Editor’s note: This post is part of the Nemotron Labs blog series, which explores how the latest open models, datasets and training techniques help businesses build specialized AI systems and applications on NVIDIA platforms. Each post highlights practical ways to use an open stack to deliver real value in production — from transparent research copilots to scalable AI agents.

By early 2026, the open source project OpenClaw had become a phenomenon. In January, its GitHub star count crossed 100,000 as developer interest surged. Community dashboards and traffic analytics showed more than 2 million visitors in a single week. By March, OpenClaw topped 250,000 stars — overtaking React to become the most-starred software project on GitHub in just 60 days.

Created by Peter Steinberger, OpenClaw is a self-hosted, persistent AI assistant designed to run locally or on private servers. The project drew attention for its accessibility and unbounded autonomy: Users could deploy an AI model locally without depending on cloud infrastructure or external application programming interfaces (APIs).

Most AI agents today are triggered by a prompt, complete a defined task and then stop running. A long-running autonomous agent, or “claw,” works differently. These agents run persistently in the background, completing tasks on their own and surfacing only what requires a human decision. They operate on a heartbeat: At regular intervals, they check their task list, evaluate what needs action, and either act or wait for the next cycle.

OpenClaw’s rapid adoption also sparked debate. Security researchers raised concerns about how self-hosted AI tools manage sensitive data, authentication and model updates. Others questioned whether local deployments could expose users to new risks — from unpatched server instances to malicious contributions in community forks. As contributors and maintainers worked to address these issues, OpenClaw’s rise prompted a broader conversation across the AI ecosystem about the trade-offs between openness, privacy and safety.

To help enhance the security and robustness of the OpenClaw project, NVIDIA is collaborating with Steinberger and the OpenClaw developer community to address potential vulnerabilities, as detailed in a recent blog post by OpenClaw.

NVIDIA contributes code and guidance focused on improving model isolation, better managing local data access and strengthening the processes for verifying community code contributions. The goal is to support the project’s momentum by contributing its security and systems expertise in an open, transparent way that strengthens the community’s work while preserving OpenClaw’s independent governance.

 To help make long-running agents safer for enterprises, NVIDIA also introduced NVIDIA NemoClaw, a reference implementation that uses a single command to install OpenClaw, the NVIDIA OpenShell secure runtime and NVIDIA Nemotron open models with hardened defaults for networking, data access and security. NemoClaw serves as a blueprint for organizations to deploy claws more securely.

Inference Demand Multiplies With Each AI Wave

AI has moved through four phases, and the time between each is shortening. Predictive AI took years to become mainstream. Generative AI moved faster. Reasoning AI arrived faster still. Autonomous AI — the wave OpenClaw represents — is setting an even faster pace.

What compounds with each wave is inference demand. Generative AI increased token usage over predictive AI. Reasoning AI increased it another 100x. Autonomous agents, which run continuously and act across long time horizons, drive inference demand up by another 1,000x over reasoning AI. Each wave multiplies the compute required.

This increase in token usage is enabling organizations to speed their productivity by orders of magnitude. For example, long-running agents can help researchers work through a problem overnight, iterate on a design across thousands of configurations, or monitor systems and surface only the anomalies that require human judgment — freeing up researchers’ work days for higher-value tasks.

Choosing the Tool: When to Deploy a ‘Claw’

While generative AI has become a staple for on-demand tasks, there are specific scenarios where the persistent “heartbeat” of a claw offers distinct advantages. Determining when to move from a standard prompt-based AI to a long-running agent often comes down to the nature of the workflow:

• From “On-Demand” to “Always-On”: While standard models are excellent for immediate, human-triggered queries, claws are often better suited for tasks that require continuous background monitoring or periodic system checks without a manual start.
• Managing High-Iteration Loops: For complex problems, like testing thousands of chemical combinations or simulating infrastructure stress tests, a claw can manage the sheer volume of iterations that might otherwise be bottlenecked by human intervention.
• Shifting from Suggestions to Actions: In many workflows, standard AI is used to provide information or drafts. A claw is often considered when the goal is for the AI to move into the execution phase — interacting with APIs, updating databases or managing files across a long time horizon.
• Resource Optimization: For massive, token-heavy reasoning tasks, deploying a local claw on dedicated hardware like an NVIDIA DGX Spark personal AI supercomputer allows for more predictable costs and data privacy compared with high-frequency cloud API calls.

How Are Organizations Using Long-Running Autonomous Agents?

The practical applications of long-running autonomous agents span every function and sector.

In financial services, agents continuously monitor trading systems and regulatory feeds, flagging material events before the morning review. In drug discovery, agents sweep new scientific literature, extracting relevant findings and updating internal databases in real time without researcher intervention — a process that previously took weeks.

In engineering and manufacturing, agents speed problem analysis by testing thousands of parameter combinations, ranking results and flagging the configurations worth examining — and all this can happen overnight. 

In IT operations, agents diagnose infrastructure incidents, apply known remediations and escalate only the novel problems — compressing average time to resolution from hours to minutes. At ServiceNow, AI specialists leveraging Apriel and NVIDIA Nemotron models can resolve 90% of tickets autonomously. 

How Can Companies Deploy Autonomous Agents Responsibly? 

Autonomous agents are hands-on. They can send communications, write files, call APIs and update live systems. When an agent produces a wrong action, there are real consequences. Getting the accountability framework right from the start is essential, and organizations deploying autonomous agents in production must treat governance as a first-order requirement.

Organizations need to see what their agents are doing, inspect their reasoning at each step, audit their actions and intervene when needed. 

Organizations deploying autonomous agents responsibly are focused on three priorities: 

• An open, auditable framework: NemoClaw is built on OpenClaw’s MIT licensed codebase, which means organizations own the full agent harness. They can read, fork and modify every layer of how their agents are built and deployed. That transparency enables teams to understand and control the system at the code level. Running open source models like NVIDIA Nemotron locally keeps sensitive workloads, including patient records, legal documents, financial transactions and proprietary research, within the organization’s own environment, ensuring that trace data stays under organizational control.
• Securing the runtime environment: NemoClaw runs agents inside OpenShell, a sandboxed environment that defines precisely what the agent can and cannot do, enforcing clear permission boundaries from the start. 
• Local compute: NVIDIA DGX Spark supercomputers deliver data-center-class GPU performance in a deskside form factor built for continuous local inference that’s always on, with local model hosting and data that stays within the organization’s environment. NVIDIA DGX Station systems scale that capability for teams running multiple agents simultaneously across complex, sustained workloads. 

The organizations defining what autonomous agents do in practice are accumulating something valuable: months of live operational learning, governance frameworks developed through real workloads and agents that have absorbed the institutional context that makes them genuinely useful. This foundation will only deepen over time.

Get Started With NVIDIA NemoClaw

Access a step-by-step tutorial on how to build a more secure AI agent with NemoClaw on NVIDIA DGX Spark. Explore how NemoClaw can deploy more secure, always-on AI assistants with a single command.​ 

Experiment with NemoClaw, available on GitHub, and join the community of developers on Discord building with NemoClaw using NVIDIA Nemotron 3 Super and Telegram on DGX Spark.

Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA AI news, joining the community and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.  

Explore self-paced video tutorials and livestreams.

[Editor’s note] The blog has been updated to note that GeForce RTX 5080-power expansion also extends to the Install-to-Play library.

It’s gonna be May — and the cloud’s in full festival mode. 

16 games are joining GeForce NOW this month, including new AAA titles arriving on launch day from Steam, Xbox, PC Game Pass and GOG, so members can stream their PC libraries instantly across almost any device. Headline launches include the highly anticipated Forza Horizon 6 and 007 First Light, both ready to hit the cloud on day one.

Meanwhile, the GeForce NOW Ultimate membership dials things up with expanded RTX 5080‑class performance across the cloud gaming library, bringing higher frame rates, richer visuals and more responsive gameplay to even more of the games members already love. Ultimate members now get priority access to RTX 5080‑class rigs, making it easier than ever to tap into next‑generation PC power from almost any device.

GeForce NOW is also celebrating 30 years of Firaxis in style, with more of the studio’s classics coming as Install‑to‑Play titles for instant access — just in time for a Steam celebration sale on select Firaxis games supported in the cloud.

And this week kicks things off with six new games dropping into the cloud, setting the stage for a May packed with new adventures.

The Cloud Keeps Getting Bigger

AAA titles are landing throughout the month on their launch dates. Members can pick them up from their favorite PC stores — Steam, Xbox, PC Game Pass and GOG — and stream their PC gaming libraries instantly on GeForce NOW without having to repurchase titles to play across devices.

The highly anticipated titles hitting the cloud this month include: 

Forza Horizon 6 from Playground Games: The Horizon festival is back and bigger than ever. This is Horizon Japan — stretching from the iconic downtown streets of Tokyo City all the way to the snowy Japanese Alps — introducing the most dense and vertical map yet. A massive new open world, a stacked car roster and that legendary “one more race” energy are all heading to GeForce NOW on the title’s launch date. Members can pick it up on PC via Steam and Xbox, including Game Pass, then hit full throttle from the cloud on almost any device.

007 First Light from IO Interactive: The game brings a modern James Bond origin story to the cloud when it launches Wednesday, May 27, for PC, inviting members to step into the shadows as Bond begins his journey. Experience a mix of stealth and action built around a “breathing” gameplay loop with IOI’s cinematic, set piece-driven storytelling. Approach encounters through stealth, direct action or creative improvisation, with multiple viable paths to each objective that let players define Bond’s style. It’s ready on day one for players grabbing it on Steam, Xbox on PC or GOG.

More Power

GeForce NOW Ultimate is expanding what premium cloud gaming looks like. Starting today, Ultimate members can stream even more of their games on RTX 5080 virtual gaming rigs — bringing the power of the NVIDIA Blackwell RTX architecture to a wide range of titles.

This update significantly broadens access to 5080 performance beyond the list of GeForce RTX 5080-optimized titles. Now, across nearly the entire GeForce NOW library, members can experience higher frame rates, richer visuals, and more responsive gameplay by default at up to 5K 120 frames per second or 360 fps at 1080p.

With RTX 5080 in the cloud, Ultimate members unlock the same cutting-edge features available to GeForce RTX 50 Series GPU owners. That includes NVIDIA DLSS 4 technology for sharper image quality and higher performance, NVIDIA Reflex for reduced system latency and faster input response, and advanced ray tracing for more lifelike lighting and reflections. 

With RTX 5080 powering the default Ultimate experience, GeForce NOW delivers next-generation performance to more games, extending the value of a GeForce NOW membership. See more details in the knowledgebase article.

30 Years of ‘One More Turn’

GeForce NOW is celebrating three decades of Firaxis — the legendary studio behind Civilization and XCOM — with a growing lineup of some of the best strategy games on the cloud.

Throughout May, GeForce NOW is rolling out a collection of classic Firaxis games, available as Install-to-Play titles for instant access. Expanding beyond Civilization V and Civilization VI and the series’ signature “one more turn” gameplay,  GeForce NOW will showcase the studio’s broader legacy. 

Take to the stars in Sid Meier’s Civilization: Beyond Earth, lay tracks in Sid Meier’s Railroads!, defend the globe in XCOM 2, and command the skies in Ace Patrol and Ace Patrol: Pacific Skies.

Add to that the anniversary celebration on Steam with discounts of up to 90% on games in the Firaxis catalog for a limited time. It’s the perfect time for members to build their Firaxis collection in the cloud.

May-jor New Games

Heroes of Might and Magic: Olden Era is the official prequel that returns to the roots of the genre-defining, critically acclaimed turn-based strategy series. Raise grand armies and wield devastating spells to overcome foes in solo and multiplayer, set against a grim, war-torn world of uneasy alliances and hard choices. Now available to stream on GeForce NOW, every decision shapes a path defined by power, strategy and hard-earned victories

Anno 117: PAX Romana is turning up the heat with its first downloadable content (DLC), Prophecies of Ash, dropping a massive new volcanic island into the heart of the Roman empire. Build a thriving city on Cinis, juggle new production chains like obsidian crafting and new items, and try to stay on the good side of Vulcan — a new deity to worship —  while a very active volcano looks in the background. Additionally, the DLC introduces a new resource a new trader, Ceacilia, and several new specialists. Available to stream on GeForce NOW, the DLC is ready to play without waiting on big downloads or patches — just jump in, claim the new land and see whether the empire can handle a little fire and brimstone.

Check out what else is available this week:

• Global Rescue (New release on Steam, April 27)
• s&box (New release on Steam, April 28)
• Far Far West (New release on Steam, April 28)
• INDUSTRIA 2 (New release on Steam, April 29)
• Heroes of Might and Magic: Olden Era (New release on Steam and Xbox, available on Game Pass, April 30)
• Bus Bound (New release on Steam, April 30)

And look forward to the games coming throughout the month:

• Conan Exiles (New release on Steam and Epic Games Store, May 5)
• Dead as Disco (New release on Steam, May 5)
• HUNTDOWN: OVERTIME (New release on Steam, May 7)
• Outbound (New release on Steam, May 14)
• Starminer (New release on Steam, May 27)
• Deep Rock Galactic: Rogue Core (New release on Steam, May 18)
• Forza Horizon 6 (New release on Steam and Xbox, available on Game Pass, May 19)
• Luna Abyss (New release on Steam, May 21)
• ZERO PARADES (New release on Steam, May 21)
• 007 First Light (New release on Steam, Epic Games Store and Xbox, available on the Microsoft store, May 27)
• Hotel Architect (Steam)
• Kiln (Steam and Xbox, available on Game Pass)
• Nuclear Option (Steam)
• Sintopia (Steam)
• Sudden Strike 5 (Steam)
• Super Battle Golf (Steam)

Adding to April

In addition to the 10 games announced last month, 11 more joined the GeForce NOW library:

• ‘83 (Steam)
• Crimson Desert (Xbox, available to play via Xbox Play Anywhere)
• DayZ (Xbox, available on Game Pass) 
• Diablo III (Ubisoft Connect)
• MapleStory M (Steam)
• Morbid Metal (Steam)
• PRAGMATA SKETCHBOOK – Demo (Steam)
• Rayman: 30th Anniversary Edition (Steam and Ubisoft)
• Tides of Tomorrow (Steam)
• Vampire Crawlers: The Turbo Wildcard from Vampire Survivors (Xbox)
• Windrose (Steam)

Outbound didn’t make it to the cloud this month due to its launch date being moved. Stay tuned to GFN Thursdays for the latest.

What are you planning to play this weekend? 

AI agent systems today juggle separate models for vision, speech and language — losing time and context as they pass data from one model to the other.

Unveiled today, NVIDIA Nemotron 3 Nano Omni is an open multimodal model that brings these capabilities together into one system, enabling agents to deliver faster, smarter responses with advanced reasoning across video, audio, image and text. This best-in-class model gives enterprises and developers a production path for more efficient and accurate multimodal AI agents with full deployment flexibility and control. 

Nemotron 3 Nano Omni sets a new efficiency frontier for open multimodal models with leading accuracy and low cost, topping six leaderboards for complex document intelligence, and video and audio understanding.

AI and software companies already adopting Nemotron 3 Nano Omni include Aible, Applied Scientific Intelligence (ASI), Eka Care, Foxconn, H Company, Palantir and Pyler, with Dell Technologies, Docusign, Infosys, K-Dense, Lila, Oracle and Zefr evaluating the model. 

“To build useful agents, you can’t wait seconds for a model to interpret a screen,” said Gautier Cloix, CEO of H Company. “By building on Nemotron 3 Nano Omni, our agents can rapidly interpret full HD screen recordings — something that wasn’t practical before. This isn’t just a speed boost: It’s a fundamental shift in how our agents perceive and interact with digital environments in real time.”

Nemotron 3 Nano Omni Enables Faster, Leaner Multimodal Agents

Consider an AI agent for customer support processing a screen recording while analyzing uploaded call audio and checking data logs — or an agent for finance tasked with parsing PDFs, spreadsheets, charts and voice notes. Today, most agentic systems accomplish these tasks with separate models for vision, speech and language. 

This approach increases latency through repeated inference passes, fragments context across modalities, and adds cost and inaccuracies over time.

By combining vision and audio encoders within its 30B-A3B, hybrid mixture-of-experts architecture, Nemotron 3 Nano Omni eliminates the need for separate perception models, driving inference efficiency at scale. It pairs this efficiency with strong multimodal perception accuracy, enabling AI systems to achieve 9x higher throughput than other open omni models with the same interactivity. The result is lower costs and better scalability without sacrificing responsiveness or quality.

In agentic systems, Nemotron 3 Nano Omni can work alongside proprietary cloud models or other NVIDIA Nemotron open models — such as Nemotron 3 Super for high-frequency execution or Nemotron 3 Ultra for complex planning — as well as proprietary models from other providers, to power sub-agents for agentic workflows such as computer use, document intelligence and audio-video reasoning.

• Computer use agents — Nemotron 3 Nano Omni powers the perception loop for agents navigating graphical user interfaces, reasoning over onscreen content and understanding user interface state over time. H Company’s latest computer usage agent, powered by Nemotron 3 Nano Omni, uses a native input resolution of 1920×1080 pixels to achieve high-fidelity visual reasoning. In preliminary evaluations on the OSWorld benchmark, this integration showed a significant leap in navigating complex graphical interfaces and used Nemotron 3 Nano Omni’s ability to process very high-resolution images. 
• Document intelligence — Interprets documents, charts, tables, screenshots and mixed-media inputs, enabling agents to reason across visual structure and text content coherently. Critical for enterprise analysis and compliance workflows.
• Audio and video understanding — For customer service, research and monitoring workflows, Nemotron 3 Nano Omni maintains audio-video context, tying what was said, shown and documented into a single reasoning stream instead of disconnected summaries.

Open and Customizable, Deployable Anywhere

Nemotron 3 Nano Omni is released with open weights, datasets and training techniques — giving organizations full transparency and control over how the model is customized and deployed. 

Developers can use tools like NVIDIA NeMo for customization, evaluation and optimization for domain-specific use cases. Because the Nemotron family of models is open, organizations can deploy them in environments that meet regulatory, sovereignty or data localization requirements.

The Nemotron 3 family — including Nano, Super and Ultra models — has seen over 50 million downloads in the past year. Omni extends the family’s capabilities into multimodal and agentic domains. 

The model is available on Hugging Face, OpenRouter and build.nvidia.com as an NVIDIA NIM microservice and through a broad ecosystem of NVIDIA Cloud Partners, inference platforms and cloud service providers. 

Its open, lightweight architecture supports consistent deployment from local systems like NVIDIA Jetson hardware, NVIDIA DGX Spark and DGX Station to data center and cloud environments. 

Visit the NVIDIA technical blog for tutorials, cookbooks and deployment guides for Nemotron 3 Nano Omni use cases. Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA news, joining the community and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.  

Explore self-paced video tutorials and livestreams.

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 advances in OpenUSD and NVIDIA Omniverse.

Manufacturing’s traditional design-build-test cycle rested on a single assumption: Real-world testing was the only reliable test environment. 

That assumption is now shifting. 

Today, high-fidelity simulation produces synthetic training data accurate enough for production-grade AI. This is enabling perception systems, reasoning models and agentic workflows to excel in live factory environments.

OpenUSD has emerged as the connective standard that makes this practical, and the manufacturers building on it are already experiencing measurable results. 

SimReady: The Content Standard for Physical AI 

As physical AI becomes integral to industrial operations, manufacturers face a foundational challenge: Assets don’t travel reliably between 3D pipelines. Every time an asset moves from a computer-aided design tool to a simulation platform, physics properties, geometry and metadata are lost — forcing teams to rebuild from scratch.

SimReady is the content standard, built on OpenUSD, defining what physically accurate 3D assets must contain to work reliably across rendering, simulation and AI training pipelines. 

In addition, NVIDIA Omniverse libraries provide the physics-accurate, photorealistic simulation layer where AI models are trained and validated before deployment. 

Four Ways Manufacturers Are Putting the NVIDIA Physical AI Stack to Work

ABB Robotics Closes the Sim-to-Real Gap at 99% Accuracy

ABB Robotics has integrated NVIDIA Omniverse libraries directly into RobotStudio HyperReality, its simulation platform used by more than 60,000 engineers globally. 

The platform represents robot stations as USD files running the same firmware as their physical counterparts, making it possible to train robots, test part tolerances and validate AI models before a production line exists. 

Synthetic training variations — such as lighting conditions and geometry differences — can be generated at scale, covering scenarios that would be impractical to replicate manually. 

“We’ve managed to vertically integrate the complete technology stack and optimize it to a point where we’re now achieving 99% accuracy on the simulated version,” said Craig McDonnell, managing director of business line industries at ABB Robotics.

The downstream outcomes: up to 50% reduction in product introduction cycles, up to 80% reduction in commissioning time and a 30-40% reduction in total equipment lifecycle cost.

JLR Compresses Four Hours of Aerodynamic Simulation to One Minute

JLR applied the same simulation-first principle to vehicle aerodynamics. Engineers trained neural surrogate models on more than 20,000 wind-tunnel-correlated computational fluid dynamics simulations across the vehicle portfolio — with 95% of aero-thermal workloads now running on NVIDIA GPUs. 

The Neural Concept Design Lab — built on Omniverse and deployed at JLR — visualizes aerodynamic changes in real time as designers adjust vehicle geometry, collapsing what was a sequential design-then-simulate cycle into a continuous loop. A result that once took four hours now takes one minute. 

Tulip Brings Real-Time Factory Intelligence to Terex for Operational Gains

Once a factory goes into production, a different intelligence challenge begins — one that simulation alone can’t address. 

Tulip Interface’s Factory Playback platform demonstrates how existing infrastructure can become an intelligence layer, turning operations records into something users can actually learn from. Tulip built Factory Playback on the NVIDIA Metropolis VSS Blueprint — a reference architecture for extracting structured intelligence from factory camera feeds — connecting camera streams, machine sensor data and operational context into a unified timeline of what actually happened. 

In addition, Factory Playback uses the NVIDIA Cosmos Reason vision language model to interpret camera streams and operator behaviors in real time, running on premises on NVIDIA GPUs.

Deployed at Terex, a global industrial equipment manufacturer with over 40 plants, the system is expected to deliver a 3% increase in yield and 10% reduction in rework. 

“I am excited to see what manufacturers will do with the power of AI to augment their daily capabilities,” said Rony Kubat, cofounder and chief information officer of Tulip Interfaces. 

Getting Started

SimReady assets, Omniverse libraries and NVIDIA’s physical AI stack provide a foundation developers can adopt, extend and combine across any industrial application. Here’s how to get started:

• See how NVIDIA and partners put physical AI to work on the factory floor at Hannover Messe.
• Start building autonomous robots, digital twins and AI-powered systems with these free, self-paced courses.
• Explore NVIDIA Isaac Sim and Omniverse libraries on the NVIDIA developer portal.
• Deploy the NVIDIA Metropolis VSS Blueprint on existing camera infrastructure to gain new insights from the shop floor. 
• Explore the SimReady Foundation specification framework on GitHub.
• Browse NVIDIA Cosmos Cookbook recipes for domain-specific physical AI applications across robotics, simulation and autonomous systems.
• Access the full Omniverse developer hub.
• Join the community to connect with fellow developers and innovators who are building the future with NVIDIA technologies.

AI agents have revolutionized developer workflows, and their next frontier is knowledge work: processing information, solving complex problems, coming up with new ideas and driving innovation. 

Codex, OpenAI’s agentic coding application, is enabling this new frontier. It’s now powered by GPT-5.5, OpenAI’s latest frontier model, which runs on NVIDIA GB200 NVL72 rack-scale systems. 

Over 10,000 NVIDIANs — across engineering, product, legal, marketing, finance, sales, HR, operations and developer programs — are already using GPT-5.5-powered Codex to achieve, in their words, “mind-blowing” and “life-changing” results. 

NVIDIA engineers have had access to GPT-5.5 through the Codex app for a few weeks, and the gains are measurable. Served on GB200 NVL72, which is capable of delivering 35x lower cost per million tokens and 50x higher token output per second per megawatt compared with prior-generation systems — economics that make frontier-model inference viable at enterprise scale.

Debugging cycles that once stretched across days are closing in hours. Experimentation that previously required weeks is turning into overnight progress in complex, multi-file codebases. Teams are shipping end-to-end features from natural-language prompts, with stronger reliability and fewer wasted cycles than earlier models. 

OpenAI’s stunning progress is just the latest example of NVIDIA’s work with every frontier model company — not just to accelerate the use of AI agents inside NVIDIA, but to help the company’s partners build the world’s best, lowest cost and most power efficient models for everyone.

As NVIDIA founder and CEO Jensen Huang told employees in a company-wide email urging everyone to use Codex: “Let’s jump to lightspeed. Welcome to the age of AI.”

A Deployment Built for Enterprise Security 

Just like humans, every agent needs its own dedicated computer. 

To ensure seamless operation within secure enterprise environments, the Codex app supports remote Secure Shell (SSH) connections to approved cloud virtual machines, allowing agents to work with real company data without exposing it externally. 

So to ensure maximum security and auditability, NVIDIA IT rolled out cloud virtual machines (VMs) for every employee to run their agent safely. This provides a dedicated sandbox for the agent to operate at its maximum capabilities while maintaining full auditability. Users can control the Codex agent running in the cloud VM from a user interface that every employee is familiar with.

A zero-data retention policy governs NVIDIA’s deployment, and agents access production systems with read-only permissions through command-line interfaces and Skills — the same agentic toolkit NVIDIA uses to run automation workflows across the company.

A Decade of Full-Stack Collaboration

The GPT-5.5 launch and the Codex rollout reflect more than 10 years of collaboration between NVIDIA and OpenAI. The partnership began in 2016, when Huang hand-delivered the first NVIDIA DGX-1 AI supercomputer to OpenAI’s San Francisco headquarters.

Since then, the two companies have worked closely across the full AI stack. 

NVIDIA was a day-zero partner for OpenAI’s gpt-oss open-weight model launch, optimizing model weights for NVIDIA TensorRT-LLM and ecosystem frameworks including vLLM and Ollama. 

OpenAI has committed to deploying more than 10 gigawatts of NVIDIA systems for its next-generation AI infrastructure — a buildout that will put millions of NVIDIA GPUs at the foundation of OpenAI’s model training and inference for years ahead.

And OpenAI and NVIDIA are early silicon and codesign partners: OpenAI provides feedback that informs NVIDIA’s hardware roadmap, and in turn gains early access to new architectures. That relationship produced a concrete milestone — the joint bring-up of the first GB200 NVL72 100,000-GPU cluster. The cluster completed multiple large-scale training runs and set a new benchmark for system-level reliability at frontier scale.

GPT-5.5 is the product of that infrastructure running at full strength. 

Learn more in OpenAI’s announcement.

GeForce NOW is doubling down on what matters most: gamers. This week’s upgrades bring smarter libraries, making it easier than ever for gamers to turn a PC collection into a cloud-powered flex.

It starts with giving existing libraries time to shine. Gamers can bring the games they love to the cloud, stream them with high performance and see the value of a GeForce NOW membership grow with new games, rewards and features.

First up, finding something to play gets an upgrade. The new in-app labels, first announced at GDC, are now live — making it simple to spot titles and new releases from connected subscription services like Xbox Game Pass and Ubisoft+.

All that sets the stage for six new games rolling onto the cloud this week. Leading the charge is Vampire Crawlers: The Turbo Wildcard, a chaotic new spin on the fast, unpredictable and packed-with-personality Vampire Survivors universe. Even the vampires can’t avoid the spotlight this time.

Round it out with a thunderous new Marvel Rivals skin for Thor, and it’s a lineup fitted to perfection.

Just Play It

GeForce NOW lets members connect their existing gaming subscriptions to bring more of their games with them on the go, across devices. Today, keeping track of which games can be streamed just got even easier. New in‑app game labels on GeForce NOW make it easy to see which titles are part of Xbox Game Pass or Ubisoft+ game libraries once accounts are connected.

Clear labels now appear directly on each game’s details — eliminating guesswork and making it simple to see exactly what’s available to play instantly from connected subscriptions. Stream hundreds of NVIDIA RTX-powered titles with a single click.

More clarity, less searching — and one more reason the cloud gaming library has never looked better.

Feeding Time

Get fired up for the feeding frenzy — Vampire Crawlers: The Turbo Wildcard is a roguelite horde escape game that stacks absurd power‑ups and turns every run into a highlight reel of close calls. Stay mobile, grab upgrades and try not to become the snack.

Every build becomes a delightful walking disaster, with weapons, perks and abilities combining into wild synergies that can turn a run from barely surviving to absolutely steamrolling in just a few upgrades. Dense enemy waves, screen‑filling attacks and unpredictable upgrade paths keep each attempt fresh — and dangerously prone to “one more run.”

On GeForce NOW, all that chaos stays razor sharp, with smooth performance for dodge‑heavy moments, crisp effects even when the screen is packed and the ability to jump back into the madness from any supported device.

Thunder Drip

The God of Thunder is trading his classic red cape for an umber‑toned, battle‑ready look that can only mean one thing: a serious style upgrade. GeForce NOW Premium members get first access to the Thor Midgard Umber Skin in Marvel Rivals starting today, and those on the free tier can grab it starting Friday, April 24, bringing stormy flair to the battlefield. 

Claim the reward by Saturday, May 23 — or before Odin hoards them all. Redeem through the GeForce NOW account portal and enter the code in game on Steam. Then stride forth, majestic and moody, because this look is eternal — at least until the next mythic crossover.

Time for Adventures

Members can look for the following new games this week:

• Vampire Crawlers: The Turbo Wildcard from Vampire Survivors (New release on Steam and Xbox, available on Game Pass, April 21)
• Tides of Tomorrow (New release on Steam, April 22, GeForce RTX 5080-ready)
• ‘83 (New release on Steam, April 23, GeForce RTX 5080-ready)
• Diablo III (New release on Ubisoft Connect, April 23)
• Crimson Desert (Xbox, available to play via Xbox Play Anywhere, GeForce RTX 5080-ready)
• MapleStory M (Steam)

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

Settled Systems
Pandora
Raccoon City

Thanks for taking us on a trip through the cloud @airiesummer #GFNShare pic.twitter.com/4XKgnCPl6J

— NVIDIA GeForce NOW (@NVIDIAGFN) April 21, 2026

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NVIDIA and Google Cloud have collaborated for more than a decade, co‑engineering a full‑stack AI platform that spans every technology layer — from performance‑optimized libraries and frameworks to enterprise‑grade cloud services. 

This foundation enables developers, startups and enterprises to push agentic and physical AI out of the lab and into production — from agents that manage complex workflows to robots and digital twins on the factory floor.

At Google Cloud Next this week in Las Vegas, the partnership reaches a new milestone, with advancements to expand Google Cloud AI Hypercomputer for AI factories that will  power the next frontier of agentic and physical AI. 

These include the new NVIDIA Vera Rubin-powered A5X bare-metal instances; a preview of Google Gemini on Google Distributed Cloud running on NVIDIA Blackwell and NVIDIA Blackwell Ultra GPUs; confidential VMs with NVIDIA Blackwell GPUs; and agentic AI on Gemini Enterprise Agent Platform with NVIDIA Nemotron open models and the NVIDIA NeMo framework.

Next-Generation Infrastructure: From NVIDIA Blackwell to Vera Rubin

At Google Cloud Next, Google announced A5X powered by NVIDIA Vera Rubin NVL72 rack-scale systems, which — through extreme codesign across chips, systems and software — deliver up to 10x lower inference cost per token and 10x higher token throughput per megawatt than the prior generation. 

A5X will use NVIDIA ConnectX-9 SuperNICs, combined with next-generation Google Virgo networking, scaling to up to 80,000 NVIDIA Rubin GPUs within a single site cluster and up to 960,000 NVIDIA Rubin GPUs in a multisite cluster, enabling customers to run their largest AI workloads on NVIDIA‑optimized infrastructure. 

“At Google Cloud, we believe the next decade of AI will be shaped by customers’ ability to run their most demanding workloads on a truly integrated, AI‑optimized infrastructure stack,” said Mark Lohmeyer, vice president and general manager of AI and computing infrastructure at Google Cloud. “By combining Google Cloud’s scalable infrastructure and managed AI services with NVIDIA’s industry‑leading platforms, systems and software, we’re giving customers flexibility to train, tune and serve everything from frontier and open models to agentic and physical AI workloads — while optimizing for performance, cost and sustainability.”

Google Cloud’s broad NVIDIA Blackwell portfolio ranges from A4 VMs with NVIDIA HGX B200 systems to rack-scale A4X VMs with NVIDIA GB200 NVL72 and A4X Max NVIDIA GB300 NVL72 systems, all the way to fractional G4 VMs with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. 

Customers can right-size their acceleration capabilities, whether using multiple interconnected NVL72 racks that scale out to tens of thousands of NVIDIA Blackwell GPUs, a single rack that can scale up to 72 Blackwell GPUs with fifth-generation NVIDIA NVLink and NVLink 5 Switch, or just one-eighth of a GPU. 

This comprehensive platform helps teams optimize every workload, from mixture-of-experts reasoning, multimodal inference and data processing to complex simulations for the next frontier of physical AI and robotics.

Leading frontier AI labs are already putting this infrastructure to work. Thinking Machines Lab is scaling its Tinker application programming interface (API) on A4X Max VMs with GB300 NVL72 systems to accelerate training, while OpenAI is running large‑scale inference on NVIDIA GB300 (A4X Max VMs) and GB200 NVL72 systems (A4X VMs) on Google Cloud for some of its most demanding inference workloads, including for ChatGPT. 

Secure AI Wherever It Needs to Run: Sovereign and Confidential

Google Gemini models running on NVIDIA Blackwell and Blackwell Ultra GPUs are now in preview on Google Distributed Cloud, so customers can bring Google’s frontier models wherever their most sensitive data resides. 

NVIDIA Confidential Computing with the NVIDIA Blackwell platform enables Gemini models to run in a protected environment where prompts and fine‑tuning data stay encrypted and can’t be seen or altered by unauthorized parties, including the infrastructure operators. 

In the public cloud, the preview of Confidential G4 VMs with NVIDIA RTX PRO 6000 Blackwell GPUs brings these protections to multi‑tenant environments — helping safeguard prompts, AI models and data so customers in regulated industries can access the power of AI without compromising on security or performance. 

This is the first confidential computing offering of NVIDIA Blackwell GPUs in the cloud, giving Google Cloud customers a new foundation for secure, high‑performance AI.

Open Models and APIs for Agentic AI

The NVIDIA platform on Google Cloud is optimized to run every kind of model — from Google’s frontier Gemini and Gemma families to NVIDIA Nemotron open models and the broader open weight ecosystem — equipping developers to build agentic AI systems that reason, plan and act. 

NVIDIA Nemotron 3 Super is available on Gemini Enterprise Agent Platform, giving developers a direct path to discovering, customizing and deploying NVIDIA‑optimized reasoning and multimodal models for agentic workflows. 

Google Cloud and NVIDIA are also making it easier to train and customize open models at scale. Managed Training Clusters on Gemini Enterprise Agent Platform introduced a new managed reinforcement learning (RL) API built with NVIDIA NeMo RL for accelerating RL training at scale while automating cluster sizing, failure recovery and job execution, so teams can focus on agent behavior and model quality instead of infrastructure management.

Cybersecurity leader CrowdStrike uses NVIDIA NeMo open libraries such as NeMo Data Designer, NeMo Automodel and NeMo Megatron Bridge to generate synthetic data and fine-tuning Nemotron and other open large language models for domain-specific cybersecurity. Running on Managed Training Clusters on Gemini Enterprise Agent Platform with NVIDIA Blackwell GPUs, these capabilities accelerate threat detection, investigation and response.

Building the Future of Industrial and Physical AI

Building industrial and physical AI at scale demands powerful hardware and a combination of open models, libraries and frameworks to develop these complex end-to-end workflows. 

NVIDIA AI infrastructure, open models and physical AI libraries available on Google Cloud, is mainstreaming industrial and physical AI applications, enabling customers to simulate, optimize and automate real-world workflows.

Solutions from leading industrial software providers, including Cadence and Siemens Digital Industries Software, are now available on Google Cloud, accelerated on NVIDIA AI infrastructure. These applications are powering the next-generation design, engineering and manufacturing of everything from chips to autonomous vehicles, robotics, aerospace platforms, heavy machinery and large-scale production systems.   

With NVIDIA Omniverse libraries and the open source NVIDIA Isaac Sim robotics simulation framework available on Google Cloud Marketplace, developers can build physically accurate digital twins and develop custom robotics simulations pipelines to train, simulate and validate robots before real-world deployment. 

NVIDIA NIM microservices for models like NVIDIA Cosmos Reason 2 can be deployed to Google Vertex AI and Google Kubernetes Engine. This enables robots and vision AI agents to see, reason and act in the physical world like humans, powering use cases such as automated data curation and annotation, advanced robot planning and reasoning, and intelligent video analytics agents for real-time insights and decision-making.

Together, these technologies help developers seamlessly move from computer-aided design to living industrial digital twins and AI‑driven robots, accelerating processes from design sign‑off to factory optimization on the NVIDIA platform running on Google Cloud.

Proven Impact: From Startups to Global Enterprises

Global enterprises, AI labs and high‑growth startups are using NVIDIA and Google Cloud’s co-engineered platform to move from prototyping to production faster, including Snap, Schrödinger and Salesforce. Snap is cutting the cost of large‑scale A/B testing by shifting data pipelines to GPU‑accelerated Spark on Google Cloud. Schrödinger is shrinking weekslong drug discovery simulations into just hours with NVIDIA accelerated computing on Google Cloud.

Startups are orchestrating the next wave of AI innovation — building new agents and AI‑native applications using NVIDIA accelerated computing on Google Cloud. 

As part of a broader ecosystem highlighted through NVIDIA Inception and Google for Startups, CodeRabbit and Factory are using NVIDIA Nemotron‑based models on Google Cloud to power code review and autonomous software development agents, while Aible, Mantis AI, Photoroom and Baseten are building enterprise data, video intelligence, generative imagery and managed inference solutions on the full‑stack NVIDIA platform on Google Cloud.

More than 90,000 developers have become a part of the joint NVIDIA and Google Cloud developer community in just over a year, tapping this platform to build and scale new AI applications.

In addition, NVIDIA has been honored at Next as Google Cloud Partner of the Year in two categories — AI Global Technology Partner and Infra Modernization Compute — in recognition of deep technical expertise and go-to-market alignment.

Together, NVIDIA and Google Cloud are giving customers a cloud‑scale platform to turn experimental agents and simulations into production systems that review code, secure fleets, enable new AI applications and optimize factories in the real world.

Learn more about the companies’ collaboration by attending NVIDIA sessions, demos and workshops at Google Cloud Next.

AI agents are transforming how work gets done across all industries, accelerating everything from content creation to decision-making.

NVIDIA’s expanded strategic collaborations with Adobe and WPP are bringing agentic AI to the center of enterprise marketing operations across creative production and customer experience orchestration. 

As demand for personalized customer experiences surges, brands require intelligent systems that can plan, create, produce and activate content continuously — without compromising control, governance or brand integrity.

Consider a global retailer delivering the right offer, image, copy and price, across millions of product, audience and channel combinations — updated in minutes instead of months. 

For marketing and creative teams, that means moving from one-size-fits-all campaigns to tailored experiences that are always on, always relevant and on brand. All of it is powered by intelligent systems that continuously generate and deliver content without sacrificing control, governance or brand integrity.

The expanded collaborations bring together three complementary strengths: Adobe’s creative and customer experience platforms and the new Adobe CX Enterprise Coworker, WPP’s global media and marketing expertise, and NVIDIA’s accelerated computing and software stack, including NVIDIA Nemotron open models, NVIDIA Agent Toolkit and the NVIDIA OpenShell secure runtime for building and running secure agentic AI systems.

As these agents begin orchestrating multistep workflows, tapping sensitive data and triggering actions across marketing stacks, enterprises need a way to enforce clear rules of engagement so every operation remains compliant, on brand and within defined risk boundaries.

Powered by the NVIDIA OpenShell runtime, every agent operates within a secure, isolated environment, delivering enterprise-grade control, consistency and auditability across the entire marketing lifecycle, with verifiable policy management, answering the question, “What can the agent do?” and not just, “What policy is in place?” 

In governed environments, enterprises can also keep key workflows and intelligence services inside their trust boundary, including securely invoking Adobe CX Intelligence as part of customer experience agents.

A live demo of CX Enterprise Coworker — powered by NVIDIA Agent Toolkit, including the OpenShell runtime and Nemotron models — will be featured during Adobe Summit’s day-two keynote taking place Tuesday, April 21, at 9 a.m. PT.

The collaboration enables:

• End-to-end agentic workflows: Adobe is developing creative and marketing agents that can generate, adapt and version on-brand assets. Adobe’s CX Enterprise Coworker orchestrates downstream customer experience workflows from personalization to activation, closing the loop between content creation and customer engagement.
• Controlled execution with NVIDIA OpenShell: Agents run in a policy-based, containerized sandbox designed to keep execution governed, observable and auditable, helping enterprises safely deploy long-running agentic workflows on premises or in the cloud.
• Commercially safe content at scale: Adobe Firefly Foundry, accelerated by NVIDIA AI infrastructure, can help organizations deeply tune custom models on their proprietary assets, enabling agents to generate commercially safe content at scale and aligned to brand identity.
• A 3D digital twins solution for scalable marketing production: Adobe’s cloud-native 3D digital twin solution is now generally available, built on NVIDIA Omniverse libraries and OpenUSD. 3D digital twins serve as persistent product identities that agents use to automate and scale high-fidelity content creation across formats, markets and configurations.

Creative Intelligence Meets Performance Intelligence With Policy-Governed Agents

Governed environments such as the ones enabled by this collaboration act as a set of “guardrails” that keep AI operations observable and auditable, preventing the system from acting outside of a company’s specific data boundaries or brand rules.

By combining Adobe’s creative platforms, WPP’s media and marketing expertise and NVIDIA’s secure infrastructure with CX Enterprise Coworker, brands no longer have to choose between speed and safety. Autonomous agents can now generate, adapt and activate content at scale while operating within governed, policy-driven environments.

The result is a new foundation for agentic marketing — where creative intelligence, performance and trust are built in from the start and delivered at global scale.

Watch NVIDIA founder and CEO Jensen Huang’s Adobe Summit fireside chat with Adobe CEO Shantanu Narayen.

Manufacturing is at an inflection point. Across every major industrial economy, the pressure to do more with less — due to faster design cycles, leaner operations and strain on skilled labor pools — is accelerating the shift to AI-driven production. 

The question is no longer whether to adopt AI, but how fast and at what scale. 

At Hannover Messe 2026, running April 20-24 in Hannover, Germany, NVIDIA and its partners are demonstrating AI-driven manufacturing in action. Attendees will experience how advancements in accelerated computing, AI physics, agents and robotics are powering industrial innovation — from agentic design and engineering to real-time simulation, vision AI agents and humanoid robots operating in factories. 

The factory of the future isn’t just a concept. It’s being built now.

AI Infrastructure: Powering Europe’s Next Industrial Era

Running AI at scale across the factories and supply chains that manufacturing output relies on requires the right underlying infrastructure. As AI becomes foundational to how products, processes and facilities are designed, built and optimized, manufacturers need a unified, sovereign foundation that’s secure, scalable and built for industrial scale.

The Industrial AI Cloud, one of Europe’s largest AI factories built in Germany by Deutsche Telekom on NVIDIA AI infrastructure, is a blueprint for the future. It provides a secure, sovereign foundation for accelerating AI and robotics across Europe’s industries. 

At the show, industry leaders, including Agile Robots, SAP, Siemens, PhysicsX and Wandelbots, will share how they are using this sovereign AI platform to run AI-accelerated workloads ranging from AI physics-driven, real-time simulation to factory-scale digital twins and software-defined robotics. EDAG, a leading independent engineering service provider, also announced it will be running its industrial metaverse platform, metys, on the Industrial AI Cloud — bringing sovereign AI infrastructure to automotive and industrial engineering at scale.

To support the increasing demand for AI infrastructure, Dell Technologies, IBM, Lenovo and PNY are also showcasing NVIDIA-accelerated systems, from the edge to data centers, enabling manufacturers to run faster simulations and develop and deploy computer vision, AI agents and robotics in production at scale.

AI-Driven Engineering

As industrial systems grow more complex, the software that engineers rely on to design, simulate and test them is being transformed with AI physics and agentic AI to keep pace. At Hannover Messe, NVIDIA partners are showcasing how AI-accelerated design and simulation is unlocking new possibilities.

Cadence, Dassault Systèmes, Siemens and Synopsys are integrating NVIDIA CUDA-X, AI physics and NVIDIA Omniverse libraries, as well as NVIDIA Nemotron open models, across their software — enabling real-time, physics-grounded simulation, AI-powered design exploration and agentic workflows that empower engineers.

Real-Time Factory Simulation

Factory-scale digital twins are critical for unlocking process simulation, real-time operations, and the testing and orchestration of robot fleets. At Hannover Messe, partners across manufacturing, energy and automotive are showing how digital twins, built on Omniverse libraries and OpenUSD, enable their customers to design, stress-test and continuously optimize their operations.

ABB will showcase how the integration of NVIDIA Omniverse libraries and Microsoft Azure cloud services into its ABB Genix Industrial IoT and AI Suite enables operations teams to understand asset performance in full context and engage AI agents to accelerate root-cause analysis.

Dassault Systèmes will demonstrate how AI-driven factories of the future are powered by virtual twin experiences. Attendees will see how these virtual twins harness NVIDIA physical AI libraries to enable autonomous, software-defined production and smarter, agile manufacturing systems.

Kongsberg Digital will highlight how integrating NVIDIA Omniverse libraries into its Kognitwin platform delivers spatial intelligence across critical energy infrastructure. The combination of digital twin models, live operational data and AI agents enables its customers to analyze complex assets, test scenarios virtually and optimize performance before changes reach the physical world.

Microsoft is demonstrating how NVIDIA Omniverse libraries integrated with Microsoft Fabric Real-Time Intelligence and IQ enable physically accurate, real-time simulations for organizations to design, simulate and optimize physical systems, while the Azure Physical AI Toolchain — built on the NVIDIA Physical AI Data Factory Blueprint — accelerates the deployment of physical AI and autonomous robots into production.

Siemens will highlight how integrating NVIDIA Omniverse libraries into its Digital Twin Composer solution turns multi-domain engineering and operational data into a comprehensive, simulation-ready digital twin — helping its customers deliver throughput gains and identify production issues before physical changes.

By combining the Wandelbots NOVA Platform with Omniverse libraries such as NVIDIA Omniverse NuRec, Wandelbots highlights a powerful pathway to digitalize real-world facilities into physically accurate simulations. For solutions like Gessmann’s GESSbot robots, this opens up future opportunities to accelerate commissioning and reduce deployment risks across complex industrial sites.

Bringing AI Agents to the Factory Floor

Traditional AI answers problems under a rigid set of conditions. AI agents bring a new level of proactive and adaptive intelligence that provides the context on what’s seen and analyzes what’s happening before taking action. 

At the show, attendees will see how vision AI agents built on NVIDIA Metropolis libraries along with Nemotron and NVIDIA Cosmos open models are transforming industrial operations, combining multiple data streams with existing camera infrastructure to reach new levels of quality control, operational efficiency and worker safety.

Invisible AI is launching its Vision Execution System, a vision AI system that uses agents to capture, structure and analyze every production cycle on the factory floor in real time. Built with the NVIDIA Metropolis VSS Blueprint and NVIDIA Cosmos Reason 2 and Nemotron models, these autonomous AI agents surface actionable insights directly to operators before issues compound. This class of production intelligence is already driving measurable gains at some of the world’s largest automotive manufacturing factories like Toyota.

Tulip Interfaces will showcase Factory Playback, which uses the VSS blueprint and Cosmos Reason 2 to synchronize machine telemetry, operator workflows, quality events and video into a searchable, contextualized timeline of operations. Terex, a global industrial equipment manufacturer with over 40 plants, uses the platform to gain valuable insights and is expected to achieve an estimated 3% increase in yield and 10% reduction in rework.

Fogsphere extends vision AI into some of the most demanding manufacturing and industrial environments. Its Vision Agent platform — now supporting ARM-based edge deployment and training workflows built on NVIDIA Cosmos Reason 2 and the Metropolis VSS Blueprint — enables its customers to build and finetune visual AI agents. Saipem, an engineering services company in the energy and industrial ecosystem, is using the platform to build agents that can detect and respond in real time to high-risk safety and environmental events. 

Machines That Can Think

AI reasoning is breaking industrial robots free from single-task constraints and time-consuming reprogramming, giving them the ability to navigate unstructured environments, learn new tasks and act autonomously. At Hannover Messe, NVIDIA partners are demonstrating robots completing real production tasks and physical AI frameworks that put autonomous automation within reach of manufacturers of every size.

At a Siemens blueprint autonomous electronics factory in Erlangen, Germany, Humanoid’s HMND 01 wheeled humanoid — running the NVIDIA Jetson Thor edge AI module for on-robot compute and developed using Isaac Sim and Isaac Lab open frameworks for simulation and reinforcement learning — has completed autonomous logistics operations in a first proof of concept within the production environment. Humanoid’s simulation-first development compressed what typically takes up to two years of hardware development down to just seven months.

SCHUNK’s GROW automation cell brings physical AI into production in a standardized, deployable form. NVIDIA Omniverse libraries and Isaac simulation frameworks enable robot behavior to be simulated, trained and validated before the cell goes live. Wandelbots’ NOVA platform connects simulation to the shop floor for continuous refinement, while EY designs the operating model to scale it across Europe’s small- and medium-sized enterprises.

Using NVIDIA’s physical AI stack, including the Physical AI Data Factory Blueprint and NVIDIA IGX Thor for industrial-grade edge compute with functional safety, Hexagon Robotics is accelerating robot training, validation and deployment. The results are already taking shape, with AEON set to perform assembly operations at a BMW Plant in Leipzig — marking one of the first humanoid deployments in a German production environment.

QNX has expanded its collaboration with NVIDIA to power safety‑critical edge AI systems for robotics, medical and industrial applications, with QNX OS for Safety 8.0 now integrated on NVIDIA IGX Thor and the NVIDIA Halos safety stack.

Explore NVIDIA AI technologies for industrial and manufacturing by joining NVIDIA at Hannover Messe.

Head straight for orbit with GeForce NOW — no space helmet required. 

PRAGMATA, Capcom’s long-awaited sci-fi action adventure, touches down on GeForce NOW the same day it launches worldwide. The futuristic journey through a cold lunar station in the near future can be streamed instantly from the cloud to almost any device, no console or heavy hardware needed.

That’s only the beginning. Five new titles join the cloud this week, expanding April’s gaming galaxy with fresh adventures and endless possibilities. 

Plus, the GeForce NOW Ultimate membership comes to gamers in India for the first time, with the service now available in beta and operated by NVIDIA.               

Time to see what’s landing on GeForce NOW.

A Mission Gone Wrong

PRAGMATA is Capcom’s newest sci-fi action adventure that blends heart, high-tech and a hauntingly quiet world set in the near future. Step into the boots of Hugh Williams, an investigator navigating a lunar research station gone silent and Diana, a young android. Armed with an arsenal of weapons and the ability to hack, every corridor and console becomes part of a cinematic experience filled with tense exploration and fast-paced action.

The story unfolds amid the cold vacuum of the moon after a massive quake hits the station researching Lunafilament — a material said to be able to create anything given enough data. Awake, injured and disoriented, Hugh crosses paths with Diana, the mysterious android girl known as a Pragmata. Now, they must work together as they face the rogue station on their way back to Earth.

PRAGMATA shines in stunning clarity with ray-traced lighting and NVIDIA DLSS 4 technology boosting frame rates and image quality. Stream it on launch day at full fidelity, even without the latest hardware — no need to wait on a large install or worry about hardware specs. Hugh and Diana’s lunar mystery is ready when the moment strikes.

Let’s Play Today

Fortnite: Save the World is now free and ready to stream instantly on GeForce NOW. The storm hits hard and the heroes hit harder — jump into a co-op adventure that mixes base-building, looting and all-out action against waves of Husks. Craft the ultimate fort, set sneaky traps and team up to protect what’s left of the world — no waiting for updates or patches, just pure fight-and-build mayhem. The storm’s closing in, but thanks to the cloud, the party’s jumping right into the action. “Save the World” isn’t available on mobile devices, including tablets.

In addition, members can look for the following:

• REPLACED (New release on Steam and Xbox, available on Game Pass, April 14, GeForce RTX 5080-ready)
• Windrose (New release on Steam, April 14, GeForce RTX 5080-ready)
• Cthulhu: The Cosmic Abyss (New release on Steam, April 16, GeForce RTX 5080-ready)
• PRAGMATA (New release on Steam, April 16, GeForce RTX 5080-ready)
• PRAGMATA SKETCHBOOK – DEMO (Steam, GeForce RTX 5080-ready)

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

You’re loading into a game and can invite ONE person to your squad…

Who’s your pick?

— NVIDIA GeForce NOW (@NVIDIAGFN) April 15, 2026

Traditional data centers only stored, retrieved and processed data. In the generative and agentic AI era, these facilities have evolved into AI token factories. With AI inference becoming their primary workload, their primary output is intelligence manufactured in the form of tokens. 

This transformation demands a corresponding shift in how the economics of AI infrastructure, including total cost of ownership (TCO), is assessed. Enterprises evaluating AI infrastructure still too often focus on peak chip specifications, compute cost or floating point operations per second for every dollar spent, aka FLOPS per dollar. 

The distinction that matters is this:

• Compute cost is what enterprises pay for AI infrastructure, whether rented from cloud providers or owned on premises.
• FLOPS per dollar is how much raw computing power an enterprise gets for every dollar spent, but raw compute and real-world token output are not the same thing. 
• Cost per token is an enterprise’s all-in cost to produce each delivered token, usually represented as cost per million tokens.

The first two are merely input metrics. Optimizing for inputs while the business runs on output is a fundamental mismatch. 

Cost per token determines whether enterprises can profitably scale AI. It’s the one TCO metric that directly accounts for hardware performance, software optimization, ecosystem support and real-world utilization — and NVIDIA delivers the lowest cost per token in the industry. 

What Are the Factors That Lower Token Cost?

Understanding how to optimize token cost requires looking at the equation for calculating cost per million tokens.

In this equation, many enterprises evaluating AI infrastructure focus on the numerator: the cost per GPU per hour. For cloud deployments, this is the hourly rate paid to a cloud provider; for on-premises deployments, it’s the effective hourly cost derived from amortizing owned infrastructure. The real key to reducing token cost, however, lies in the denominator: maximizing the delivered token output.

That denominator carries two business implications.

• Minimize token cost: When this increase in token output is reflected through the cost equation, it drives down cost per token, which is what grows the profit margin on every interaction served.
• Maximize revenue: More tokens delivered per second also translates to more tokens per megawatt, which means more intelligence to use in AI-powered products and services, generating more revenue from the same infrastructure investment.

So focusing only on the numerator means missing what drives the denominator. Think of it as an “inference iceberg”: The numerator sits above the surface, visible and easy to compare. The denominator is everything beneath the surface, which represents key factors that determine real-world token output. Accurately evaluating AI infrastructure starts with asking what lies beneath. 

• Surface-level inquiry:
  • What is the cost per GPU hour?
  • What are the peak petaflops and high-bandwidth memory capacity?
  • What are the FLOPS per dollar?
• In-depth cost analysis:
  • What is the cost per million tokens? Specifically, what is the cost per million tokens for large-scale mixture-of-experts (MoE) reasoning models, which represent the most widely deployed type of AI models?
  • What is the delivered token output per megawatt? For on-premises deployments especially, where capital commitment to land, power and infrastructure is substantial, maximizing intelligence produced per megawatt is critical.
  • Can the scale-up interconnect handle the “all-to-all” traffic of MoE models?
  • Is FP4 precision supported? Can the inference stack make use of FP4 while maintaining high accuracy?
  • Does the inference runtime support speculative decoding or multi-token prediction to increase user interactivity?
  • Does the serving layer support disaggregated serving, KV-aware routing, KV-cache offloading and other optimizations?
  • Does the platform support the unique workload requirements of agentic AI — including ultralow latency, high throughput and large input sequence lengths?
  • Does the platform support the full lifecycle, from training and post-training to high-scale inference, across all model architectures, to ensure infrastructure fungibility and high utilization?

Every one of these algorithmic, hardware and software optimizations must be active and integrated, or the denominator collapses. A “cheaper” GPU that delivers significantly fewer tokens per second results in a much higher cost per token. AI infrastructure that gets it right across the full stack ensures that every optimization enhances the others.

Why Does Cost per Token Matter Much More Than FLOPS per Dollar?

The following data for the DeepSeek-R1 AI model demonstrates the difference between theoretical and actual business outcomes.

Looking at compute cost alone, the NVIDIA Blackwell platform appears to cost roughly 2x more than NVIDIA Hopper — but compute cost says nothing about the output that investment buys. An analysis of mere FLOPS per dollar suggests a 2x NVIDIA Blackwell advantage compared with the NVIDIA Hopper architecture. However, the actual outcome is orders of magnitude different: Blackwell delivers more than 50x greater token output per watt than Hopper, resulting in nearly 35x lower cost per million tokens. 

Note: Data is sourced from NVIDIA analysis and the SemiAnalysis InferenceX v2 benchmark. 

This massive divergence proves NVIDIA Blackwell delivers a massive leap in business value over the earlier Hopper generation that far outpaces any increase in system cost.

How to Choose the Right AI Infrastructure

Comparing AI infrastructure based on compute cost or theoretical FLOPS per dollar isn’t just insufficient; it doesn’t provide an accurate representation of inference economics. As the data demonstrates, an accurate evaluation of AI infrastructure’s revenue potential and profitability requires a shift from input metrics to cost per token and delivered token output.

NVIDIA delivers the industry’s lowest token cost and highest token throughput through extreme codesign across compute, networking, memory, storage, software and partner technologies. Moreover, the constant optimization of open source inference software such as vLLM, SGLang, NVIDIA TensorRT-LLM and NVIDIA Dynamo built on the NVIDIA platform means that on existing NVIDIA infrastructure, token output continues to increase and the cost per token continues to decline long after it’s acquired.

Leading cloud providers and NVIDIA cloud partners are already delivering this advantage at scale. Partners such as CoreWeave, Nebius, Nscale and Together AI have deployed NVIDIA Blackwell infrastructure and optimized their stacks to bring enterprises the lowest token cost available today, with the full benefit of NVIDIA’s hardware, software and ecosystem codesign behind every interaction served.

The NAB Show 2026 trade show, running April 18-22 in Las Vegas, is set to showcase a wave of new features and optimizations for top video editing applications. Bringing together over 60,000 content professionals from across the broadcast and media and entertainment industries, the event highlights how video editors, livestreamers and professional creators are exploring new tools, accelerated by NVIDIA RTX technology, to enhance and streamline their creative workflows.

At the show, Adobe is announcing a new Adobe Premiere Color Mode in beta. 

Designed to function as a dedicated grading environment nested directly within Premiere, it offers a clean, responsive interface that lets editors stay in their creative flow rather than relying on external tools for color correction. Tapping into GPU acceleration on NVIDIA GeForce RTX- and NVIDIA RTX PRO-equipped systems, this streamlined workflow, operating in 32-bit color depth for the first time, delivers significantly faster performance and quality.

NVIDIA also launched a new update to NVIDIA Project G-Assist — an experimental AI assistant that helps tune, control and optimize GeForce RTX systems. 

Color Meets Compute

Premiere’s Color Mode is a new clean, responsive interface within Adobe Premiere that enables editors to do color grading on native videos. Every element is designed to guide editors through the grading process without distractions. A large program monitor anchors the experience, providing immediate visual feedback as adjustments are made to enable faster decision-making and more precise control.

A clip grid view allows editors to visualize progression across shots in a sequence. This makes it easier to maintain consistency across scenes and ensure a cohesive look throughout a project. 

Controls are organized into focused modules, each tailored to a specific aspect of color grading. Multiple modules can be active simultaneously, giving editors flexibility while maintaining clarity. Each control features a unique heads-up display (HUD), providing contextual guidance without cluttering the interface.

Color grading is one of the most computationally intensive tasks in post-production. Every adjustment — bidirectional controls, multi-zone tonal shaping and stacked color operations — runs on NVIDIA GPUs, accelerating playback, iteration and visual feedback.

Editors can work with up to six luminance adjustment zones, moving beyond traditional highlights, midtones and shadows models. This allows for more nuanced tonal control and finer adjustments across the image. 

Visual scopes are context-aware, dynamically adapting based on the selected tool. HUD overlays provide visual cues directly within the scopes, helping editors understand how their adjustments affect the image without needing to interpret complex visual scopes and graphs.

The entire system now operates in 32-bit color depth precision, delivering maximum color fidelity and preventing unwanted clipping. Editors retain full control, with the ability to clip colors intentionally when needed for creative effect. Color styles can also be applied flexibly, at the sequence, clip, reel or custom group level, making it easier to manage looks across complex projects.

Download the Adobe Premiere (beta) to get started with Color Mode. 

Project G-Assist: Enhanced Recommendations and Controls 

The NVIDIA Project G-Assist on-device AI assistant helps users get the most out of their hardware. Today’s update adds an advanced detection system for gaming settings, as well as an enhanced knowledge system, enabling G-Assist to deliver higher accuracy when providing advice or adjusting settings for esports and AAA gaming.

The assistant can also now control more settings across systems. It can configure advanced RTX features from the NVIDIA App, including NVIDIA DLSS Overrides, Smooth Motion, RTX HDR, Digital Vibrance and encoder settings.

Download Project G-Assist v0.2.1 from the NVIDIA App.

#ICYMI: The Latest Updates for RTX AI PCs

Learn how visual effects shop Corridor Crew’s Niko Pueringer built his own green screen key tool, powered by NVIDIA RTX GPUs, at NAB. Stop by the Puget Systems booth on Monday, April 20, at 1 p.m. PT for a special presentation, or tune in on NVIDIA Studio’s YouTube channel on Tuesday, April 21, at 12 p.m. PT to watch the full session.

Also at NAB, join NVIDIA’s Sabour Amirazodi for a special presentation at the ASUS booth on Tuesday, April 21, at 11 a.m. PT. Amirazodi will showcase how guiding generative AI can produce creative outputs like storyboards or entire movie trailers — based on a single image input. 

Check out content creator Gavin Herman’s Studio Session, “How to Edit Professional Talking Head Videos in DaVinci Resolve,” on the NVIDIA Studio YouTube channel. Generative workflow specialists can watch this two-hour, instructor-led workshop on how to use NVIDIA GPU acceleration for ComfyUI.

LM Studio is now an official OpenClaw provider. OpenClaw can now run local models through LM Studio on NVIDIA GPUs, unlocking faster on-device performance.

Unsloth and NVIDIA have teamed up to eliminate hidden bottlenecks that slow down fine-tuning on NVIDIA GPUs, improving fine-tuning performance by 15%. 

Google’s Gemma 4 family of omni-capable models are built for local AI across a wide range of devices. Google and NVIDIA have optimized Gemma 4 for NVIDIA GPUs, enabling efficient performance on NVIDIA RTX-powered PCs and workstations, NVIDIA DGX Spark personal AI supercomputers and NVIDIA Jetson Orin Nano edge AI modules.

Check out this NVIDIA GTC session on how developers can build, run and optimize AI agents locally on NVIDIA GPUs, covering everything from quantization to backends like Ollama and applications like OpenClaw and ComfyUI.

Wondershare Filmora has added a new feature for Eye Contact Correction based on the NVIDIA Broadcast Eye Contact feature. This feature runs on the cloud on NVIDIA GPUs, designed to refine the gaze of subjects in post production for a more natural, confident and camera-ready look, delivering polished, professional videos in seconds. 

Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.

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This National Robotics Week, NVIDIA is highlighting the breakthroughs that are bringing AI into the physical world — as well as the growing wave of robots transforming industries, from agricultural and manufacturing to energy and beyond.

Advancements in robot learning, simulation and foundation models are accelerating development, enabling robots to move from training in virtual environments to real-world deployment faster than ever.

With NVIDIA platforms for simulation, synthetic data and AI-powered robot learning, developers now have the tools to build machines that can perceive, reason and act in complex environments.

Building the Next Generation of AI Robots

At NVIDIA GTC last month, a new wave of technologies was introduced to accelerate the development of AI-powered robots.

At the core is a full-stack, cloud-to-robot workflow that connects simulation, robot learning and edge computing — making it faster to build, train and deploy intelligent machines.

Key announcements include:

• New NVIDIA Isaac GR00T open models enable robots to understand natural language instructions and perform complex, multistep tasks using vision language action reasoning.
• New NVIDIA Cosmos world models for generating synthetic data and training robots at scale help systems learn more efficiently and generalize across environments.
• The general availability of open source physics engine Newton 1.0 provides a fast and reliable foundation for dexterous robot manipulation with accurate collision detection, realistic object contact and stable simulation of complex systems with both rigid and flexible parts.
• Expanded simulation capabilities with the general availability of NVIDIA Isaac Sim 6.0, Isaac Lab 3.0 and Omniverse NuRec technologies allow developers to model real-world scenarios and validate robotic systems before deployment.

Watch on-demand sessions from the NVIDIA GTC global AI conference to catch up on recent breakthroughs in robotics, showcased by leading experts in the field.

Driving Breakthroughs in Surgical Precision

PeritasAI is advancing a new generation of surgical robotics by integrating physical AI into real-world operating environments. Using NVIDIA Isaac for Healthcare and the Rheo blueprint for hospital automation, the company is developing multi-agent intelligence that can sense, coordinate and act in real time.

In collaboration with Lightwheel and Advent Health Hospitals, this work brings embodied intelligence into the operating room — supporting surgical teams with situational awareness, sterile coordination and intelligent management of instruments, implants and workflows.

From Words to Motion: NVIDIA NemoClaw Brings Natural Language Commands to Isaac Sim

NVIDIA Omniverse developer Umang Chudasama has integrated NVIDIA NemoClaw with NVIDIA Isaac Sim to navigate a Nova Carter autonomous robot using plain natural language commands — no manual coding required. NemoClaw translates text instructions (like “move two meters forward”) into executable Python scripts, which are then sent to Isaac Sim via a custom REST application programming interface in real time.

The entire system runs within Isaac Sim, giving the robot a realistic, physics-accurate warehouse environment to operate in before ever touching the real world. Pairing Isaac Sim with NemoClaw means faster development, safer testing and a smarter path to deployment. Rather than programming robots line by line, developers can now simply talk to them, marking a meaningful shift toward truly collaborative, language-driven robotics.

OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework

Underwater simulators are crucial for developing reliable perception systems, but they still struggle with accurate physics‑based sensor modeling and fast rendering. 

Helping close this gap is OceanSim, a GPU‑accelerated, high‑fidelity simulator developed by researchers at the University of Michigan. It uses advanced physics‑based rendering techniques to make synthetic underwater images look more realistic. Using GPUs, the simulator can render imaging sonar in real time and generate synthetic data quickly. 

OceanSim uses NVIDIA Isaac Sim and plugs into NVIDIA Omniverse libraries, creating a seamless link between robot‑learning research and underwater robotics. This integration lets developers easily develop and deploy embodied AI techniques for underwater applications.

RoboLab: Benchmarking the Next Generation of Generalist Robots

RoboLab is a high-fidelity simulation benchmark for developing and evaluating generalist robot policies — powering systems designed to perform diverse tasks across environments.

Built on NVIDIA Isaac and NVIDIA Omniverse simulation technologies, RoboLab taps into photorealistic environments and physics-based modeling to train and test robotic policies at scale. This enables researchers to measure how well behaviors learned in simulation transfer to the real world as tasks grow in complexity. 

By combining advanced simulation with structured evaluation, RoboLab accelerates the path from virtual training to real-world deployment.

RoboLab features will be incorporated into the roadmap of NVIDIA Isaac Lab-Arena, an open source framework for large-scale policy setup and evaluation.

Smarter Palletizing With AI-Driven Reasoning

In warehouse environments, palletizing robots typically follow fixed rules — handling boxes the same way regardless of contents, condition or fragility. A project developed by Doosan Robotics introduces a more adaptive approach using NVIDIA Cosmos Reason.

By analyzing a single camera image, the system can infer box contents, detect damage and adjust how each item is handled — such as placement, speed and grip — based on estimated weight and fragility. This reduces common issues like incorrectly stacking damaged or fragile goods.

To build robots that understand the physical world before they ever deploy in it, robotics researchers and developers are building policy models powered by NVIDIA Cosmos world foundation models (WFMs). Toyota Research Institute customizes Cosmos WFMs for their own world model to achieve state-of-the-art results across dynamic view synthesis, state-of-the-art teleoperation data augmentation and navigation world models.

Mimic robotics takes a different angle with mimic-video, a video-action model that pairs a pretrained internet-scale video model with a flow-matching action decoder, replacing the static image-language backbones of traditional VLAs with video-learned physical dynamics — achieving 10x better sample efficiency and 2x faster convergence on real-world manipulation tasks. 

Together, both teams demonstrate a fundamental shift: robots trained on world models that capture physics and causality need dramatically less real-world data to perform reliably in conditions they’ve never seen.

Open, Intelligent Robotics on NVIDIA Jetson: Community Innovations Powering the Next Wave of Physical AI

This National Robotics Week, OpenClaw running on the NVIDIA Jetson platform showcases how quickly open source innovation is evolving into real-world, intelligent robotics. 

From practical applications to innovative projects, the robotics community is building what’s next — and fast. 

Developers are pushing the boundaries of autonomy — including hardware-in-the-loop testing powered by Jetson Thor, evaluating camera streams from NVIDIA Isaac Sim and even building systems that can generate their own code to complete tasks.

In addition, OpenClaw now running entirely locally on NVIDIA Jetson Thor — powered by optimized NVIDIA Nemotron open models and the vLLM open inference library — marks a major leap toward private, low-latency edge AI for robotics. And innovations like the NVIDIA NemoClaw stack on Jetson are expanding what’s possible at the intersection of open source and high-performance robotics platforms.

Training and Refining Movement in Simulation 

Gennady Plyushchev, a robotics creator known as Skyentific, is documenting the process of building a walking bipedal robot, from simulation and design to real-world deployment — showcasing a simulation-first approach to robot development.

By using NVIDIA Isaac based simulation workflows alongside NVIDIA Jetson for on-device AI and control, the project demonstrates how developers can rapidly iterate in virtual environments before deploying to physical systems. 

The result highlights a broader shift in robotics: using AI, simulation and edge computing to accelerate development and bring increasingly capable humanoid robots to life.

University of Maryland Researchers Develop Robots for Complex Household Tasks

To bring robots into everyday life, researchers at the University of Maryland, recipients of a grant from the NVIDIA Academic Grant Program, are developing AI-powered humanoid systems capable of performing complex household tasks with greater autonomy.

The project centers on building robot foundation models that unify perception, planning and control. Using the NVIDIA Isaac open robotics development platform, researchers can create photorealistic, high-fidelity virtual home environments populated with diverse objects and layouts, allowing robots to practice millions of task variations and safely test rare or complex scenarios.

NVIDIA RTX PRO 6000 Blackwell GPUs for training large models and NVIDIA Jetson AGX Thor developer kits for efficient deployment on physical robots help bridge the gap between research and real-world applications.

By combining advancements in generative AI, sequential decision-making and scalable computing, the work represents a key step toward general-purpose robots that can support people in homes, healthcare settings and beyond.

Announcing the MassRobotics Fellowship

The second cohort of the Amazon Web Services (AWS) MassRobotics fellowship comprises startups being recognized for compelling industrial use cases harnessing robotics and computer vision. They will receive access to technical resources and AWS cloud credits.

The cohort includes NVIDIA Inception members Burro, Config Intelligence, Deltia, Haply Robotics, Luminous Robotics, Roboto AI, Telexistence, Terra Robotics and WiRobotics, each developing technologies spanning humanoid robotics, industrial automation, haptics and agricultural systems.

Burro creates autonomous agricultural robots for tasks like grape harvesting and crop scouting.

Config Intelligence builds data infrastructure for general-purpose bimanual robotics to enable reliable two-handed tasks in real-world settings.

Deltia provides AI-driven manufacturing intelligence that optimizes assembly lines using computer vision and analytics.

Haply Robotics designs haptic control devices that serve as “steering wheels” for physical AI systems across industries.

Luminous Robotics deploys AI-powered robotic systems for fast, low-cost solar-panel installation and maintenance.

Roboto AI offers a data-analytics platform that accelerates robot development by managing and analyzing robotics data.

Telexistence develops AI-powered humanoid robots and remote-controlled systems for retail and logistics. 

Terra Robotics develops laser-weeding agricultural robots to automate sustainable farming.

WiRobotics creates wearable walking-assist and humanoid robots to enhance mobility and physical interaction, using training data from assisted products to train its humanoids.

Accelerating How Utility-Scale Solar Projects Are Built in the Field

Maximo, a solar robotics business incubated within The AES Corporation, recently completed a 100-megawatt solar installation using its robot fleet. Developed with NVIDIA accelerated computing, NVIDIA Omniverse libraries and the NVIDIA Isaac Sim framework, Maximo demonstrated that autonomous installations can operate reliably for utility-scale projects.

The solution improves installation speed, safety and consistency, helping close the gap between rising demand for faster time to power and construction capacity.

As solar expansion faces ongoing labor constraints and rising demand, AI-driven field robotics systems like Maximo are helping accelerate infrastructure buildout, reduce costs and redefine how energy projects are delivered.

Aigen Advances Sustainable Farming With Agricultural Robotics

To help regenerate the Earth, Aigen’s solar-powered autonomous robots are breaking farmers’ dependency on chemicals through precision weed control powered by vision AI.

The NVIDIA Inception startup is building a new kind of farming system that’s powered by clean energy and continuously enriched by data. Aigen’s fleet of solar-driven rovers uses advanced computer vision to identify and remove weeds, dramatically reducing the need for herbicides.

Farming has no standard environment. Every field is different — different crops, different soil, different equipment, weeds, growth stages and geographies. That fragmentation makes real-world data collection slow, expensive and inconsistent. By post-training NVIDIA Cosmos open world foundation models on their specialized data and harnessing NVIDIA Isaac Sim pipelines, Aigen is building the system that generalizes for millions of agriculture scenarios.

On the ground, each rover runs inference using an NVIDIA Jetson Orin edge AI module to distinguish crops from weeds in real time.

Using these rovers, farmers can grow crops more sustainably and profitably, using regenerative practices that heal the land and foster ecological balance.

A timeless story of grit, faith and rebellion takes center stage as Samson: A Tyndalston Story joins the GeForce NOW library today. 

The highly anticipated release from Liquid Swords can now be streamed on nearly any device with GeForce NOW bringing cinematic intensity and mythic storytelling to the cloud.

Catch it as part of four new games in the cloud this week.

Stream the Power

Tyndalston is a city built on debt, muscle and memory. Samson: A Tyndalston Story from Liquid Swords follows Samson, a former enforcer pulled back to the streets that made him. Violence is currency as every fight is personal, every hit carries history and every escape feels earned in a city that never forgives.

Gameplay blends cinematic melee action with choice-driven narrative progression. Every confrontation — from shadowed alley brawls to large-scale set pieces — feels purposeful, reflecting Samson’s internal struggle between vengeance and redemption. Brawls hit fast and close. Cars aren’t set pieces — they’re weapons. Momentum and terrain decide if the player walks away or falls harder. Every job, debt and decision cuts toward freedom or collapse.

The game takes full advantage of ray-traced global illumination, reflections and shadows, creating a city that feels cinematic and alive. NVIDIA DLSS 3.5 boosts performance, while NVIDIA Reflex technology cuts down latency to keep controls razor-sharp during split-second fights. With GeForce NOW, the experience streams instantly at maximum fidelity, even without the latest hardware. No waiting around for downloads or worrying about system specs, just dive straight into the grit and glow of Tyndalston.

Celebrate New Games

Celebrate three decades of Rayman with the definitive edition of the platforming classic in Rayman 30th Anniversary Edition, featuring five versions from iconic consoles, over 120 additional levels and an exclusive documentary that explores the creation of the limbless hero. Stream it on GeForce NOW without having to wait around for downloads or updates. 

In addition, members can look for the following:

• Samson (New release on Steam, April 8, GeForce RTX 5080-ready)
• Morbid Metal (New release on Steam, April 8, GeForce RTX 5080-ready)
• DayZ (New release on Xbox, available on Game Pass, April 9) 
• Rayman: 30th Anniversary Edition (Steam and Ubisoft)

GeForce RTX 5080-ready game this week, in addition to Samson and Morbid Metal:

• Starfield (Steam and Xbox, available on Game Pass)

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

Who's the most iconic animal from a video game? Drop a pic or gif! (this post isn't just to farm cute animal pics, no idea what you're talking about).

— NVIDIA GeForce NOW (@NVIDIAGFN) April 7, 2026

Open models are driving a new wave of on-device AI, extending innovation beyond the cloud to everyday devices. As these models advance, their value increasingly depends on access to local, real-time context that can turn meaningful insights into action. 

Designed for this shift, Google’s latest additions to the Gemma 4 family introduce a class of small, fast and omni-capable models built for efficient local execution across a wide range of devices.  

Google and NVIDIA have collaborated to optimize Gemma 4 for NVIDIA GPUs, enabling efficient performance across a range of systems — from data center deployments to NVIDIA RTX-powered PCs and workstations, the NVIDIA DGX Spark personal AI supercomputer and NVIDIA Jetson Orin Nano edge AI modules.

Gemma 4: Compact Models Optimized for NVIDIA GPUs 

The latest additions to the Gemma 4 family of open models— spanning E2B, E4B, 26B and 31B variants — are designed for efficient deployment from edge devices to high-performance GPUs.  

This new generation of compact models supports a range of tasks, including: 

• Reasoning: Strong performance on complex problem-solving tasks.  
• Coding: Code generation and debugging for developer workflows.   
• Agents: Native support for structured tool use (function calling).  
• Vision, Video and Audio Capabilities: Enables rich multimodal interactions for object recognition, automated speech recognition, and document or video intelligence. 
• Interleaved Multimodal Input: Mix text and images in any order within a single prompt.  
• Multilingual: Out-of-the-box support for 35+ languages, pretrained on 140+ languages. 

The E2B and E4B models are built for ultraefficient, low-latency inference at the edge, running completely offline with near-zero latency across many devices including Jetson Nano modules. 

The 26B and 31B modelsare designed for high-performance reasoning and developer-centric workflows, making them well suited for agentic AI. Optimized to deliver state-of-the-art, accessible reasoning, these models run efficiently on NVIDIA RTX GPUs and DGX Spark — powering development environments, coding assistants and agent-driven workflows.  

As local agentic AI continues to gain momentum, applications like OpenClaw are enabling always-on AI assistants on RTX PCs, workstations and DGX Spark. The latest Gemma 4 models are compatible with OpenClaw, allowing users to build capable local agents that draw context from personal files, applications and workflows to automate tasks. Learn how to run OpenClaw for free on RTX GPUs and DGX Spark or using the DGX Spark OpenClaw playbook. 

Check out the Google DeepMind announcement blog to learn more about the latest additions to Gemma 4 family.

Getting Started: Gemma 4 on RTX GPUs and DGX Spark 

NVIDIA has collaborated with Ollama and llama.cpp to provide the best local deployment experience for each of the Gemma 4 models.    

To use Gemma 4 locally, users can download Ollama to run Gemma 4 models or install llama.cpp and pair it with the Gemma 4 GGUF Hugging Face checkpoint. Additionally, Unsloth provides day-one support with optimized and quantized models for efficient local fine-tuning and deployment via Unsloth Studio. Start running and fine-tuning Gemma 4 in Unsloth Studio today. 

Running open models like the Gemma 4 family on NVIDIA GPUs achieves optimal performance because NVIDIA Tensor Cores accelerate AI inference workloads to deliver higher throughput and lower latency for local execution. Plus, the CUDA software stack ensures broad compatibility across leading frameworks and tools, enabling new models to run efficiently from day one.  

This combination allows open models like Gemma 4 to scale across a wide range of systems — from Jetson Orin Nano at the edge to RTX PCs, workstations and DGX Spark — without requiring extensive optimization. 

Check out the NVIDIA technical blog for more details on how to get started with Gemma 4 on NVIDIA GPUs and learn more about NVIDIA’s work on open models. 

#ICYMI: The Latest Updates for RTX AI PCs 

 Catch up on RTX AI Garage blogs for a host of agentic AI announcements from NVIDIA GTC, such as new open models for local agents. These models include NVIDIA Nemotron 3 Nano 4B and Nemotron 3 Super 120B, and optimizations for Qwen 3.5 and Mistral Small 4. 

 NVIDIA recently introduced NVIDIA NemoClaw, an open source stack that optimizes OpenClaw experiences on NVIDIA devices by increasing security and supporting local models.  

 Accomplish.ai announced Accomplish FREE, a no-cost version of its open source desktop AI agent with built-in models. It harnesses NVIDIA GPUs to run open weight models locally, while a hybrid router dynamically balances workloads between local RTX hardware and the cloud — enabling fast, private, zero-configuration execution without requiring an application programming interface key. 

Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. 

Follow NVIDIA Workstation on LinkedIn and X.  

No joke — GFN Thursday is skipping the tricks and heading straight into the games. April kicks off with ten new titles, bringing fresh adventures to GeForce NOW, including the launch of Capcom’s highly anticipated PRAGMATA.

A dozen new games are available to stream this week, including Arknights: Endfield, which expands the acclaimed series into a full 3D real‑time strategy adventure. On GeForce NOW, every battle flows with precision and every mission looks sharper than ever.

So gear up, grab a controller or gaming device of choice, and get ready to stream — another month of great gaming is now underway.

Command the Frontier

Arknights: Endfield from Hypergryph expands the acclaimed Arknights universe into a full, 3D, real‑time strategy role-playing game. Blending tactical planning with sleek sci‑fi aesthetics, the title invites players into a world featuring terraformed settlements, advanced technology and looming threats beneath the planet’s surface.

Set on the perilous planet Talos‑II, Endfield follows a group of pioneers uncovering lost secrets and battling hostile factions. The game seamlessly merges base‑building, exploration and combat — with squads of operators coordinating in real time to overcome environmental hazards and powerful enemies. Every decision impacts survival, progress and the unfolding mystery of the world.

On GeForce NOW, Arknights: Endfield can be played at the highest settings from virtually any device, enabling crisp visuals and high performance without compromise. GeForce RTX rendering brings the game’s metallic skylines and glowing wastelands to life, while ultralow-latency streaming ensures every tactical command lands with precision. 

Spring Into April

Capcom’s Mega Man Star Force Legacy Collection includes seven games and additional features, including a gallery of illustrations and music. Eleven‑year‑old Geo Stelar is a grieving boy who isolates himself after the mysterious disappearance of his astronaut father. His life changes when he encounters an extraterrestrial being named Omega‑Xis, granting him the power to become Mega Man. The collection streams instantly with GeForce NOW, turning any device into a Star Force terminal ready to save the world once more.

Check out what else is available this week:

• Hozy (New release on Steam, March 30)
• Cooking Simulator 2: Better Together (New release on Steam, March 31)
• Legacy of Kain: Ascendance (New release on Steam, March 31)
• Subliminal (New release on Steam, March 31)
• Super Meat Boy 3D (New release on Steam, March 31)
• I Am Jesus Christ (New release on Steam, April 2)
• ALL WILL FALL (New release on Steam, April 3, GeForce RTX 5080-ready)
• Arknights: Endfield (Official Site)
• Mega Man Star Force Legacy Collection (Steam)
• Nova Roma (Steam and Xbox, available on Game Pass)
• RuneScape: Dragonwilds (Steam)
• Way of the Hunter 2 (Steam, GeForce RTX 5080-ready)

And look forward to the games coming throughout the month:

• Vampire Crawlers: The Turbo Wildcard from Vampire Survivors (New release on Steam, April 21)
• Samson (New release on Steam, April 8)
• Replaced (New release on Steam and Xbox, available on Game Pass, April 14)
• Cthulhu: The Cosmic Abyss (New release on Steam, April 16)
• PRAGMATA (New release on Steam, April 17)
• Outbound (New release on Steam, April 23)
• Heroes of Might and Magic: Olden Era (New release on Steam, April 30)
• Bus Bound (New release on Steam, April 30)

More of March

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

• 1348 Ex Voto (Steam, GeForce RTX 5080-ready)
• BATTLETECH (Xbox, available on Game Pass)
• Cooking Simulator 2: Better Together (Steam)
• Despot’s Game (Xbox, available on Microsoft)
• Diablo II: Resurrected (Steam)
• Hozy (Steam)
• King’s Quest (Ubisoft)
• Monster Hunter Stories 3: Twisted Reflection (Steam, GeForce RTX 5080-ready)
• Super Meat Boy 3D (Steam)
• Warcraft I: Remastered (Ubisoft)
• Warcraft II: Remastered (Ubisoft)
• Way of the Hunter 2 (Steam)

What are you planning to play this weekend? Check out Crimson Desert on GeForce NOW in Anytime Anywhere Gaming’s YouTube review.