open source - Faz Business | فاز الأعمال

NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier

Posted on September 1, 2026 by faz_business

“We’re at an inflection point in cybersecurity,” Jensen Huang told a sold-out crowd at CrowdStrike’s Fal.Con 2026 in Las Vegas Tuesday. Attacks are now automated. Defense has to be, too.

The NVIDIA founder and CEO joined CrowdStrike CEO and founder George Kurtz to announce CrowdStrike SafeMind, its agentic cybersecurity system developed by the CrowdStrike Cyber Superintelligence Lab.

“This is the beginning of a new age of cybersecurity,” Huang told the crowd of 10,000 security professionals. “On the one hand, the adversaries are going to be more armed than ever. On the other hand, all of you are going to be more armed than ever.”

SafeMind combines CrowdStrike’s purpose-built, beyond frontier-capable models and customized agentic harnesses, with defensive models built on NVIDIA Nemotron, in a continuous coevolution loop where offense and defense repeatedly challenge and improve each other.

CrowdStrike also announced CrowdStrike Falcon IQ to operationalize Project QuiltWorks through agentic workload automation and expanded its CrowdStrike Guardian AI safety solution.

“We have asymmetric advantages because we have a large community of cybersecurity experts who want to work with each other and keep the world safe,” Huang told the crowd.

CrowdStrike’s annual conference drew security leaders from financial services, healthcare, the public sector and critical infrastructure.

“The real gap that I saw was that the attackers had frontier AI, and the defenders didn’t,” Kurtz told them. “And that changes now.”

SafeMind

CrowdStrike built SafeMind’s defensive model using NVIDIA Nemotron open models, post-trained with CrowdStrike’s cyber experience and threat data. The SafeMind models are paired with proprietary cybersecurity harnesses optimized to work as an agentic stack.

The result ships natively in the CrowdStrike Falcon platform as SafeMind, CrowdStrike’s agentic cybersecurity system. SafeMind brings offensive and defensive AI together in a continuous coevolution loop, where each side adapts to and strengthens the other. This process continuously hardens the security of the customer environment until attacks are unsuccessful.

“Your decade and a half of security data that we can train on — we can take a frontier model and make it essentially a super AGI that is incredibly good at cybersecurity,” Huang told Kurtz.

“Together with NVIDIA, we built cybersecurity’s first complete agentic system for cybersecurity, including the first frontier models and harness purpose-built for defenders,” Kurtz said. “This isn’t a copilot baked into someone else’s intelligence. It’s not a chatbot with a security skin. It is a frontier-class model built and trained by CrowdStrike on our data in partnership with NVIDIA.”

NVIDIA Nemotron 3 Ultra orchestrates the defensive agent harness. A fine-tuned Nemotron 3 Super powers SafeMind’s rule-generation sub-agent.

By post-training Nemotron with CrowdStrike data, CrowdStrike internal evaluations showed that the Blue Solano model — based on Nemotron 3 Super — delivered higher accuracy rates than leading frontier models at 99% lower cost.

While SafeMind can operate as a complete system, the models can be used independently to empower defenders to stay ahead of the adversary. Security experts can also pair their own models with CrowdStrike’s custom harnesses, giving customers the flexibility to use the right models and capabilities for their environment.

“The harness is essentially the exoskeleton of the large language model,” Huang said. “The large language model is the brain. The exoskeleton turns it into an agent — and this exoskeleton doesn’t have to be the same shape and capability for every domain.”

When AI Is the Defense

AI-enabled attacks rose 89% in the past year, and the fastest eCrime breakout time has reached 27 seconds, according to CrowdStrike. Human-speed response isn’t defense. It’s documentation.

“There are many applications in the world where you must have the ability to fine-tune, to post-train — to create an AI that is super good at a particular domain,” Huang said. “Nemotron was created for precisely that. Completely free. Incredibly fast. You have the ability to have an asymmetric advantage against whatever comes your way.”

With open Nemotron as the base, CrowdStrike’s security teams post-trained on their own threat data without sending it to an outside provider, and customized the AI to their environment.

That’s not possible with a closed frontier model, and in security, the ability to inspect what’s defending matters.

Red vs. Blue

NVIDIA announced its work testing the CrowdStrike SafeMind models and harnesses in a high-fidelity cyber agent environment running as a simulation of the NVIDIA network.

The testing runs SafeMind in an offensive-defensive loop for adversarial coevolution. An offensive red-team agent finds the exploit, a blue-team defensive agent closes it and the findings become actionable detections to block attacks.

The red-agent harness runs Recon, Assault and Compromise sub-agents executing attack paths inside the cyber agent environment. The blue-agent harness monitors via Falcon sensors, generates detection candidates, validates them and promotes them.

CrowdStrike built the test environment with NVIDIA: a digital twin of NVIDIA’s own accelerated computing infrastructure, validated against NVIDIA’s real threat landscape.

“The basic framework of SafeMind — an adversarial model acting on a digital twin of the environment, with a defender model in a continuous cat-and-mouse loop, eventually learning how to secure itself — this basic framework applies to robotics, edge computing, enterprise computing and just about everything,” Huang said.

CrowdStrike also announced Falcon IQ. NVIDIA Nemotron models help to power the agentic engine at the heart of Charlotte AI AgentWorks, CrowdStrike’s no-code agent development platform where Falcon IQ runs.

Falcon IQ uses more than 50 agents working together as a unified agentic workforce to automate the most time-intensive workflows in assessment, prioritization and remediation.

Partners use Falcon IQ to deliver customized findings, recommendations and executive outputs to customers. Charlotte AI AgentWorks enables every Falcon user to build their own agentic security workforce.

The Full Stack

CrowdStrike has thousands of customer organizations generating trillions of daily security events.

With NVIDIA’s full-stack accelerated computing platform, the collaboration runs from the chips up through the models to the harnesses acting on what those models find. For Kurtz, that’s the point.

“The crowd in CrowdStrike,” Kurtz added, “is the asymmetry that puts the defenders in a unique position to defeat the adversary.”

Universitas Gadjah Mada, Indosat and NVIDIA Open Indonesia’s First University AI Center to Develop Local AI Talent

Posted on August 14, 2026 by faz_business

Indonesia is taking charge of its AI future.

This week, the Ministry of Communication and Digital Affairs (Komdigi), Indosat Ooredoo Hutchison (Indosat or IOH), NVIDIA and Universitas Gadjah Mada (UGM) launched the UGM Indosat NVIDIA AI Technology Center (NVAITC) in Yogyakarta — the country’s first university-based AI technology center. Established under Indonesia’s AI Center of Excellence initiative, UGM Indosat NVAITC brings government, industry and academia together to develop AI that addresses Indonesia’s most urgent national priorities.

“The Indonesia AI Center of Excellence reflects our long-term vision to position Indonesia as a nation that not only adopts AI but also develops and contributes AI innovations to the world,” said Meutya Hafid, Indonesia’s Minister of Communication and Digital Affairs. “Through initiatives like this, we are laying the foundations of Indonesia’s AI sovereignty and ensuring AI becomes a driver of economic growth, national competitiveness and solutions to Indonesia’s most pressing challenges.”

“UGM is committed to supporting Indonesia’s AI ambitions through education, research, and innovation that deliver real societal impact,” said Prof. dr. Ova Emilia, Ph.D. “This initiative supports national priorities in higher education, research downstreaming and talent development by accelerating the adoption of AI and preparing future-ready Indonesian talent.”

Compute That Belongs to the Country

Powered by NVIDIA’s full-stack AI platform and GPU Merdeka — Indosat’s sovereign GPU-as-a-service platform — UGM Indosat NVAITC gives UGM’s researchers and students access to enterprise-grade accelerated computing, AI software, open source, pretrained models, development frameworks and technical mentorship. It also connects Indonesian researchers to a worldwide ecosystem of expertise.

“At Indosat, we believe no Indonesian should be left behind in the AI era,” said Vikram Sinha, president director and CEO of Indosat Ooredoo Hutchison. “Through UGM Indosat NVAITC, we are bringing the best of global AI technologies and expertise to Indonesia, while expanding access for the ecosystem of researchers, students, startups, and innovators across the country. By strengthening AI readiness and empowering Indonesian talent, we aim to support the government’s vision for AI and help position Indonesia not only as a user of AI technologies, but as a nation that develops and contributes AI innovations to the world.”

The opportunity is real. Indonesia is the world’s fourth-most populous country, with researchers and developers working on problems of scale and urgency. What they’ve historically lacked is access to the compute, models and infrastructure to move from insight to impact.

“Indonesia is home to an extraordinary community of researchers, developers and innovators with the potential to shape the future of AI,” said Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA. “From healthcare and agriculture to disaster preparedness, the opportunities for AI to drive real change are immense. Through UGM Indosat NVIDIA AI Technology Center, NVIDIA is committed to equipping Indonesian talent with NVIDIA Nemotron open models and expertise to turn that potential into innovation with local and global impact.”

AI for Indonesian Challenges

Three initial projects define what this center is for, focused on healthcare, agriculture and natural disaster response.

Indonesia records over 1 million new tuberculosis (TB) cases every year. TB is curable — but it kills when it goes undetected. Detection in rural and underserved areas has depended on equipment and expertise unavailable at the community level. UGM’s Faculty of Medicine Public Health and Nursing team, led by dr. Dian Kesumapramudya Nurputra, M.Sc, Ph.D, SpA , is developing an AI-powered electronic screening technology — eNose-TB — that screens for TB by analyzing breath samples. The goal: affordable, fast, accessible screening that reaches patients in remote clinics and villages — no specialist or expensive lab required.

“As researchers, we have always believed that technology developed in Indonesia can solve Indonesian challenges,” said Dian. “Through the center, access to world-class AI infrastructure and expertise will help us accelerate our research and bring us closer to our dream of developing affordable and accessible healthcare technologies that can improve lives across Indonesia.”

Agriculture employs nearly 30% of Indonesia’s workforce. SmartAgri uses multimodal AI — combining satellite imagery, sensor data and local agricultural knowledge — alongside edge computing to deliver precision farming for Indonesian terrain, crops and smallholders. These AI-powered recommendations help farmers know exactly when and how to irrigate, delivering impact that compounds over time.

Indonesia sits on the Pacific Ring of Fire, facing more natural disaster risk than almost anywhere on earth. Tech4Disaster is building a geospatial AI platform using NVIDIA accelerated computing to process satellite and sensor data at speed — giving communities and emergency responders earlier warning, better situational awareness and faster coordination tools when disaster strikes.

Learn more about the UGM Indosat NVIDIA AI Technology Center.

Tech Visionary Says the Big AI Labs Don’t Get What People Want

Posted on August 14, 2026 by faz_business

Tim O’Reilly’s yardstick for measuring the worth of a company, person, or society has long been create more value than you capture. It’s no surprise that O’Reilly—publisher, internet pioneer, VC, conference organizer, and dispenser of tech wisdom—is applying that metric to the way people design and use AI. Specifically, he’s pushing for a future where open-source AI is an elixir for the masses. He worries that, like Microsoft in the 1990s, today’s hyperscalers are trying to lock users into their products. So he is promoting efforts to open-source AI technology—not only making critical technical details such as neural-net weights accessible, but unlocking the whole stack of an AI system, giving control to designers and users.

O’Reilly sees AI as a new creative medium, which he uses extensively—and even has a blog about his chats with it. During our conversation, we discovered that we disagree about AI’s role in producing original content. Guess who took which side.

STEVEN LEVY: You are all in on open-source AI. Make your case.

TIM O’REILLY: First, let’s make sure we’re talking about the same thing. When most people talk about open-source AI, they’re really just talking about open-weight models. It’s much bigger than that. In the ’90s, when everybody else was focused on open-source licenses, I was like, “No, no, it’s about the architecture of the system. Does it enable participation?”

Why is that needed?

The big labs are reading the future wrong. They have told themselves a narrative where having the biggest, best model is the key to the future. Big models like Claude are optimizing for particular use cases, but they aren’t necessarily the use cases that people want. I want to embed my own special sauce. The most important thing is having a clean separation between the model, the harness, and the application. And right now we’re not getting that. They built an architecture of control rather than an architecture of freedom and participation, so they have the ability to track you.

Isn’t it against the interests of big companies to give up control?

Oh, it’s totally against their interests. But that doesn’t mean that they’re making the right strategic decision. For a long time, the latest and greatest models were really better for everything. Now they’re better for some things and worse for others. People are talking a lot about how Fable and Sol are worse writers than the lower-level models. [Note: Anthropic and OpenAI would disagree.] The breakthroughs that we’re getting in so-called frontier AI are actually pushing us further away from what ordinary people are going to need. We could win frontier AI here in the US, and China will kick our ass because they have lower-level models diffused widely through society. The goal is to give people the ability to innovate freely, to paint outside the lines.

People worry that open-source software can be a security risk, since bad actors will be able to jump the frontier model’s guardrails.

All of the cybersecurity incidents we’ve seen are from the frontier models. So risks like cybersecurity and the ability to develop pathogens are actually an argument for slowing down the frontier models more than an argument for restricting open-weight models.

So you feel that with open source the AI powers will no longer be dominant?

I don’t predict the future. but I will say the world is proceeding the way that I hoped it would. What if the big frontier models end up like mainframes or supercomputers—aimed at really hard problems, which are not actually the thing that gets diffused throughout society? People are building things like Pi, an open-source [agentic] harness. One of the things we’re working on at my nonprofit, the AI Disclosures Project, is the idea of an open-memory consortium. Mark Zuckerberg’s thesis is, he’s going to lock you in because Meta will give you the AI that knows you best. The open-source vision needs to say no to this. Open source can give you the ability to switch models, switch providers, and maintain all the context that it needs.

NVIDIA Open Sources First GPU-Accelerated Medical Physics Simulation Framework

Posted on July 23, 2026 by faz_business

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.

NVIDIA Vera Rubin Maximizes Intelligence per Dollar for Post-Training

Posted on July 19, 2026 by faz_business

Think of a professional athlete. What separates elite performers is what happens between games: continuous refinement, adjusting to new opponents and sharpening skills based on what the last game exposed.

Agentic AI works the same way. A model is no longer asked for an answer. It’s given a goal and has to keep adapting as environments shift, edge cases emerge and tools change. Unlike a generative model responding to a prompt, an agentic model must plan, use different tools and recover from problems it encounters mid-run.

That’s why post-training, the phase that refines a model after initial training on raw data, is no longer a one-time finishing step. It’s continuous, because the environment that agentic models operate in shifts fast. The tools an agent uses can change week to week. Edge cases surface in production that no test set anticipated. Each deployment brings its own codebase, policies and environment.

Post-training runs loop back from production as new problems surface. The compute footprint grows not because any single run is larger, but because the runs never stop. Agentic AI introduces a new compute pattern for post-training, making it the central workload of the agentic era and the primary driver of intelligence per dollar.

The goal of post-training is to maximize intelligence per dollar by maximizing the yield of every forward and backward pass in the continuous learning cycle. The forward pass — inference — is measured in cost per token. That means that every improvement to cost per token flows directly into intelligence per dollar.

Agentic Post-Training Demystified

Post-training is where intelligence is built. In pretraining, the model learns to predict the next token, which gives it fluency but not intelligence. Post-training is where it learns to write code, plan a multistep task, use a search tool and recover when something goes wrong. Inference is what comes after: the model working on the job, priced in cost per token.

Because there’s no answer key to memorize, only a reward, the model learns by reinforcement learning (RL) techniques. When given a task, it writes out an attempt — the forward pass — the same work it does on the job. The attempt is scored, and the lesson updates the model’s weights — the backward pass. Across millions of attempts, intelligence grows.

Each step is compute intensive, and running this loop at scale is an orchestration problem: thousands of environments generating rollouts in parallel, rewards being verified and updated weights flowing back into training with accelerators fully utilized. NVIDIA NeMo open libraries, such as NeMo Gym for training environments and NeMo RL for distributed post-training, turn post-training from bespoke research code into repeatable infrastructure.

Why Intelligence per Dollar Extends Cost per Token

If inference is the revenue engine, post-training is the multiplier: the more capable the model, the higher the value of every token served.

Cost per token is the key metric for the inference factory: the all-in cost of delivering 1 million tokens. Intelligence per dollar sits one layer up, answering a different question: what does it cost to build a model worth serving, and keep it worth serving as its environment changes?

The two are nested, not competing. AI infrastructure that lowers cost per token also lowers the cost of every point of intelligence built into the model. And every point of intelligence built in raises the value of every token the inference factory serves.

In other words, cost per token measures operating yield; intelligence per dollar measures whether the investment in model intelligence is paying off.

Illustrative 20 billion rollout tokens, based on prior-generation Nemotron 3 Super’s ~1.2 million rollouts at ~10,000 tokens each, scaled up for the larger Ultra model. Intelligence per dollar between platforms is independent of this assumption; the absolute values scale with the token count.

NVIDIA BioNeMo Agent Toolkit Brings Accelerated AI to Life Sciences Researchers in Claude Science

Posted on July 1, 2026 by faz_business

Life sciences has entered an era of computational scale, and for more than a decade, NVIDIA has built the full GPU-accelerated computing stack — spanning hardware, frameworks, libraries, models, microservices and domain-specific tools — to help researchers run more sophisticated workflows and iterate faster.

This week, Anthropic announced Claude Science, an AI workbench for science research that lets scientists converse with agents in natural language to run their work end to end.

Claude Science integrates with NVIDIA BioNeMo Agent Toolkit as a resource that scientists can access within their workflow. The toolkit packages NVIDIA-accelerated capabilities as callable skills, enabling Claude Science to select the appropriate tool, prepare valid inputs and execute the workflow — all while connecting to NVIDIA compute resources deployed anywhere. This brings NVIDIA’s accelerated models, libraries and NVIDIA NIM microservices directly into the same environment where the rest of the research happens.

The world’s largest pharmaceutical companies use NVIDIA technologies to advance AI-enabled research across drug discovery, genomics, medical imaging, molecular design and protein engineering. Today, 18 of the top 20 pharmaceutical companies use NVIDIA BioNeMo, underscoring the breadth of its role across the ecosystem.

Advancing the Agentic Era of Scientific Discovery

Claude Science lets scientists use natural language to move their research from intent into action, without manually configuring models, endpoints, or software environments. NVIDIA BioNeMo Agent Toolkit extends that with access to accelerated workflows and models like Evo 2, Boltz-2 and OpenFold3, so the analyses that benefit from acceleration run faster.

A scientist begins by describing a research task, such as analyzing a genomic sequence, predicting a protein structure or designing a potential binder, in natural language. Claude Science interprets the request and orchestrates the work through preconfigured domain-specialized agents that know established workflows across genomics, proteomics, single-cell analysis, cheminformatics and clinical research.

BioNeMo Agent Toolkit gives these agents the context needed to connect each step with an appropriate NVIDIA scientific capability. Each skill includes information about its purpose and required inputs, helping agents prepare and execute the workflow and return outputs for review.

The result is an iterative loop between scientific reasoning and accelerated computational work. Scientists can inspect outputs, refine their questions and determine the next step while staying focused on the science.

One powerful example is generating better inhibitors of common cancer targets. In this workflow, a scientist starts with a known cancer-causing antigen mutation and asks Claude to design numerous potential inhibitors. Claude Science integrated with BioNeMo Agent Toolkit and NVIDIA NIM microservices accelerates high-throughput inhibitor prediction, optimization and validation.

A Scientific Foundation Built for Agents

AI agents reason, plan and use tools to complete tasks. In life sciences, those tools are often specialized computational workflows.

An autonomous AI scientist agent doesn’t reason in isolation. It may need to fingerprint a library of compounds, cluster promising hits, generate conformers for top candidates, analyze genomic context and compare perturbation responses before recommending the next experiment.

Each step relies on a scientific tool, and the agent can only work as fast as those tools run.

NVIDIA BioNeMo Agent Toolkit gives scientific agents the accelerated tools they need to operate at the speed of science. It includes:

NVIDIA Parabricks accelerates genomic analysis from hours to minutes, so an agent can integrate genomic context into a decision in near real time.
RAPIDS-singlecell, developed by scverse, compresses a 1.3-million-cell preprocessing and clustering workflow from 52 minutes to 25 seconds, so single cell analysis becomes part of the reasoning loop rather than an offline batch of jobs.
nvMolKit accelerates cheminformatics operations like similarity search and conformer generation by up to 3,000x, so an agent iterating across a massive chemical space gets results at the speed of thought.
NVIDIA BioNeMo open models deliver core biomolecular capabilities accelerated by NVIDIA libraries, so an agent has a purpose-built scientific model for each step of a workflow.
BioNeMo NIM microservices package those models as enterprise-ready inference endpoints — containerized microservices with the full accelerated software stack pre-integrated and tuned for high-performance inference — so an agent can call a single stable application programming interface for production deployment.

NVIDIA BioNeMo Agent Toolkit is open and harness-agnostic, allowing the same scientific skills to work across agent frameworks and research platforms. The toolkit and its skills are available now through NVIDIA developer resources and GitHub.

Scientists can access BioNeMo-powered workflows through Anthropic’s Claude Science, which is entering public beta today. As part of the public beta, Anthropic is inviting researchers to provide feedback on additional domain specialists and integrations they need.

How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies

Posted on June 9, 2026 by faz_business

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

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

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

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

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

Commitment to Compute

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

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

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

From Fund to Frontier

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

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

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

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

Cosine Builds Sovereign Coding Platform

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

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

Cursive Trains Self-Improving AI Systems

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

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

Doubleword Optimizes Inference to Deliver Abundant Intelligence Tokens

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

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

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

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

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

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

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

Video courtesy of Nebius and Prima Mente.

AI Talent, Policy and Production

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

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

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

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

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

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

And enterprise AI is moving from pilot to production:

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

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

Join NVIDIA at London Tech Week.

NVIDIA and Google Cloud Empower the Next Wave of AI Builders

Posted on May 21, 2026 by faz_business

At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning paths, hands-on labs and events that help them build using the full-stack NVIDIA AI platform on Google Cloud.

Launched at Google I/O last year, the community brings together developers, data scientists and machine learning engineers who want to sharpen their AI skills on the latest NVIDIA and Google Cloud technologies.

New additions for the community are rolling out this year, including a learning path for using the JAX library on NVIDIA GPUs, a new NVIDIA Dynamo codelab focused on inference optimizations, as well as monthly developer livestreams.

Over the last year, the community has become a go‑to hub for AI builders using NVIDIA‑accelerated tools for data science and machine learning. The result has been production‑ready retrieval-augmented generation applications on Google Kubernetes Engine (GKE) and instrumenting observability for agent workloads.

These AI builders are also experimenting with new large language model research and prototyping hybrid on‑premises and cloud inference for real‑world use cases like sports analytics and enterprise data pipelines.

Building With Google DeepMind’s Gemma, NVIDIA Nemotron and Open Frameworks

NVIDIA and Google Cloud are equipping developers with learning resources and hands-on labs that combine NVIDIA libraries, open models and tools with Google Cloud’s AI platform — so they can build optimized, production‑ready AI applications faster.

For example, developers can accelerate data science and analytics with the NVIDIA cuDF library in Google Colab Enterprise or Dataproc, or deploy multi-agent applications by combining Google DeepMind’s Gemma 4 models, NVIDIA Nemotron open models and Google Agent Development Kit with Google Cloud G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs in Google Cloud Run or with spot instances.

NVIDIA and Google Cloud work closely across open frameworks like JAX so developers can build, scale and productize JAX workloads on NVIDIA AI infrastructure on Google Cloud — from single‑GPU experiments to multi‑rack deployments — while getting strong performance and a consistent experience.

This work extends to Google Cloud AI Hypercomputer, where the MaxText framework uses these JAX optimizations to train large models efficiently on NVIDIA GPUs.

Building on the same foundation, NVIDIA Dynamo on GKE helps developers optimize large-scale inference — including mixture-of-experts models — so they can serve AI applications more efficiently with NVIDIA accelerated infrastructure on Google Cloud.

To help developers get hands-on with these capabilities, a new learning path on running and scaling JAX on NVIDIA GPUs and a new NVIDIA Dynamo on GKE inference codelab will become available next month for members in the Google Cloud and NVIDIA developer community.

Advancing Responsible AI With Google DeepMind’s SynthID and NVIDIA Cosmos

AI agents are increasingly built from a system of AI models — combining proprietary and open source models that reason, plan and act on users’ behalf.

Amid this shift, trust and transparency are foundational, so developers and organizations can understand how these systems work and what they generate.

NVIDIA was the first industry partner to collaborate with Google DeepMind on SynthID, an AI watermarking technology that embeds robust digital watermarks directly into AI‑generated content, which helps preserve the integrity of outputs from NVIDIA Cosmos world foundation models available on build.nvidia.com.

Cosmos models provide rich 3D perception and simulation capabilities for robots, autonomous machines and other physical AI systems, while SynthID brings content transparency to the imagery and video they rely on.

Together, they help preserve the integrity of AI‑generated content so developers can build and deploy agentic applications more responsibly across cloud, edge and real‑world environments.

Building on a Full-Stack NVIDIA and Google Cloud Platform

This year, Google I/O is putting the spotlight on new agentic experiences and tools for developers — and NVIDIA and Google Cloud are focused on ensuring builders have the infrastructure, software and learning resources they need to make the most of them.

For developers in the community building on NVIDIA and Google Cloud, the skills and tools they learn can scale, effortlessly taking projects from prototype to enterprise‑grade workloads.

At Google Cloud Next, Google Cloud and NVIDIA expanded their full‑stack platform to help developers train, deploy and operationalize agents on Google Cloud. This collaboration includes work on NVIDIA Vera Rubin-powered A5X instances, Google DeepMind Gemini models and more, and is being harnessed by leading AI labs and enterprises including OpenAI, Thinking Machine Labs, Schrodinger, Salesforce, Snap and Crowdstrike. Learn more in this blog.

Join the NVIDIA and Google Cloud developer community to connect with other builders and stay up to date on new tools, developer events and programs.

The EU Is Going Through a Trump-Fueled Breakup With Big Tech

Posted on May 21, 2026 by faz_business

As tensions between President Donald Trump and Europe continue to simmer, the continent is accelerating its moves to reduce its addiction to US technology. Cities and governments are ditching Microsoft Office for open-source alternatives, shifting to European cloud hosting for local AI, and moving defense data to systems without American involvement. Nowhere has this been more clear than in France.

Over the last few months, the French government has sped up its efforts to develop and deploy its own technology for government officials. The country has, arguably, emerged at the head of Europe’s growing digital sovereignty push, which aims to cut some reliance on US-based technology over concerns around data security, the Trump administration’s unpredictability, and changing prices. French budget minister David Amiel recently called for the state to “break free” from American systems and use those it can control.

“We are not just explaining what we want to do,” Stéphanie Schaer, the head of DINUM, France’s digital transformation ministry, tells WIRED over a call on the nation’s video-calling platform Visio. “We already did it in a few matters.” So far, more than 40,000 French government staff have started using the home-grown video platform, while the rest will move away from Zoom, Microsoft Teams, and others by 2027. “We are confident enough to use it every day and we are not dependent on just one actor that will tell us you have to use my video conference,” Schaer says.

Across France’s central government agencies and vast civil service, officials plan to shift to as many French, European, and open source technology alternatives as possible in the coming years. Schaer says it is important for the French government to be in control of the technology that it is using, with data being stored locally in the country, not abroad.

As part of this, DINUM has been developing a set of productivity tools, collectively called “LaSuite,” since at least 2023. As well as Visio, it includes instant messaging app Tchap, Messagerie instead of Gmail or Outlook, Fichiers for documents and file sharing, plus text editing software Docs, and Grist for spreadsheets. Some of the software is still in beta and has not been fully rolled out to French officials yet. However, Tchap already has 420,000 active users, Schaer says, with 20,000 civil servants adopting it each month.

“We are based on open source software. So we don’t develop all the code,” Schaer says. There are public plans for new features, although code is published on Microsoft-owned Github. All data handled by the alternatives has to be processed in France and stored with providers who have approval from the country’s cybersecurity agency ANSSI. Earlier this month, the Dutch government moved its open-source code off of GitHub and onto a Forgejo instance hosted on government-owned servers.

While open source is key, the French government is also working with other countries and private firms on the development of its tools. “We can reuse what has been developed by the community and we contribute to this community,” Schaer says. For instance, Visio, which can host calls of up to 150 people and has AI transcription of calls, is built on technology from French firms Outscale and Pyannote.

While Schaer’s department is aiming to lead by example, all of France’s central government agencies have to come up with plans to move away from US tech—across office software, antivirus, AI, databases, and more—by this fall. On April 23, French officials also announced the country will move its health data platform away from Microsoft to local cloud provider Scaleway, after a years-long decision process.

NVIDIA and ServiceNow Partner on New Autonomous AI Agents for Enterprises

Posted on May 6, 2026 by faz_business

Enterprise AI has learned to generate. It has learned to reason. Now companies are asking the next question: How should AI act?

Early agent systems have shown what’s possible, moving beyond simple prompts to take on more complex tasks. The next step is bringing those capabilities into enterprise environments — where agents must operate with context, control and consistency across real workflows.

At ServiceNow Knowledge 2026, NVIDIA founder and CEO Jensen Huang joined ServiceNow chairman and CEO Bill McDermott during the opening keynote to discuss the next phase of enterprise AI.

The companies are expanding their collaboration across the full stack, delivering specialized autonomous AI agents that are safe and easy to adopt — powered by NVIDIA accelerated computing, open models, domain-specific skills and secure agent execution software, and bringing together enterprise workflow context from ServiceNow Action Fabric and governance from ServiceNow AI Control Tower.

ServiceNow is introducing Project Arc, a long-running, self-evolving autonomous desktop agent designed for knowledge workers, including developers, IT teams and administrators.

Unlike standalone AI agents, Project Arc connects natively to the ServiceNow AI Platform through ServiceNow Action Fabric to bring governance, auditability and workflow intelligence to every action the autonomous desktop agent takes. It can access the local file systems, terminals and applications installed on a machine to complete complex, multistep tasks that traditional automation can’t handle, but with the controls enterprises actually need to deploy AI at scale.

The work is designed based on three requirements every company will need for long-running, autonomous agents: open models and domain-specific skills that can be customized and security that helps agents act without exposing sensitive data or systems — all running on AI factories that deliver efficient tokenomics.

Bringing this level of autonomy to enterprises requires control from the start.

Project Arc uses NVIDIA OpenShell, an open source secure runtime for developing and deploying autonomous agents in sandboxed, policy-governed environments. ServiceNow is building on and contributing to OpenShell to advance a common foundation for secure, enterprise-grade agent execution. With OpenShell, enterprises can define what an agent can see, which tools it can use and how each action is contained.

“Project Arc represents the next step in our ongoing collaboration with NVIDIA, bringing autonomous execution to the desktop,” said Jon Sigler, executive vice president and general manager of AI Platform at ServiceNow. “By combining OpenShell’s runtime layer with ServiceNow AI Control Tower, and powered by ServiceNow Action Fabric, we’re delivering the governance and security that enterprise AI requires.”

Open Models and Agent Skills Scale Enterprise AI

To be effective, enterprise AI systems must be adaptable. NVIDIA and ServiceNow are building on an open ecosystem that allows organizations to tailor models and applications to their specific domains and data.

NVIDIA agent skills enable specialized agents, such as ServiceNow AI Specialists, to deliver targeted capabilities across enterprise workflows. For example, the NVIDIA AI-Q Blueprint for building specialized deep research agents empowers ServiceNow AI Specialists to gather context, synthesize information and support more complex decision-making across business functions.

In addition, the NVIDIA Agent Toolkit, including NVIDIA Nemotron open models, provide flexible building blocks and specialized skills for developing customized AI applications. To support real-world performance that these systems can perform reliably, the companies are also advancing NOWAI-Bench, an open benchmarking suite for enterprise AI agents, integrated with the NVIDIA NeMo Gym library. NOWAI-Bench includes EnterpriseOps-Gym, one of the industry’s most challenging enterprise agent benchmarks, where Nemotron 3 Super currently ranks No. 1 among open source models.

Unlike general benchmarks, these evaluations focus on multistep workflows — where enterprise AI systems often encounter real challenges — helping teams build agents that perform reliably in production environments.

Efficient AI Factories

As AI agents become long running and always on, scaling them across millions of workflows requires not just capability but efficiency — making token economics central to enterprise AI.

NVIDIA AI factories are built to deliver the lowest-cost, most-efficient tokenomics for production AI. The NVIDIA Blackwell platform delivers more than 50x greater token output per watt than NVIDIA Hopper, resulting in nearly 35x lower cost per million tokens. For enterprises running agents across millions of workflows, that efficiency can determine how quickly AI moves from pilots to broad production use.

ServiceNow AI Control Tower integrates with the NVIDIA Enterprise AI Factory validated design, extending governance and observability to large-scale AI workloads. With added agent observability capabilities, organizations can monitor behavior in real time and manage AI systems across their full lifecycle — from deployment to optimization.

AI is becoming a new way that work gets done. What’s changing now is that the core pieces required to deploy it at scale — capable agents, built-in guardrails and proven performance — are all coming together.

The companies that move fastest will be the ones that give agents the infrastructure to act, the context to make decisions and the governance to keep every action accountable — and NVIDIA and ServiceNow are making this a reality for the world’s enterprises.

Learn more about NVIDIA OpenShell and the NVIDIA AI-Q Blueprint.

Tag: open source