort launching today to expand access to the advanced computing, data, software and expertise needed for AI-enabled research and education.

Consistent with the aims of the Genesis Mission, the program will support state and multistate groups of colleges and universities working together to strengthen America’s AI ecosystem. In partnership with private industry, philanthropic organizations and state and local governments, the program will expand the AI infrastructure, software, educational resources and technical support needed by faculty, students and researchers across the country. 

These regional hubs will help institutions share AI computing resources, accelerate scientific discovery and innovation, and prepare students to participate in the AI economy.

Expanding Access to AI Infrastructure

The State and Regional AI Infrastructure Hubs program will bring shared resources closer to the institutions and communities they serve. 

State or regional consortia can pool expertise, focus on specific local priorities, achieve economies of scale and create pathways for institutions that might otherwise remain outside the frontier of AI-enabled research and education. Flexible approaches — including on-premises infrastructure, cloud computing or a combination — will allow consortia to design resources around their regional needs and economic priorities. 

The hubs will resemble the public-private partnership between NVIDIA, NVIDIA cofounder Chris Malachowsky and the University of Florida (UF) in 2020 to turn UF into the country’s first true AI university and provide AI compute access to all Florida public universities. That initiative now serves as a national model. Since launching its university-wide initiative in 2020, UF has grown to more than 300 AI-focused faculty and embedded AI education and research across all 16 colleges. And since 2017, UF faculty and units have received more than $511 million in AI research awards.

NVIDIA has also expanded academic compute access in other ways, including as a leading contributor to the NSF-led National Artificial Intelligence Research Resource (NAIRR) pilot program on which today’s announcement is built. 

Through NAIRR, NVIDIA partnered with university research teams across the country to turn computing resources into usable scientific capacity — giving researchers the infrastructure, tools and expertise needed to move from idea to experiment to discovery. The resources also facilitated meaningful educational opportunities that gave students critical real-world skills for the AI economy. 

Preparing the AI Workforce

AI infrastructure alone is not enough. A successful national AI strategy must include efforts to build a workforce that can use advanced computing, data resources and AI tools in real scientific and industry settings.

That means pairing infrastructure with clear learning pathways. Universities, community colleges and regional partners can build degree programs, short-form certificates and stackable credentials that help learners move from foundational AI literacy into applied skills. Those pathways will help students, faculty, working adults and technical professionals use AI, including open source models and technologies, in fields like physical AI and automation, healthcare, energy, agriculture, manufacturing, quantum computing and cybersecurity. 

NVIDIA can support this work by providing training resources, educator enablement, applied learning content, technical guidance, partner platforms and access to tools that help institutions move from awareness to hands-on capability. As NVIDIA’s education and training offerings evolve, the goal remains the same: help institutions build repeatable, openly available programs that prepare learners to use AI systems, accelerated computing and data workflows responsibly and effectively.

This is how regional hubs become more than infrastructure projects. Students gain practical experience. Faculty expand their ability to teach and apply AI across disciplines. Working professionals can earn new skills without leaving the workforce. And institutions can connect training to local employer needs, research priorities and the economic opportunities that matter most to their communities.

Connecting Research, Workforce and Regional Growth

For policymakers and leaders, the hubs offer an opportunity to connect regional research and educational infrastructure with regional priorities and broader workforce and economic-development strategies.

Institutions can cultivate talent for local needs, support research connected to regional industries and build stronger relationships among universities, community colleges, employers and government. These connections will help translate AI leadership into scientific progress, new businesses and high-quality jobs.

No single organization can build this capacity alone. Sustained collaboration among government, higher education, philanthropic organizations and private industry is essential to ensure that advanced AI resources are broadly available and effectively used.

Private-sector contributions can complement public investment with technology, implementation expertise and workforce development programs. Public institutions, in turn, can help direct those capabilities toward scientific, educational and economic priorities that advance regional needs and national interest.

Learn more about the NSF State and Regional AI Infrastructure Hubs program and how NVIDIA is helping expand access to AI research and education. 

For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for.

Handling these long‑tail events takes more than just object detection and motion prediction. AVs must understand the situation, reason about cause and effect, choose the right action and turn that decision into a safe, comfortable path — all in real time and in a way developers can inspect, validate and trust.

NVIDIA Alpamayo 2 Super, available now for commercial use, is part of the Alpamayo family, the most-adopted open reasoning models for autonomous driving on Hugging Face, supporting a wide range of AV-relevant capabilities within a single foundation model. 

Built on NVIDIA Cosmos 3 Super Reasoner and post‑trained with reinforcement learning, the model advances the AV ecosystem on two fronts: open commercial licensing and leading multitask capabilities for autonomous driving. 

Alpamayo 2 Super is part of NVIDIA’s growing collection of open models, datasets and tools for autonomous driving, expanding access, strengthening competition, giving developers greater control and supporting safer, more transparent AV deployment. 

Open Licensing for Production AVs

Alpamayo 2 Super is available on Hugging Face under OpenMDW‑1.1, the Linux Foundation’s permissive license for open AI model distributions. The license covers fine‑tuning, derivative models and commercial redistribution, allowing AV developers, automakers, truckmakers and suppliers to adapt Alpamayo to their own data, driving policies and deployment strategies. 

This openness lets AV researchers and companies keep control of their own data and infrastructure, as well as own the value they create through specialized models and accumulated know‑how. Such control is essential for workflows involving proprietary fleets and safety. 

Earlier Alpamayo releases were initially introduced for R&D. The OpenMDW license is now being  applied across the entire Alpamayo model family so developers can deploy any of the models commercially without requiring additional permissions. This creates a direct path from adaptation to deployment.

Open weights make that path economically viable. Teams can build on advanced reasoning without re‑training every foundation capability from scratch or paying frontier‑model costs for every task, matching the right model to the right job at the right cost. 

Alpamayo 2 Super enables frontier-scale reasoning in cloud-based development workflows, where developers can generate high-quality reasoning traces, synthetic training data and teacher outputs for model distillation. Within the Alpamayo model family, Alpamayo 2 Super delivers the highest reasoning and driving performance for multimodal autonomous driving development, while Alpamayo 1.5 and Alpamayo 1 provide more cost-efficient options for cloud-based development and model distillation.

The resulting distilled models can then be optimized for efficient, real-time inference in production vehicles. Together, the Alpamayo model family provides a cloud-to-car workflow that combines frontier-scale reasoning with scalable deployment across commercial AV fleets.

For AV programs, that means frontier‑scale reasoning in the cloud and efficient, specialized models in the vehicle — a more sustainable way to scale safe autonomy into commercial fleets.

Benchmark-Leading Reasoning at Frontier Scale

Alpamayo 2 Super ranks first on LingoQA, an autonomous driving reasoning benchmark, among nearly 40 models evaluated. In NVIDIA testing using the Lingo‑Judge metric, it outperformed Qwen2.5‑VL 72B by 17.0 points, Gemini 2.5 Pro by 15.1 points and GPT‑4o by 23.2 points, demonstrating state‑of‑the‑art reasoning for driving‑centric scenarios. Alpamayo 2 Super also ranks first across all autonomous driving benchmarks evaluated by NVIDIA, underscoring its leading performance across a broad range of AV capabilities. 

Alpamayo 2 Super offers 3x the scale of the 10‑billion‑parameter NVIDIA Alpamayo 1.5 and Alpamayo 1 models. The added capacity helps the model better generalize reasoning from sparse examples — a critical capability for the rare, multi‑agent interactions where conventional systems often struggle. 

The model reasons over full‑surround camera coverage, fusing views from the vehicle’s front, sides and rear. This 360‑degree context enables richer understanding of lane changes, merges, unprotected turns and complex intersections, where risks commonly arise.

A Multitask Foundation Model for Robotaxis and Autonomous Driving

For each driving situation, Alpamayo 2 Super can produce five tightly coupled outputs:

• A trajectory describing the vehicle’s planned path.

• A chain‑of‑causation (CoC) trace that explains the reasoning behind the decision.

• A meta‑action (e.g., yield, lane changes, stops) that captures the model’s intent.

• Reasoning auto-labels that generate CoC annotations for training and validation data.

• Visual question answering responses with 2D visual grounding that link the model’s answers to specific regions in camera images.

Together, these outputs offer insight into the model’s decision-making process. Developers can tie what the model observed to the action it selected, making decisions easier to understand, critique and validate. 

CoC traces integrate with NVIDIA Halos safety‑validation workflows and support AI safety aligned with ISO/PAS 8800 requirements, providing a stronger foundation for AV safety engineering. 

Alpamayo 2 Super can also be deployed as an autolabeler to generate CoC labels and perform visual question answering with 2D grounding on proprietary fleet data. By linking its reasoning to specific regions in camera images, the model can transform raw driving clips into richer training data, compressing annotation cycles from months to days.

Beyond planning and auto-labeling, Alpamayo 2 Super supports scene understanding, model critiquing and knowledge distillation. These multitask capabilities enable developers to use a single foundation model across more of the development stack, simplifying tooling and accelerating iteration.

An Open Ecosystem for Reasoning‑Based AVs

Alpamayo 2 Super is part of a broader family of open models, frameworks and datasets for AV development. 

Other tools in the family include: 

• NVIDIA AlpaSim, which provides closed‑loop simulation.

• NVIDIA AlpaGym, which enables high‑throughput reinforcement learning.

• NVIDIA Physical AI Open Datasets, which supply data for training and testing.

• Open training recipes and an autolabeling pipeline to accelerate model development, training and validation.

Alpamayo has already surpassed 500,000 downloads on Hugging Face, reinforcing its position as the most-adopted open reasoning model family for autonomous driving on the platform.

Download NVIDIA Alpamayo 2 Super on Hugging Face to explore the model, evaluate its reasoning capabilities and start building the next generation of robotaxis and autonomous vehicles.

Surging AI demands are driving the need for massive datasets and context windows that burst past the confines of system memory. 

But rising needs aren’t met by simply adding more storage capacity. What’s needed is useful, grounded insights from AI factories and efficient, secure storage architectures that enable those insights. 

At this week’s Future of Memory and Storage (FMS) conference, NVIDIA is unveiling new storage advancements and showcasing how the next leap in AI depends as much on the storage infrastructure feeding accelerated computing as on the computing power itself.

The pressure on that infrastructure is intensifying as AI agents consume massive amounts of data — and GPUs can now initiate storage requests directly, generating thousands of concurrent operations.

To serve those requests, storage systems must continuously encrypt, compress, verify and reconstruct data. These critical data services can become bottlenecks when thousands of agents access storage simultaneously.

Benchmarks highlighted in this NVIDIA technical blog show that the NVIDIA Vera CPU, part of NVIDIA Vera BlueField-4 STX, delivers up to 3.21x higher throughput than an x86 CPU in a two-stage compression and encryption pipeline. This means that with Vera, storage platforms can absorb the flood of AI data more efficiently — delivering greater throughput with significantly less compute infrastructure.

With accelerated computing, storage stops being a passive place to keep data and becomes an active part of the data path. 

This upends the old economics of determining when data belongs in memory (where applications can fetch it faster) versus on a storage drive (where it can be held in cheap and plentiful space). The tradeoff was first framed 40 years ago, when the answer was measured in accessing that data in minutes. On today’s GPUs, paired with AI storage solutions from NVIDIA and partners, the same tradeoff now plays out in microseconds.

Closing the gap between AI’s needs and memory shortage depends on extreme codesign across the whole ecosystem, from memory and storage manufacturers to the software built on them. 

Open Source NVIDIA cuFile APIs Enable Interoperability for Storage Solutions

At FMS, NVIDIA announced it is open sourcing its cuFile application programming interfaces (APIs) — and the vertical storage software stack underneath them — which let GPUs, not just CPUs, read from and write to storage directly. cuFile is an open source component of NVIDIA GPUDirect Storage.

Using hundreds of thousands of GPU threads, fast high-bandwidth memory and other methodologies, cuFile enables securely accessing data from storage in just microseconds.

This represents how the industry is unifying a security-first storage stack based on Linux best practices, providing interoperability between GPUs and data. 

In addition, fast, secure access to data and storage is a foundational element to powering preventive and detective cybersecurity measures. Making cuFile openly available will help make security context, data and storage accessible at the speed AI-powered defenses need. Such open technologies support initiatives such as the new Open Secure AI Alliance.

This site is the new home for APIs that are open to contributions — with Google, Intel, NVIDIA and Meta as inaugural maintainers — and can be optimized for use across various software and hardware platforms, driving innovation and efficiency for developers and enterprises.

NVIDIA and Industry Leaders Advance New Frontier of AI Storage

In addition, NVIDIA and storage industry leaders are optimizing memory and storage solutions through an initiative called Storage-Next. The NVIDIA-driven initiative brings together storage makers, controller vendors, thermal design, cooling and orchestration operators, and standards bodies to align on how GPU-driven storage should behave — then turn these advancements into interoperable, open industry standards.

Storage-Next includes over 40 leading storage and flash vendors — including DDN, KIOXIA and Micron — each contributing to the next generation of AI storage technologies with NVIDIA.

The initiative is grounded in accelerated data access for large AI datasets. To support this, NVIDIA offers SCADA — short for scaled, accelerated data access — a framework that lets massively parallel GPUs pull only the data necessary for the application directly from storage into their own high-speed memory.

For example, DDN is integrating SCADA with Infinia, its software-defined, AI-native data intelligence platform built to eliminate storage bottlenecks at scale. 

“AI success will be defined not by how much infrastructure organizations own, but by how productively they use it,” said Sven Oehme, chief technology officer at DDN. “Our collaboration with NVIDIA is helping create a more direct, efficient connection between GPUs and data — keeping accelerated computing resources productive, speeding time to insight and enabling customers to achieve stronger business and financial returns from their AI investments.”

Storage-Next and SCADA extend NVIDIA’s longstanding work on AI storage infrastructure, including on NVIDIA Vera BlueField-4 STX — a modular, rack-scale foundation powered by the NVIDIA Vera Rubin platform, NVIDIA Vera BlueField-4 storage processors and NVIDIA Spectrum-X Ethernet networking.

Defining a new class of AI-native data platforms, NVIDIA STX uses the unified NVIDIA DOCA security stack to let enterprises enable continuous policy enforcement in the AI data path.

Plus, NVIDIA CMX Context Memory Storage provides an AI‑native context tier for long‑context, multi‑turn, agentic AI inference, built on NVIDIA STX.

SCADA Enables Fast AI Storage That Stays Secure

Speed at the storage layer comes with a catch. Letting an application talk straight to a drive is quick, but done carelessly, it can scribble over other processes’ memory — a security hole, not a feature. 

NVIDIA SCADA uses a safe, robust method to achieve scaled direct access by splitting the job in two:

• The user parts of an application that need raw speed stay outside the trusted computing base. 

• A separate, privileged component configures protected access between the user application and its approved storage at setup, adhering to standard Linux protocols for security enforcement while efficiently safeguarding data.

It’s all part of how advancements in fast, massively parallel, efficient, secure AI storage infrastructure can feed better data to applications and AI factories — so they can produce more useful, accurate, grounded intelligence at scale. 

Join NVIDIA sessions at FMS, running Aug. 4-6 in Santa Clara, California, and learn more about NVIDIA AI storage.

See notice regarding software product information. 

Members of the Open Secure AI Alliance — now more than 120 organizations strong — are developing new guidelines to strengthen agentic AI cybersecurity as the annual Black Hat conference begins in Las Vegas today. 

The Linux Foundation today shared a Request for Comments on Shared AI Findings Exchange (SAFE), a proposed set of guidelines designed to turn agentic cybersecurity incidents into shared protection for the entire ecosystem.

The SAFE guidelines are being drafted by an Open Secure AI Alliance working group. NVIDIA, Cisco, CrowdStrike, Hugging Face and Red Hat are among Open Secure AI Alliance members working with the Linux Foundation to contribute to the initial proposal.

The SAFE guidelines include proposals to confidentially collect and analyze AI incidents and near misses, inform those impacted, identify recurring control failures and publish evidence-based operating recommendations that reduce systemic risk.

Cybersecurity is a race without a finish line. Every major technology shift has created new potential attack surfaces. Defenders must move now at agent speed to respond rapidly to protect infrastructure and intellectual property — and the best way to do that is together. When trusted ecosystems share threat intelligence openly, collective defense becomes a force multiplier.

Open Secure AI Alliance Delivers More Tools for AI Cybersecurity

The SAFE framework adds to technology contributions Open Secure AI Alliance members are making to build and share open, inspectable tools across the full AI security stack.  

An AI agent isn’t just a model. It’s a system — identity controls, harnesses, guardrails, logs and evaluation — and securing it requires more than vulnerability scanning. 

Security has always been strongest in the layers — and in the community’s willingness to share what it knows. The hardest problems get solved when defenders learn from each other, openly and at speed.

Full Stack of Open NVIDIA Cybersecurity Software and Models

NVIDIA’s contributions run the length of the stack, starting with the NVIDIA Labs Object-Oriented Agent (NOOA) research harness, on GitHub — which makes agent behavior easier to test, trace, audit and govern. 

The NVIDIA OpenShell runtime restricts what an agent can see, touch and do — enforcing security and privacy controls at the agent level, so an agent can’t reach what it shouldn’t. 

NVIDIA’s open model families — NVIDIA Nemotron for agentic AI, NVIDIA Cosmos for physical AI, NVIDIA Isaac GR00T for robotics, NVIDIA BioNeMo for healthcare and life sciences, and NVIDIA Alpamayo, the world’s largest model for autonomous vehicles licensed for commercial use — ship with open weights, datasets and training techniques. 

NVIDIA open source verified agent skills extend that trust to the capability layer. 

Each skill provides portable instruction sets — cataloged, scanned for risks such as prompt injection and tools poisoning, cryptographically signed and documented with a skill card. Defenders know exactly what an agent skill does, where it came from and whether it was modified after publication.

NeMo Guardrails, NeMo Anonymizer and NeMo Safe Synthesizer help enforce safety policies, protect sensitive data and generate privacy-safe synthetic data.

And Garak, NVIDIA’s open source LLM vulnerability scanner, lets security teams check models for data leaks, prompt injections and jailbreak scenarios before they ship. 

Alliance Members Expand Tools for Open Ecosystem Development

Other members of the Open Secure AI Alliance have also been building across the full defensive stack, spanning identity and permissions, harnesses, runtime guardrails, security AI models, observability and evaluation, data security and privacy, availability and resilience, and more. 

Some of the latest contributions across different layers of the stack are highlighted below, with more continuing to arrive.

Identity and Permissions — Who Gets to Act

You can’t secure what you can’t identify. 

Okta is developing reference implementations for agent identity and access, showing how Cross App Access (XAA), an open protocol, enables AI agents operating in OpenShell sandbox environments to securely connect to enterprise applications.

Palo Alto Networks has contributed open source tools from Idira, its next-generation identity security platform, including Agent Guard and Agent Watch. These tools help developers and agent builders apply identity security best practices and safe guards such as securely retrieving secrets for agentic workflows. 

A new open source project founded by Red Hat, asago takes an organization’s custom governance requirements — such as those referenced in NIST, OWASP and the EU AI Act — and maps them directly to what agents are allowed to do at runtime, with a single audit trail from policy clause to live control.

Harnesses and Tooling — How Security Work Gets Orchestrated

AI agents are more than a model – they tap into systems of open and closed models, harnesses, tools and runtimes to get work done.

If a model is the agent’s brain, the harness is the body that takes action by using tools. The harness surrounding the model acts like an orchestrator that determines how agents are deployed, coordinated and constrained. 

Alliance members are contributing tooling, harnesses and supporting technologies across this emerging layer of the AI security stack.

Amazon, which today became one of the newest members of the Open Secure AI Alliance, contributes Strands Agents, an open source toolkit for building AI agents that is open at every layer, giving developers full visibility into agent behavior and the ability to evaluate agentic systems in production. Amazon also contributes Cedar, an open source authorization language that enforces deterministic, verifiable boundaries on what AI agents are permitted to do, giving customers fine-grained, analyzable access controls to help ensure only authorized actions reach enterprise resources.

Capital One open sourced VulnHunter for agentic AI code security.

Cloudflare is offering its Vulnerability Discovery Harness as an open source skill to add security to agent systems.

Microsoft AI Red Team has open sourced several tools and harnesses. PyRIT – Python Risk Identification toolkit enables AI red teamers to run automated red teaming, with built in memory, supporting common targets, as well as custom endpoints. 

RAMPART turns red-team findings and real-world incidents into repeatable tests that run as software changes. Clarity helps teams question design assumptions and identify potential failures before code is written.

Microsoft has also open sourced Assert, which converts natural language requirements and expected AI safety and security behaviors into executable evaluations.

Atlas is Wiz’s autonomous vulnerability research engine that orchestrates specialized AI agents to discover and validate security flaws across code and open source packages.

Visa has also joined the Open Secure AI Alliance, contributing its open sourced Visa Vulnerability Agentic Harness to help teams identify issues, support remediation and validation, quickly and safely.

Models — Intelligence Built for AI Safety and Defense

Not every security or safety task calls for a general-purpose model. Specialized security and safety models are purpose-built for defense: trained to understand code, locate vulnerabilities and reason about threats at scale. They can work to support agentic workflows as systems of models, with both open and closed models working together to get the job done efficiently.

Cisco DefenseClaw is an open source agentic governance layer that sits on top of NVIDIA OpenShell to provide robust, automated security at the runtime level when scaling agentic workforces. Cisco has also released two of its Antares security small language models to help pinpoint where known vulnerabilities exist within a codebase; and Project CodeGuard to embed secure-by-default practices directly into AI coding workflows.

CrowdStrike is fine-tuning the NVIDIA Nemotron Nano model for cyber defense. Internal testing achieved 96% accuracy in generating investigation queries within Falcon LogScale, delivering a natural-language interface that boosts agent investigative efficiency. CrowdStrike has also published research demonstrating how a specialized NVIDIA Nemotron Nano reasoning model outperforms much larger models on Security Operations Center detection triage while introducing calibrated logit-based confidence to enable measurable, tunable, and auditable autonomous security decisions.

Mistral today released its new Shieldstral multimodal safety classifier model as open weights under Apache 2.0.

Seeing what an agent did is only part of the picture. Defenders also need to understand why it acted, whether the system behaves safely and how attacks are evolving in the real world. 

Akamai brings insights from its State of the Internet reports and Security Intelligence Group research, drawing on real-world data to illuminate AI-era threats and explain how emerging exploits work so defenders can learn, adapt and respond.Cognition has released a trustworthiness evaluation, which measures alignment and security risks of open source-derived models. The evaluation demonstrates these risks can be mitigated via post-training.  

Numbat is Perplexity’s open source agent security suite for client endpoints. It detects, investigates, and prevents agent activity across macOS, Linux and Windows — giving defenders a structured record of what agents actually did.

Uber open sourced key components of ADR (Agentic AI Detection and Response), a production system that reconstructs the full causal chain of AI agent activity -– from prompt to reasoning, tool calls, and outcomes -– to help security teams detect threats. Today, ADR supports more than 200,000 agent sessions per day across 30,000 endpoints, using a two-tier analysis approach that combines efficient detection with deeper investigation for high-confidence threats. 

Availability and Resilience — Rapid Recovery When Moments Count

Agent systems must remain dependable under disruption, contain failures and recover safely without losing critical state or exposing the broader environment.

LangChain is adding resilience capabilities to its open source frameworks — Deep Agents, LangGraph and LangChain — enabling agents to retry interrupted work, follow a safe recovery path, resume from a saved state instead of starting over and automatically fall back to alternative models when the primary model fails.

Veeam helps organizations keep the data and infrastructure behind AI resilient and recoverable with technologies such as Kanister, its open source framework for data protection on Kubernetes. It helps teams protect and recover AI workloads, vector databases, and data to a verified known-good state.

More contributions are coming. When members publish reusable mitigations, defenders across the ecosystem can inspect, adapt and improve them, helping security practices evolve as AI advances.

Join members of the Open Secure AI Alliance at Black Hat today, Tuesday, Aug. 4, at 5:15pm PT, for a group photo outside the Main Stage, Business Hall at the Mandalay Bay Convention Center.

Learn more or share interest in joining the Open Secure AI Alliance.

Back to school means balancing assignments, deadlines and downtime. GeForce NOW makes it easy to have it all.

With cloud gaming, everyday laptops used for class can also become GeForce RTX-powered gaming setups. 

When it’s time to switch from studying to gaming, members can jump into Halo: Campaign Evolved, as GeForce NOW is bringing one of the gaming world’s most celebrated titles to nearly any device through the cloud, alongside eight total games joining the GeForce NOW library this week.

From A+ to AAA Gaming 

This back-to-school season, the easiest upgrade isn’t replacing laptops in backpacks. It’s unlocking even more from these same laptops, from creating to gaming.

With a GeForce NOW membership, Chromebooks, Macs and Surface devices can become GeForce RTX-powered gaming PCs in the cloud. The GeForce NOW library features thousands of supported PC games, letting members stream titles they already own from digital stores like Steam, Xbox PC Game Pass and Epic Games Store in just a few clicks.

The cloud keeps games up to date and ready to play, making it easy to jump into a match between classes, after homework or whenever there’s free time. Time spent waiting on downloads, patches and installs becomes time spent in game instead.

The Ultimate membership enables best-in-class experiences by delivering 5080-class performance in the cloud with NVIDIA DLSS, ray tracing and NVIDIA Reflex technologies for responsive, visually rich gameplay — all on the hardware gamers already own.

A creator known as “Menguiny” recently put this to the test after picking up a MacBook Neo. GeForce NOW turned the laptop — usually the creator’s go-to for editing videos and everyday work — into a top-notch gaming machine, too.

Another creator, “TechWithSeong” highlighted that same versatility GeForce NOW offers. Rather than juggling separate devices for work and gaming, he used GeForce NOW Ultimate to stream supported PC games across the devices he already owns — from a MacBook used for editing to gaming on the go with handheld devices.

Gamers can start with a day pass and experience all the premium benefits without a monthly commitment. Plus, the cost of a day pass can be applied toward a first membership purchase — it’s like earning a little extra credit along the way.

Everyone, at Their Stations

Experience the Master Chief’s iconic journey on GeForce NOW in Halo: Campaign Evolved — a modernized remake of Halo: Combat Evolved’s campaign — rebuilt with high-definition visuals, updated cinematics and refined gameplay. Whether discovering Halo for the first time or returning after 25 years to finish the fight, it’s as the Master Chief says: “We’re just getting started.”

Crash-land on the mysterious ringworld alongside Cortana and uncover great secrets while battling overwhelming Covenant forces. Fight through the rebuilt campaign, then continue the adventure with three brand-new missions featuring the Master Chief and Sergeant Johnson. Expanded weapons, iconic vehicles, hijackable Wraith tanks and optional Skull modifiers offer even more ways to play.

Suit up across PCs, Macs, Chromebooks, handhelds, mobile devices, TVs and more with GeForce NOW Ultimate, powered by GeForce RTX 5080-class performance in the cloud. Experience Halo with NVIDIA DLSS, ray tracing and NVIDIA Reflex technologies delivering higher frame rates and ultralow latency that keep every firefight feeling responsive and worthy of a Spartan.

From solo gaming to online co-op modes, GeForce NOW cloud saves let members log in and pick up exactly where they left off across supported devices — no lengthy downloads, extra storage space or expensive hardware upgrades required. The only thing left to carry is the mission.

Let’s Play Today

In addition, members can stream the following games this week:

• Halo: Campaign Evolved (New release on Steam and Xbox, available on Game Pass July 28)
• Mistfall Hunter (New release on Steam July 29)
• Call of Duty: Black Ops 6 (New release on Ubisoft Connect July 30)
• Sudden Attack Zero Point (New release on Steam, July 30)
• The Ranchers (New release on Steam, available July 30)
• Corsair Cove (New release on Steam and Xbox, available on Game Pass July 31)
• Funnel Runners (Steam)
• Pathogenic (Steam)

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

Which game lore are you so familiar with that you can give a school presentation on it?

— NVIDIA GeForce NOW (@NVIDIAGFN) July 29, 2026

As a discerning AI investor who values style and substance, Sarah Guo knows this season’s standout accessory isn’t the latest designer purse — but what’s inside it. 

In a recent video, Guo, founder of AI-native venture capital firm Conviction and co-host of the AI podcast No Priors, highlighted how the NVIDIA Jetson platform for edge AI and robotics gives busy developers both power and portability in one agentic-ready AI platform built for the physical world — and every industry. The best part? It all fits in one handbag.

Compact enough to carry in a bag, yet powerful enough to take on the toughest problems, NVIDIA Jetson modules and developer kits power robots, autonomous machines and real-world AI projects in classrooms, labs and makerspaces — wherever inspiration strikes — enabling developers to build using industry-transforming frontier open models at the highest standard of safety and security.

Whether for a student running their first robotics project on Jetson Orin Nano Super, a professor bringing cutting-edge AI to their curriculum on Jetson AGX Orin or a researcher pushing the limits of autonomous systems with Jetson AGX Thor, the platform enables building, learning and launching the next generation of intelligent robots — in every classroom, every lab and every country. 

As Guo shows: bring the bag, Jetson brings the robot brain. 

Check back here throughout the week for a series of examples — beginning with the Jetson Orin Nano Super below — to help developers navigate the NVIDIA Jetson platform and get started on their next robotics breakthroughs.

NVIDIA Jetson Orin Nano Super: Ideal for Building a First AI Robot

Robots deserve a real brain and, apparently, an incredibly chic commute. When Guo popped the Jetson Orin Nano Super into her Jacquemus Mini, she gained the entire AI stack right at her fingertips — now with a cute handle: clutch!

Jetson Orin Nano Super brings desktop-class generative AI to a handbag-friendly developer kit, offering first-time builders a practical path to learning computer vision, building AI agents, prototyping edge AI and more. With 67 trillion operations per second (TOPS) of AI performance, Jetson Orin Nano Super helps developers get started on the problems they’ve always wanted to solve.

To help builders move from inspiration to implementation, NVIDIA Jetson Device Skills and Jetson BSP Skills give students, researchers and developers an easier way to harness coding AI agents to create, optimize and deploy real-world AI at the edge.

Robot builders can get inspired to start their first — or next — dream project with these innovative examples of Jetson Orin Nano Super in action:

Self Driving My EV Car  | Model SidewalkPilot

A custom SidewalkPilot AI model autonomously executes maneuvers in a toy electric vehicle using Jetson Orin Nano Super.

Reachy Mini Jetson Assistant

Reachy Mini Jetson Assistant is a low-latency, fully on-device voice and vision assistant for Reachy Mini Lite powered by NVIDIA Jetson Orin Nano Super. Everything runs locally with GPU acceleration — no cloud, no API keys, no internet required at runtime. Get started here.

Building My First AI Robot From Scratch

Coding with Lewis constructed an AI-powered robot using Mistral — an open-weight model — showing that first time robotics developers can build from the ground up using Jetson Orin Nano Super.

Robotics AI Podcast

Using Jetson Orin Nano Super, Asier Arnaz built a Yocto-powered Robotics AI video podcast featuring two AI models discussing topics in real-time and highlighting what makers are creating at the edge.

Inspired yet? Get started by joining our livestream series. Across three modules, developers can learn to run generative AI, build claw agents and bring vision-language and vision-language-action models to power real-world physical AI applications — all on NVIDIA Jetson.

Bring Powerful AI to Real-World Machines With Jetson AGX Orin

With 275 TOPS of AI performance, Jetson AGX Orin is there when workloads get more complex. For Guo, it’s simple: more TOPS, more tote.

For more advanced makers, Jetson AGX Orin brings the full power of AI into coursework, capstone projects and applied research, powering advanced robots, autonomous machines and AI at the edge — and proving serious AI no longer has to live in a server room, or be reserved for those who can afford one. 

Jetson AGX Orin lets builders test real ideas in real environments, then carry the whole thing to class, the lab, the demo table or that first investor meeting.


With versatility for advanced robotics curriculum and serious startup projects, Jetson AGX Orin can support computer vision, generative AI, autonomous navigation and more, making it ideal for transforming virtually every industry, whether creating delivery bots, smart vision capabilities or industrial automation.


Go next-level with these innovative examples of Jetson AGX Orin in action:

Live VLM WebUI

A browser-based interface streams live vision language model inference on Jetson AGX Orin, letting users interact with a VLM in real time through their camera feed.

SMoRes

A Carnegie Mellon University robotics team’s autonomous system builds a 3D map of an environment while simultaneously searching for survivors in time-critical rescue scenarios.

Build Bigger Than ‘Back to School’ With Jetson AGX Thor

School’s out and robotics builders mean business. The largest workloads deserve the biggest brain and Jetson AGX Thor lives up to its name, delivering up to 2070 FP4 teraflops of AI compute and 128GB of memory. Take it from the expert: Guo big or go back to class. Of course, a bigger brain begets a bigger bag.

As the ultimate platform for next-generation humanoid robots and autonomous systems that require sophisticated real-time reasoning at the edge, this is the brain every robot has been waiting for. Jetson AGX Thor delivers server-class compute and AI agent capabilities for the most challenging workloads in labs, factories and the field — across every industry, in every corner of the world.

With Jetson AGX Thor, the future is officially in the bag. Go big with these innovative examples of Jetson AGX Thor in action:

Matcha Bot

A first-place hackathon entry from University of Illinois Urbana-Champaign’s SIGRobotics team uses two robotic arms running the NVIDIA Isaac GR00T N1.5 model on Jetson AGX Thor to autonomously pour, prepare and whisk matcha.

Multimodal AI Studio

A creative development environment on Jetson AGX Thor combines vision, audio and language inputs to enable rich multimodal AI experimentation at the edge.

Anyone can make a robot move. Jetson makes it think, putting frontier AI in the hands of every builder, everywhere. So, which NVIDIA Jetson module fits best? Start with one that’s right and then upgrade when ready. Isn’t that right, Guo?

Learn more about NVIDIA Jetson.

Open source software is a critical pillar of the global economy. It underpins cloud computing, financial services, manufacturing, telecommunications, government and internet services by making technology accessible and observable to communities of experts. 

Cybersecurity is among the top three beneficiaries of open source software. The Open Secure AI Alliance — building on the leadership of the Linux Foundation’s Akrites initiative and OpenSSF community work — will work to remediate and disclose vulnerabilities using open technologies. 

Just as open source created a shared foundation for software, the United States and its partners now face a choice in AI security: whether the defenses that protect our infrastructure will sit inside a few opaque systems or be built on open models, harnesses and tools that any defender can study, adapt and deploy.

The world needs both closed and open models. For cybersecurity, open models and open harnesses are essential because they democratize defensive capabilities, increase transparency for defenders, enable cyber defense while protecting data, and complement frontier closed models with customizable, localized controls. Open source enables massively distributed community-driven and self-controlled defense – with no single point of failure. 

Open models, like any powerful technology, can be misused — including through attempts to weaken safeguards or repurpose capabilities for cyber attacks — but those risks are not unique to open systems, and they must be managed wherever advanced AI is deployed.

The recent Hugging Face security incident delivered a clear reminder: cyber defenders need open, frontier agentic systems for self-defense. When closed AI tools — unable to distinguish attackers from defenders — blocked essential forensic analysis, Hugging Face ran the open-weight GLM 5.2 model on its own infrastructure to analyze more than 17,000 actions and contain the intrusion.  

That incident showed a practical truth: when defenders cannot inspect, adapt and run advanced AI on their own infrastructure, their ability to respond is constrained at exactly the moment speed matters most. Companies and countries need open frontier defensive tools and techniques so critical industries can build security systems across a multi-vendor ecosystem and avoid single points of failure.

That is the mission of the Open Secure AI Alliance: to ensure defenders everywhere have open, frontier tools they can trust and control.

Leaders across cloud computing, cybersecurity, enterprise software, open source foundations and AI research — including NVIDIA, Adobe, Amdocs, AHEAD, Aible, Akamai, Amazon, Anyscale, Arcade Dev, Arteris, Atlassian, Box, Cadence, Canonical, Capital One, Check Point, Checkmarx, Cisco, ClickHouse, Cloudera, Cloudflare, Cloudsmith, Cognition, Cohere, Cohesity, ControlPlane, Commvault, CrowdStrike, Crusoe, Cyberhaven, Databricks, Datadog, Dataiku, DDN, Dell Technologies, DepthFirst AI, Docker, DoorDash, Dream Security, Echo, Elastic, EleutherAI, Endor Labs, Exaforce, F5, Factory AI, Fireworks AI, Fortanix, Fortinet, G42, Genspark, GitHub, Glean, H2O.ai, HPE, Hugging Face, IBM, Infoblox, Infosys, Intel, JFrog, Kindo AI, Kong, Kyndryl, LangChain, Ledger, Lenovo, the Linux Foundation, Microsoft, Mirantis, Mistral, Mozilla, NAVER, NEAR AI, NetApp, Netskope, Nokia, Nous Research, NTT, Nutanix, Okta, OpenClaw, OpenHands, Palantir, Palo Alto Networks, Perplexity, Pinterest, Poolside, Red Hat, Reflection AI, Replit, Rubrik, Salesforce, Samsung Electronics, SAP, SentinelOne, ServiceNow, Siemens, SK Telecom, Snowflake, Snyk, Sonar, SpaceXAI, Spectro Cloud, SUSE, Synopsys, Thales, Thinking Machines Lab, TrendAI, Uber, Upwind, UiPath, Veeam, Visa, vLLM, VMware by Broadcom, VotalAI, Wiz, Workday, World Wide Technology, Zenity and Zscaler are inaugural partners in the Open Secure AI Alliance, a movement to develop and share open technologies, techniques and tools to safeguard software and agents in the age of AI.

Some argue that open models are inherently less safe because they can be misused for cyberattacks or modified to remove guardrails. Those risks are real, but they do not disappear in closed systems, and simply keeping weights closed does not prevent determined attackers from seeking or exploiting powerful AI.

The right response is not to deny defenders access to capable open systems. It is to pair openness with strong safeguards, clear rules against malicious misuse, rigorous evaluation and rapid remediation. In cybersecurity, the safer path is the one that gives more defenders the ability to test, verify and strengthen the systems on which society relies. 

Defenders need both frontier closed models and frontier open models, working together, so they can choose the right system for the job and ensure that transparency, adaptation and sovereign control are available wherever security demands them.

Open Research Enhances Cybersecurity and AI Safety

An AI agent isn’t just a language model. It is a complex system built from models, harnesses and guardrails. 

Real AI safety and security depend on the full agent stack — identity, permissions, harnesses, guardrails, logs and evaluation — not just on whether model weights are open or closed. Open harnesses and tools make those controls easier for many defenders to inspect, test and improve.

NVIDIA is contributing open models, model weights, data and new agent harness research to the Open Secure AI Alliance to speed the development of new cybersecurity tools and techniques.

The new open source NVIDIA Labs Object-Oriented Agent (NOOA) project is now available on GitHub to make advanced AI safety capabilities more accessible for agent harnesses. The NOOA research framework enables harnesses to better integrate with models to make agent behavior easier to test, trace, audit and govern.

Across the Alliance, contributors are building an open defense stack for agents — from identity and isolation to safe model formats, multi-model scanning and secure coding workflows. 

HPE contributes to SPIFFE/SPIRE, which creates zero-trust identity framework standards and methods that can cryptographically verify AI agents and services to ensure only authorized workloads communicate and access enterprise resources. 

Hugging Face has offered Safetensors — a safe format to store AI model weights, providing transparency and guarantees of no remote code execution — to the PyTorch Foundation. 

IBM and Red Hat’s Lightwell extends security across the open source supply chain with digitally signed patches. 

Microsoft’s MDASH multi-model agentic scanning harness orchestrates specialized AI agents to discover, debate and prove exploitable bugs. 

SpaceXAI has open sourced the Grok Build terminal-based AI coding agent to promote trust, transparency and new capabilities, and plans to open source the weights of the Grok line of models to support the developer and research communities.

A Call to Policymakers and Regulators

As policymakers and regulators grapple with AI safety, it will be crucial to recognize open models, harnesses and security tooling as defensive assets, not liabilities, in AI and cybersecurity policy. Blanket restrictions on open frontier AI systems would weaken defensive capacity and risk concentrating power, dependence and vulnerability in a few closed providers.

Companies and governments should invest in shared open infrastructure for AI defense — datasets, evaluation frameworks, attack simulators and red-teaming tools — much as past generations invested in open source software.

The Future We Should Build

The age of AI agents can be one of resilience and shared security. With the right choices, open secure AI systems can give defenders the tools they need, strengthen competition, extend technological leadership and ensure that the safety and security of this extraordinary technology are built in the open for everyone. 

That future will not be secured by assuming that secrecy alone is safety. It will be secured by building systems that are strong enough to withstand scrutiny, flexible enough to be improved and open enough to mobilize the full community of defenders.

That future is worth building — and the Open Secure AI Alliance invites governments, industry and researchers to join in the work of defending the AI era together.

Learn more or share interest in joining the Open Secure AI Alliance.

The complexity of modern chip design continues to grow as engineering teams work to develop increasingly sophisticated CPUs, GPUs and AI systems. To help meet that challenge, NVIDIA is collaborating with industry leaders Cadence and Synopsys to optimize critical electronic design automation (EDA) applications for the NVIDIA Vera CPU.

NVIDIA is now deploying Vera across EDA workflows used to develop its next generation of CPUs and GPUs, demonstrating how high-performance CPU architecture can help accelerate some of the industry’s most demanding engineering workloads.

Accelerating Critical EDA Workloads

What’s at stake is the pace of chip production. In turn, the tempo of industry technologies that stand to benefit from boosted EDA workloads, driving development momentum.  

Simulation, verification and implementation technologies play a central role in semiconductor development. Long before a chip reaches manufacturing, engineers spend years validating behavior, identifying corner cases and refining designs through thousands of iterations.

While GPUs and AI have accelerated many aspects of chip design, several critical EDA workloads remain heavily dependent on CPU performance. Logic simulation, formal verification and portions of digital implementation often depend on fast individual cores, efficient memory systems and strong overall throughput.

That makes CPU architecture an important factor in determining how quickly engineering teams can validate designs, explore alternatives and move products toward tapeout.

Highlighting Early Results With Cadence and Synopsys

NVIDIA’s initial testing includes several leading EDA applications. The results highlight Vera’s ability to accelerate two of the most compute-intensive stages of modern chip design. Early testing on selected production-class workflows shows promising results. 

Cadence Jasper, a formal verification platform, uses smart proof technology and machine learning to find and fix bugs and improve verification productivity early in the design cycle.

Synopsys VCS, a high-performance functional verification solution used to simulate and validate complex chip designs before fabrication, used the same number of cores in the test. 

Both applications showed up to 1.5x higher performance on selected workloads.

Beyond benchmark results, NVIDIA is working closely with both companies on application profiling, software optimization and system-level tuning designed to improve engineering productivity across a broader range of workflows over time.

Bringing Vera to the Design Process

NVIDIA is deploying Vera throughout the EDA workflows used to create future NVIDIA processors.

Vera combines 88 custom NVIDIA Olympus CPU cores with a high-efficiency LPDDR5X memory subsystem and second generation NVIDIA Scalable Coherent Fabric designed to deliver strong per-core performance, high memory bandwidth and consistent low latency for demanding engineering applications.

These capabilities are particularly important for workloads that mix latency-sensitive jobs with large-scale regression testing across compute farms. Faster execution can shorten individual verification runs, while greater throughput enables engineers to evaluate more design alternatives and complete more validation within the same development window.

Going From RTL to Silicon

After defining a processor’s architecture and microarchitecture, engineers describe much of its behavior at the register-transfer level (RTL). Multiple verification and implementation technologies then work together to transform that design into manufacturable silicon.

These workflows span logic simulation, formal verification, regression testing and digital implementation, helping engineers validate functionality, identify corner cases and transform designs into manufacturable silicon.

Because these stages are interconnected, improvements in verification throughput can help organizations identify issues earlier and reduce costly downstream design iterations.

Building Future NVIDIA Chips With NVIDIA CPUs

The deployment of Vera across NVIDIA’s own engineering workflows reflects a broader strategy: accelerate each workload with the compute architecture best suited to the task.

In EDA, GPUs and AI continue to speed many algorithms, while high-performance CPUs remain essential for critical simulation, verification and implementation workloads. Together, they help improve the performance of the overall design cycle.

Looking ahead, NVIDIA plans to build on Vera with the next-generation Rosa CPU, powered by the NVIDIA Rigel core, while continuing to optimize leading EDA applications across its CPU roadmap.

By using NVIDIA CPUs to help design future NVIDIA CPUs and GPUs, the company is creating a continuous feedback loop between silicon design, software optimization and systems engineering, with each generation helping build the next.

Learn more about NVIDIA at DAC 2026.

At this week’s AI Summit in San Francisco, South Korean President Jae Myung Lee and some of the country’s top business leaders and researchers are meeting with NVIDIA and ecosystem partners to chart Korea’s AI progress.

Building on NVIDIA founder and CEO Jensen Huang’s visit to Korea last month, this week’s discussions and announcements advance the nation’s full-stack push to expand Korea’s AI infrastructure and expertise on its path toward becoming a global center for AI innovation. 

To start, NVIDIA and the Korea Advanced Institute of Science and Technology (KAIST) today announced a joint AI research lab at the KAIST Kim Jaechul Graduate School of AI in Seoul, dedicated to advancing agentic AI for South Korea. It’s the first joint AI lab between a Korean university and any global technology company.

The collaboration will establish a robust academic AI research program, bringing together NVIDIA full-stack AI expertise, NVIDIA Nemotron open models and NVIDIA AI Cloud partner computing with the world-class scientific talent at KAIST, one of Asia’s premier research universities.

Check back here for updates as the summit continues, including Huang’s meetings with President Lee as well as Korea and U.S. business leaders.

Friday, July 24, at 10:10 p.m. PT

NVIDIA and Korea Partners Announce Next Wave of AI Innovation

NVIDIA and its Korea ecosystem partners — from NAVER, SK Telecom and Hyundai Motor Group to the nation’s top research universities, KAIST and Seoul National University (SNU) — are committing to developing national AI factories and physical AI platforms, as well as advancing joint research in agentic AI. 

Core to it all: NAVER, Brookfield and NVIDIA will expand NAVER’s NVIDIA DSX AI factory at its GAK Sejong data center to 200 megawatts — roughly 100,000 GPUs, built on the NVIDIA Vera Rubin platform — more than tripling the initial buildout announced in June. 

Plus, SK Group and NVIDIA today announced plans for a $500-billion-plus comprehensive partnership to establish AI infrastructure serving the surging demand for global compute. Broadening initial plans announced in June, the expanded collaboration will deploy NVIDIA Vera Rubin infrastructure powered by SK hynix HBM4 memory.

And Hyundai Motor Group outlined a physical AI strategy anchored by a robot reference platform developed with NVIDIA, extending a buildout with 50,000 NVIDIA Blackwell GPUs. The collaboration also includes the integration of NVIDIA DRIVE Hyperion — an autonomous vehicle development platform combining NVIDIA DRIVE AGX in-vehicle computing running on the safety-certified NVIDIA DriveOS operating system — with HMG’s vehicle platforms.

NVIDIA Deepens Collaboration With Top Korea Universities

NVIDIA and KAIST’s Department of Mechanical Engineering are planning an NVIDIA AI Technology Center (NVAITC) for collaborative research, talent development and knowledge exchange in physical AI — including projects that tap into NVIDIA Nemotron open models. 

This is in addition to the NVIDIA-KAIST joint research lab for agentic AI at the KAIST Kim Jaechul Graduate School of AI in Seoul — built for the Korean language and local industry, backed by a multiyear commitment to fund and train Korean researchers.

NVIDIA and Seoul National University are also planning an NVAITC covering AI research, talent development and education — spanning foundation models, accelerated computing, physical AI and AI for science, with potential cooperation on AI infrastructure. This will include work harnessing NVIDIA Nemotron and NVIDIA Cosmos open world foundation models.

Friday, July 24, at 10:10 p.m. PT

Huang and President Lee Meet to Discuss Future of AI

President Lee met with Huang Friday at the Fairmont San Francisco ahead of the AI Summit to discuss deepening the partnership behind Korea’s national AI ambitions.

President Lee opened by noting he’d seen reports of Huang eating beer and fried chicken in Seoul — and that he’d hoped to join. 

Huang said the enthusiasm he felt across Korea during the visit, “from the fried chicken restaurant to the Korean barbecue restaurant, was incredible. Everyone in Korea loves AI.”

The meeting was substantive. President Lee outlined South Korea’s vision to become a pivotal hub in the global AI supply chain — spanning semiconductor manufacturing, AI infrastructure deployment and industrial integration.

Huang noted that NVIDIA and Korea have been partners for over 25 years — from PC gaming to the AI revolution — and committed to deepening that partnership across AI infrastructure, semiconductors, physical AI and research.

“From AI chips and physical AI to AI infrastructure, Korea has achieved remarkable results under your leadership over the past year,” Huang said. “This is truly the beginning of a golden age for Korea.”

President Lee’s response: “I hope this is only the beginning.” 

Friday, July 24, at 7:45 p.m. PT

Korea’s AI Moment: Tech Titans Convene to Build a Full-Stack Future

The sheer gravity of the global AI boom was displayed Friday on stage at the Midway, a premier event venue in San Francisco. The city’s Dogpatch neighborhood has never been so well dressed. South Korean President Lee Jae Myung laid out a bold vision: South Korea isn’t merely going to participate in the AI revolution. It’s aiming to be one of its key engines.

The gathering was a high-stakes meeting of global compute leadership. Moderated by Stanford University’s Soh Kim, the stage brought together an eye-popping lineup of tech leaders. Sharing the floor with President Lee: NVIDIA’s Jensen Huang, OpenAI’s Sam Altman, Broadcom’s Hock Tan and Anthropic’s Dario Amodei.

Joining them was South Korea’s industrial vanguard — Samsung Electronics Chairman Jay Y. Lee, SK Group Chairman Chey Tae-won, Hyundai Motor Group Executive Chair Euisun Chung and NAVER founder Hae-jin Lee.

President Lee opened with his “San Francisco AI Declaration,” drawing a line from the city’s rebirth after the 1906 earthquake to Korea’s post-war rise into an IT powerhouse. He pitched a multitrillion-dollar push into AI semiconductors, hyperscale data centers and physical AI, comparing the emergence of artificial intelligence to “humanity discovering fire anew.” He framed Korea’s ambition around compute infrastructure, education and public services designed to spread AI’s benefits broadly. 

Huang matched the moment. Looking back over a 25-year partnership, from seeding Korea’s gaming culture to the memory chips that make modern AI run, he put it simply: “If not for the invention of HBM memory in Korea, NVIDIA wouldn’t have been able to invent the supercomputers that power AI today.

“This really is the golden age for Korea,” he added. 

SK Group’s Chey Tae-won gave the room a sense of what the demand actually looks like at this scale. Every time he has dinner with Huang, Chey said, Huang tells him “more chips” … and he predicted that whatever number Huang had quoted that day, he’d ask for more the next time they met. 

Altman didn’t dress it up: “There really would not have been what we have, this incredible AI revolution, without Korea.” 

Amodei said Korea plays a role “throughout the stack, from semiconductors to infrastructure to an incredible concentration of talent.”

The message out of the Midway was clear. The AI era isn’t just being built in Silicon Valley. Korea is helping build it.

Friday, July 24, at 2:00 p.m. PT

Korean Tech Leaders Visit NVIDIA Santa Clara Campus

Samsung Executive Chairman Lee Jae-yong, Hyundai Motor Group Executive Chair Euisun Chung, and NAVER founder and Chairman Lee Hae-jin joined NVIDIA CEO and founder Jensen Huang on a tour of NVIDIA’s headquarters in Silicon Valley. 

The Korean business leaders posed for photos with Huang in the lobby of NVIDIA’s Endeavor building — its angular design a nod to the triangles that are the building blocks of modern computer graphics — as employees worked at their desks around them.

Thursday, July 23, 9 p.m. PT

NVIDIA and SK Celebrate Longstanding Collaboration Over Dinner

On the eve of the summit, Huang, SK Group Chairman Chey Tae-won, and the SK hynix, SK Telecom and NVIDIA teams gathered for dinner in Woodside, California. It was a warm welcome to Silicon Valley on a warm summer evening for the Korean business leaders. 

NVIDIA and SK — which have had a longstanding partnership — announced in June an expanded collaboration to codevelop memory for NVIDIA platforms spanning AI infrastructure, personal AI and physical AI. 

Also in June, SK Telecom announced plans to build AI infrastructure to power Korea’s innovation in physical AI, robotics and more.

Learn more about NVIDIA’s work with Korea ecosystem partners.

Lock in and load up the cloud. GFN Thursday brings fresh updates and new adventures, all ready to play without waiting for downloads.

Set sail in Path of Exile: Curse of the Allflame and charge in Battlefield 6 Season 4 both launching major content for members this week.

Then revisit Capcom legends like Breath of Fire IV, Dino Crisis and Dino Crisis 2, jump into Halo: Campaign Evolved Advanced Access and discover nine titles arriving on GeForce NOW.

The Curse Calls

Prepare to brave the unforgiving waters of Wraeclast. Path of Exile: Curse of the Allflame launches Friday, July 24, sending Exiles into the perilous Frozen Seas.

Guided by formidable corsair captain Valerie and her cursed navigator Vesper, gamers must piece together scattered treasure charts, board the legendary vessel — the Sovereign — and chart a course into the abyss. Descend into the depths using Allflame Lanterns to uncover valuable relics while surviving the dangers lurking beneath the waves.

The update introduces sweeping changes, including a complete rework of sockets and links. The Scion also gains the all-new Luminary Ascendancy, the Mercenaries of Trarthus return alongside mysterious Atlas Anomalies, and the preexisting Abyss, Legion and Talisman mechanics receive revitalized gameplay and exclusive rewards.

GeForce NOW members can dive in the moment it launches across their devices, no downloads needed. Learn more about the latest additions on the Path of Exile website.

Rise Through the Ranks

Battlefield 6 takes the fight further with Season 4, bringing new battlegrounds, expanded progression and new ways to play. 

Take to the land, air and sea with the massive Tsuru Reef and the return of the fan-favorite Wake Island, coming in phase two. Both feature aircraft carriers with operational flight decks, new naval vehicles and a dynamic wave system that helps move every match. The update also introduces Custom Lobbies and Spectator Mode, delivering more ways to play.

From large-scale multiplayer battles to intense vehicle combat, GeForce NOW lets members deploy across nearly any device. Ultimate members can stream with GeForce RTX 5080-powered performance across PCs, Macs, handhelds, mobile devices, TVs and more.

Whether returning to the frontlines or enlisting for the first time, members can leap into the latest Battlefield update the moment it arrives on GeForce NOW — without waiting for patches.

Dragons and Dinosaurs — on Any Device

Three iconic Capcom adventures arrive on GeForce NOW this week, ready for longtime fans to revisit and new players to discover.

Journey through the rich fantasy world of Breath of Fire IV, a role-playing game following the intertwining stories of Ryu and Fou-Lu. Master an array of skills and magic to unlock strategic victories in battle and go head to head with unique enemies using powerful Dragon Transformations. 

Raw instinct takes over in Dino Crisis and Dino Crisis 2, the survival-horror series that combines pulse-pounding action, resource management and thrilling dinosaur encounters. Pursued by a relentless prehistoric terror, players fight to survive from the facility to the world of the Cretaceous Period.

Whether reliving unforgettable moments or discovering these legendary titles for the first time, gamers on GeForce NOW can experience gaming history on supported devices like Chromebooks, Fire TV and Steam Deck — no legacy hardware required.

Spartans — Start Early

Spartans don’t have to wait. Players who prepurchase the Premium Edition of Halo: Campaign Evolved can suit up for Advanced Access before the game’s global launch on Tuesday, July 28. 

Start the weekend with all the new games arriving on GeForce NOW:

• ZeroSpace (New release on Steam, available July 20)
• The Life and Suffering of Prince Jerian (New release on Steam, available July 20)
• The Planet Crafter (New release on Xbox, available on Game Pass July 21)
• Carnival Hunt (New release on Steam, available July 23)
• Dinoblade (New release on Steam, available on July 23)
• Breath of Fire IV (Steam)
• CloverPit (Xbox, available on Game Pass)
• Dino Crisis (Steam)
• Dino Crisis 2 (Steam)

The last word goes to the Community Corner. One GeForce NOW member recently shared they found themselves back on GeForce NOW Ultimate because they missed the flexibility of gaming across devices like Steam Deck. Their post sparked a discussion about why members tap into cloud gaming, whether as a backup, a travel companion or their primary way to play.

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

NVIDIA founder and CEO Jensen Huang today visited the Naval Postgraduate School in Monterey, California, to commission an NVIDIA DGX GB300 system — bringing one of the world’s most powerful AI platforms fully online for the students, researchers and faculty at the U.S. military’s flagship graduate university.

“Our nation depends on our men and women who fight on the front lines. Nothing is more valuable to you than information and insight, and information and insight in a timely way, and to understand its impact and consequence,” Huang said at the event. “I can’t imagine anything more important.”

The DGX GB300 supercomputer with NVIDIA Mission Control software gives NPS’s more than 1,500 in-resident students and 600 faculty on-premises access to large-scale AI computing, including model training and inference capability for applications spanning weather prediction, cybersecurity, and disaster resilience and response planning.

The commissioning, which took place during the school’s three-day Converge @ NPS event, marks the latest chapter in an ongoing collaboration to develop AI-based technologies at NPS for education and real-world applications. 

It’s built based on an NVIDIA AI Technology Center on the Monterey campus — a dedicated hub for AI research and graduate instruction, now anchored by the DGX GB300.

“As we modernize our technology, we must also modernize how we educate our leaders,” said Admiral Samuel Paparo, commander of the U.S. Pacific Command. “Access to advanced computing capability means NPS students and faculty understand the opportunities and responsibilities that come with these technologies.”

Training Leaders to Build With AI

NPS educates active-duty officers and international partners across disciplines — from space operations to ocean science — awarding graduate degrees while centering each program on applied research with direct relevance to real-world problems. 

“Leadership is about being in service of something else. You’re in service of your team. You’re in service of your country. You’re in service of your mission,” Huang said to attendees. “And all of this technology is just tools to help you.” 

The DGX GB300 gives those programs new technical depth: the ability to train foundation models in house, run high-fidelity simulations at scale and develop AI tools with immediate applicability.

Huang told attendees that even if they aren’t experts in computer science, today’s computers running AI make it easy. 

“The most important advice that I would give to someone is to engage the technology,” Huang said. “It’s not as hard as you think it is, and the reason for that is because, on first principles, technology is supposed to get smarter and smarter over time.” 

NVIDIA has also expanded the collaboration through its Deep Learning Institute, putting instructor toolkits in the hands of NPS faculty so AI is woven through graduate curricula across departments. 

“Many of you will command in AI-enabled environments. Information will move at lightning speeds, compressing response times,” Paparo said. “Our advantage will come from leaders, like you, who can use the technology to see, understand, decide and act faster while exercising the judgment, experience and leadership that machines cannot provide. That education is happening here at NPS.”

From Ocean Models to Digital Twins

NPS runs a regular cadence of hackathons that have produced advances in autonomy, ocean research and operations planning — applied work that demands serious computing to scale. 

Researchers are using AI to model sea conditions, predict atmospheric changes and build digital twins of complex environments — work that feeds directly into the scientific domains where NPS has deep expertise.

The DGX GB300 will now power these workloads on campus. 

Through the school’s partnership with MITRE, a nonprofit organization, NPS has developed a framework built on NVIDIA Omniverse libraries that provides high-fidelity digital twin environments for simulating real-world navigation and decision-making under uncertain conditions.

To learn more, watch NPS and NVIDIA researchers present on AI modeling and simulation at NVIDIA GTC Washington, D.C.

NVIDIA Partner Ecosystem Brings AI Infrastructure to Life

The DGX GB300 at NPS didn’t come together in isolation. DDN, VAST and Vertiv joined the effort as hardware and systems integration partners. 

DDN contributed high-performance data infrastructure to help researchers efficiently access, manage, protect and scale data for demanding AI workloads. 

VAST Data provided a unified data platform to enable secure data access and management across edge, core and cloud environments. 

Vertiv provided the racks, cooling and power infrastructure needed to bring the system online, as well as installation sequencing, testing and commissioning support, and fluid management and monitoring. 

Learn more about NVIDIA’s collaboration with NPS and explore the NVIDIA DGX GB300.

Before a healthcare robot can be useful in the real world, it has to learn how the physical world pushes back. Anatomy varies. Instruments bend, press, slip and interact with tissue. Imaging can be noisy or incomplete. And the rare, edge scenarios developers most need to understand don’t appear on schedule.

That creates one of the biggest bottlenecks in healthcare robotics: obtaining the enormous amount of varied data developers need to train, test and improve robot behavior.  

NVIDIA Medical Physics Simulation framework — a new open source, GPU-accelerated capability within NVIDIA Isaac for Healthcare — announced today, helps medical robotics developers model anatomy-device interaction, generate hard-to-capture scenarios, test in silico, and train or evaluate robot policies before hardware-heavy testing. 

The framework brings together anatomy and medical device behavior with sensor simulation and robot learning so teams can create reusable simulation environments instead of rebuilding custom scenes for every workflow, saving developers time and bringing innovations to market faster. 

Because Medical Physics Simulation is open source, healthcare robotics developers can inspect the framework, adapt it to their own devices and workflows, and build on a GPU-accelerated foundation that works seamlessly with the broader NVIDIA stack.

Open source is especially important in healthcare because teams need transparency into the data, models and weights that shape system behavior. Access to open models and model weights can help developers reproduce results, evaluate performance across different anatomies and scenarios, identify limitations and build evidence for regulatory review. 

A Virtual Training Ground for Medical Robots

For physical AI, experience is data in motion. Developers need to train robots to operate properly even when anatomy changes, devices behave differently, conditions shift or a policy fails unexpectedly.

Medical Physics Simulation helps developers simulate anatomy, device contact, friction and sensor inputs, then test in interactions and environments to evaluate how robots perform across those changes. Powered by NVIDIA CUDA and part of Isaac for Healthcare — built on the NVIDIA Warp, Newton and Cosmos simulation and generative AI technologies — the framework can run hundreds of parallel simulation environments, helping teams explore more scenarios and identify failure modes earlier in development. 

For robot builders, this turns simulation from a bespoke engineering project into reusable infrastructure. The difference now is scale: benchmarks show 8,192 robot-training environments running in parallel with GPU-native simulation cut training from over five hours to under two minutes. 

With this framework, developers can connect vascular anatomy, flexible instruments such as catheters and guidewires, simulated X-ray imaging and reinforcement learning. The framework is designed to extend beyond that example to additional devices, anatomies, sensors and healthcare robotics domains.

Medical Physics Simulation brings together classical physics simulation and generative AI physics simulation. Classical simulation helps model known physical rules, such as device contact, friction and motion. NVIDIA Cosmos-H Dreams, the real-time generative AI physics simulation capability within Medical Physics Simulation, helps model visual scene dynamics learned from procedural data.

Together, these approaches give developers a richer way to build and test healthcare robotics systems in virtual environments before moving to physical prototypes and lab testing.

An Ecosystem Building the Future of Medical Robotics

Medical robotics leaders are already applying simulation-driven development to solve specific surgical challenges.

CMR Surgical and Cambridge Consultants, part of Capgemini, are using Cosmos-H-Dreams to implicitly learn interaction physics for soft-tissue surgical procedures and generate patient-specific simulations. CMR contributed nearly 500 hours of anonymized clinical data from its Versius Surgical Robotic System to the Open-H Embodiment open dataset, benefiting procedures including cholecystectomy, prostatectomy, hernia repair and hysterectomy.

“Open source models allow us to build on shared knowledge, accelerating responsible innovation and, ultimately, gives us the potential to deliver more consistent care and better outcomes for patients worldwide,” said Chris Fryer, chief technology officer at CMR Surgical.

Johnson & Johnson MedTech is using Isaac for Healthcare’s Medical Physics Simulation and a Cosmos-based foundation model to build digital twins of its endoluminal MONARCH platform for urology, modeling complex anatomy and kidney-stone scenarios.

XCath is using the Medical Physics Simulation for endovascular autonomy policy training. Inner Logic is accelerating the evolution of medical technology with synthetic data, validating device mechanics and producing in silico evidence to support regulatory pathways with NVIDIA Medical Physical Simulation.

Medtronic Structural Heart is exploring applying Medical Physics Simulation with simulated X-ray sensing to generate data for catheter navigation research.

A New Layer in the Isaac for Healthcare Stack

As a modular capability within NVIDIA Isaac for Healthcare, Medical Physics Simulation can be used on its own or alongside digital twin pipelines, medical sensor simulation, the NVIDIA Isaac Lab open robot-learning framework and NVIDIA open models and policies.

Developers can explore the open source Medical Physics Simulation framework, review available reference workflows and start building simulation environments for their own devices, anatomies and healthcare robotics applications.

The AI era runs on AI infrastructure. Many of these advanced systems are built and tested in Texas.

Wistron opened its first U.S. manufacturing facility today in Fort Worth — a 324,000-square-foot greenfield plant producing superchips at the heart of some of the world’s most capable AI systems.

In front of an audience of Wistron executives, Taiwan government officials and local Fort Worth leaders, NVIDIA founder and CEO Jensen Huang took the stage with Wistron Chairman Simon Lin at the factory’s opening ceremony to talk about what it means to build the infrastructure for the AI era in America.

“Manufacturing is an essential pillar for every economy and every country,” Huang said. “Building chip plants, packaging plants, computer system plants like this, and AI factories all over the United States, has allowed the United States to really reindustrialize for the first time in a long time.”

The plant — Wistron’s D1 facility — currently runs two manufacturing cells: one producing the NVIDIA GB300 Grace Blackwell Ultra Superchip and one that will produce the NVIDIA Vera Rubin Superchip. D1 is scaling up this year to produce tens of thousands of boards per month of the most advanced technology in the industry. 

Building AI at scale takes more than chips and code. It takes factories, supply chains and skilled workers — a domestic manufacturing base capable of building at volume and adapting as the technology moves. The Fort Worth plant is where that capacity takes shape.

A $700 Million Commitment

The state-of-the-art facility represents a $700 million commitment to advanced manufacturing in the U.S. and has created over 500 new jobs across the Texas facility, with plans to expand to 1,000 by the end of the year. 

“It’s creating jobs in plumbing, construction, manufacturing and electricians,” Huang said. “So many different types of jobs here in the United States.”

“From the macro view of the whole AI world, down to community and country — it’s a great vision,” Lin said. “I think we’re going to enjoy the next era of AI life. I hope that we will be happier and healthier.”

NVIDIA has committed to manufacturing up to $500 billion in advanced AI platforms in the United States. The Wistron Fort Worth factory is one of the projects making that number real.

Building First in a Virtual World

Wistron designed and fully simulated the entire facility before construction began in a digital twin built on NVIDIA’s open platform, including Nemotron and Cosmos frontier models, Omniverse and Metropolis libraries, and open frameworks including PhysicsNeMo. 

That enabled Wistron engineers to validate assembly line layouts, optimize production processes and train workers on standard operating procedures virtually — before any system came online in the real factory. 

Winning Every Layer

The AI era isn’t just about chips — it’s about winning at every layer of the stack, from semiconductors to systems to the factories that build them.

“Just as agriculture is a fundamental infrastructure of society, in the future these AI factories will be a fundamental infrastructure of society,” Huang said. “Today we have roads and agriculture and railroads. In the future we’ll have AI factories and the internet. Electricity comes in … and the tokens get generated in the factories.”

The ceremony included the unveiling of the first NVIDIA GB300 Grace Blackwell Ultra Superchip produced on site, which Huang signed.

Huang described the system built around the NVIDIA GB300 Grace Blackwell Ultra Superchip as “the most powerful AI supercomputer in the world.”

“It has one-and-a-half million parts inside — two tons, 4 million dollars — and we’re producing them like phones, right here in Wistron, cranking them out in volume because the world needs all of these machines to drive the intelligence infrastructure,” Huang added. 

Huang also sat down for interviews with Mike Allen of Axios and separately with Lana Ferguson of the Dallas Morning News — who also covered Coherent’s groundbreaking of a new manufacturing facility in Sherman in June.

A new chapter of American manufacturing is taking shape in Fort Worth. Wistron’s factory is part of NVIDIA’s broader commitment to build in America, for America — strengthening U.S. supply chains, creating high-skilled jobs and giving American communities a direct role in building the future with AI.

NVIDIA Vera Rubin is here, and it’s going gigascale.

Vera Rubin NVL72 production is ramping up with racks running at partners CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Nebius. Spanning 350+ factory sites in 30 countries, Vera Rubin has the largest, most mature rack-scale supply chain ever assembled to meet customer compute demand.

The Vera Rubin platform is built from chip to grid to deliver the highest performance per watt and the lowest token cost. CoreWeave’s first benchmark on DeepSeek-R1 says it all: 10x more throughput per megawatt than Grace Blackwell NVL72 — landing directly on the metric that matters most for power-constrained AI factories.

Advancing Performance With Extreme Codesign

What makes this possible is extreme codesign across seven chips and five rack trays — Vera Rubin NVL72, Vera CPU rack, Groq 3 LPX, Spectrum-6 SPX and Vera BlueField-4 STX — all engineered as a single system rather than assembled from separate off-the-shelf products. 

The NVIDIA Vera CPU is at its center. It redefines what an AI factory CPU can be. Designed and built for the agent era, its custom Olympus core delivers 2x single-threaded performance, 3x core-to-core bandwidth and 40% lower memory latency versus competing chiplet designs, making it the most efficient single-threaded CPU for the agentic workloads that matter most.  

Accelerating AI Factories With Purpose-Built Networking 

For networking, the platform’s sixth-generation NVLink scale-up delivers more than 2x throughput on complex workloads, 3x lower latency and 10x higher packet rates than off-the-shelf Ethernet. For scale-out, Spectrum-X Ethernet combines 102.4T Spectrum-6 switch systems, 1.6T ConnectX-9 SuperNICs, adaptive routing, advanced congestion control, telemetry and open operating system support, enabling 1.6x higher RDMA bandwidth than off-the-shelf Ethernet. 

The world’s leading AI infrastructure builders — including CoreWeave, Microsoft, SpaceXAI and Tesla — are among the first to bring in Spectrum-6 switches to accelerate their AI factories. NVIDIA Photonics with co-packaged optics for scale-out — the industry’s first such switch in volume manufacturing — adds 5x lower power and 10x higher MTBI versus pluggable transceivers, with CoreWeave, Lambda and OCI among the first adopters. 

Spectrum-XGS Ethernet extends performance across sites with 1.9x multi-site throughput because gigascale AI isn’t a single building problem. 

And NVLink Fusion opens the NVIDIA infrastructure platform to third-party XPUs, giving partners a faster path to market on the proven NVLink scale-up stack and ecosystem.

Saving Setup Time, Water 

NVIDIA’s three generations of rack-scale codesign produced a Vera Rubin NVL72 system with no cables, fans or hoses in the tray, cutting compute tray assembly time from hours to one minute. 

A 45-degree Celsius liquid cooling inlet temperature design enables chiller-free dry-cooler operation. For new AI factories, this higher-temperature dry cooling along with the closed-loop liquid cooling system saves millions of gallons of water per megawatt annually.

Tuesday, July 21, 8:00 a.m. PT

NVIDIA Vera Rubin Powers Europe’s Open Model Era

Vera Rubin is delivering next-generation performance to Europe’s AI infrastructure.

It’s the foundation for a newly expanded Microsoft and Mistral partnership that brings frontier AI to the region, combining open European models with cloud and customer-controlled environments so governments and regulated industries can adopt it on their own terms.

Underpinning the partnership is a new multibillion-dollar agreement focused on expanding AI infrastructure in Europe. Mistral is adding its GPU capacity, drawing on thousands of the latest NVIDIA Vera Rubin GPUs to increase AI compute availability for customers and provide a shared platform for training, inference and large-scale deployment.

NVIDIA Vera Rubin is ramping into full production. The rack-scale AI supercomputer unifies seven new chips codesigned as one system, and it will power the next generation of Mistral Compute and Microsoft’s European AI infrastructure, drawing on tens of thousands of GPUs.

Europe wants the world’s most capable AI, running under its own laws, close to home, and fully within its control. And the bar keeps rising: agentic systems can consume up to 15x more tokens than traditional AI applications, making efficient infrastructure a strategic priority. Meeting that expectation takes more than computing capacity: AI that scales efficiently while satisfying regional requirements for data control, governance, resilience and strategic autonomy.

Vera Rubin provides the computing foundation for Europe’s open-model ecosystem. Its full-stack architecture combines accelerated computing, networking and software so models and agents run efficiently from training through production. 

Open Models Built for Enterprise AI

Together, Microsoft, Mistral and NVIDIA are delivering sovereign-ready AI across public cloud, cloud-connected and fully disconnected private cloud environments — pairing the flexibility of open models with the infrastructure required to operate them at scale.

Mistral Medium 3.5 and OCR 4 are now available in Microsoft Foundry, and Mistral models are integrated into Microsoft Copilot Studio. Through Azure Local and Foundry Local, customers can use the same models, tools and operating patterns across cloud and customer-controlled environments.

AI on Europe’s Terms

Government agencies can apply AI to sensitive workflows. Healthcare and financial services organizations can deploy within regional requirements. Manufacturers can process data on site for faster decisions. Compared with NVIDIA GB200 NVL72, Vera Rubin NVL72 delivers up to 10x more tokens per megawatt and one-tenth the cost per million tokens, providing more intelligence within the same power footprint.

Sovereign AI should not force organizations to choose among innovation, economics and control. With Vera Rubin as the computing foundation — and Microsoft and Mistral delivering sovereign cloud infrastructure and open European models — Europe can pursue all three.

Read the Microsoft and Mistral press release for more details.

Tuesday, July 21, 8:00 a.m. PT

NVIDIA Vera Rubin NVL72 on CoreWeave Demonstrates 10x More Tokens Per Megawatt Than Blackwell in Benchmark

Embracing extreme codesign with NVIDIA, CoreWeave is delivering an order-of-magnitude performance leap on Vera Rubin NVL72. 

Close collaboration with partners like CoreWeave is mission-critical to bringing up a new generation of NVIDIA accelerated computing into AI factories. 

After months of co-engineering work, CoreWeave became the first AI cloud to bring up and validate Vera Rubin NVL72 — and it is now sharing the first measured performance numbers from live hardware.

CoreWeave ran a DeepSeek-R1 benchmark on Vera Rubin NVL72 and saw 10x improvement in tokens per second per megawatt compared with Grace Blackwell NVL72. 

Tokens per megawatt is the metric that determines whether AI infrastructure can profitably scale. More tokens per megawatt means more intelligence from the same power budget, or the same workload on significantly less power. AI labs and enterprises will use the Vera Rubin platform to scale its AI factories on the CoreWeave cloud.   

Beating Bottlenecks With NVIDIA Spectrum-X

DeepSeek R1’s mixture-of-experts architecture makes all-to-all GPU communication a critical requirement at scale: Each token must be routed across distributed expert sub-networks. Vera Rubin NVL72’s 260 TB/s all-to-all NVLink 6 fabric removes that constraint, enabling the rack to behave as a single unified accelerator. 

CoreWeave is among the first to deploy the NVIDIA Spectrum-X Ethernet SN6600-LD as the switching fabric for Vera Rubin NVL72. 

Built on the 102.4 Tb/s Spectrum-6 switch chip and featuring a liquid-cooled design, CoreWeave deploys dense switching racks, delivering 1.64 Pb/s per rack with 100% more capacity than previous-generation air-cooled switches. CoreWeave also provides a fully non-blocking, multi-plane, multi-rail spine and leaf fabric connecting Vera Rubin NVL72 GPUs without oversubscription.

Tuesday, July 21, 8:00 a.m. PT

NVIDIA Vera Rubin NVL72 Drives New Google Cloud A5X Instance for Ineffable Intelligence

NVIDIA Vera Rubin NVL72 is powering Google Cloud’s first A5X instance, now up and running for London startup Ineffable Intelligence.

Ineffable Intelligence develops a new generation of intelligent “superlearner” systems that continuously learn through experience to discover new breakthroughs across all fields.

Ineffable Intelligence’s agents learn directly from interaction with their environments, rather than from static datasets. Instead of using large language models, Ineffable is developing “superlearner” systems through reinforcement learning, generating experience across continuously simulated, massively parallel environments and rapidly translating that experience into policy updates and evaluation. These tightly coupled learning loops place exceptional demands on compute, memory bandwidth and interconnect, requiring infrastructure that can operate at enormous scale with extremely low latency.

“The next era of research requires the next era of hardware,” said Lasse Espeholt, cofounder of Ineffable Intelligence. “We feel privileged to work with the teams at NVIDIA and Google Cloud, who were able to grant us early access to Vera Rubin. The support across both teams has been unmatched; we were up and running almost immediately and are already testing infra for our superlearners.”

NVIDIA Vera Rubin NVL72 is designed for this kind of agentic training, delivering predictable latency, high utilization and significantly more intelligence per dollar than previous-generation systems, making it a natural platform choice for large-scale reinforcement learning.

Google Cloud A5X instances, announced at Google Cloud Next, are bare-metal instances built on NVIDIA Vera Rubin NVL72 rack-scale systems, delivering up to 10x lower inference cost per token and 10x higher token throughput per megawatt than the prior generation. 

A5X uses NVIDIA ConnectX‑9 SuperNICs combined with next-generation Google Virgo networking, enabling clusters that can scale to tens of thousands of NVIDIA Rubin GPUs within a single site and up to nearly a million GPUs across multisite configurations, giving customers a unified, AI‑optimized stack for training, tuning and serving frontier, open, agentic and physical AI models while optimizing for performance, cost and sustainability.

This infrastructure is designed to help unlock the next generation of reinforcement learning systems for breakthroughs in superlearning and superintelligence.

Tuesday, July 21, 8:00 a.m. PT

NVIDIA Vera CPU Doubles Orchestration Speed for DeepInfra AI Cloud

Benchmark results from DeepInfra show that the NVIDIA Vera CPU is more than twice as fast and can support more concurrent AI agents compared with other CPUs.

Cloud platform DeepInfra, an early access participant in the NVIDIA open AI ecosystem, independently designed and ran benchmarks using its production AI agent infrastructure. DeepInfra processes nearly 5 trillion tokens a week, with about 30% driven by agentic systems. Its cloud platform is built for high-throughput AI inference. 

The benchmarks demonstrate support for up to 1.6x more concurrent AI agents at the same quality of service and up to 2.2x faster orchestration than alternative CPUs, while improving infrastructure utilization and cost efficiency. These results show that the NVIDIA Vera CPU delivers the cost efficiency, low latency and throughput that production agentic AI demands.

As AI agents take on more complex reasoning, planning, tool use and data movement, CPU performance has become increasingly important for orchestrating work around each model call. 

Part of NVIDIA’s extreme codesign approach to AI factories, the Vera CPU is built for agentic workloads. DeepInfra’s benchmark highlights how the NVIDIA Vera CPU helps cloud providers improve infrastructure utilization, increase cost efficiency and support more concurrent AI agents at the same quality of service.

Learn more about the NVIDIA Vera Rubin platform.

Tuesday, July 24, 1:30 p.m. PT

NVIDIA Vera Rubin to Join Nebius AI Cloud Serving Europe and U.S., Providing Global Availability

NVIDIA Vera Rubin NVL72 and Spectrum-6 are arriving on Nebius Cloud in Europe.

Nebius will be an early entrant of AI clouds bringing the Vera Rubin platform to Europe and the U.S., providing global availability of the latest accelerated computing to AI natives and enterprises.

The company recently received its first Vera Rubin NVL72 system at its Finland AI Factory, the first of many systems that will be deployed globally. 

For connecting NVIDIA Vera Rubin NVL72 beyond the rack, Nebius is deploying NVIDIA’s Spectrum-6 102.4T Ethernet switch, part of NVIDIA’s latest Spectrum-X Ethernet networking platform built for massive scale AI fabrics. 

Nebius got early access through close collaboration with NVIDIA to verify the entire stack before customers run their workloads.

Learn more about the NVIDIA Vera Rubin platform.