While science fiction lore tells us that humans will meet extraterrestrial life in the form of little green men, the detail that should raise hairs this Halloween is this: the search for extraterrestrial intelligence took a major leap forward with AI, and it increasingly focuses on AI agents built by humans listening for AI agents built by aliens.

Some scientists have suggested that extraterrestrial civilizations, if they exist, could be billions of years older than humanity. Such civilizations might have developed technologies far beyond humanity’s, including forms of AI.

Breakthrough Listen and the SETI Institute are bringing agentic AI into the daily search for extraterrestrial intelligence. At the Allen Telescope Array in Northern California, agents can now steer the instrument, enhance and correct incoming data, and apply AI models to the incoming stream — all from a single natural language request. The NVIDIA Holoscan platform processes the massive streams of sensor data, giving agents live data to act on.

The result is a faster, more adaptable search for potential signals from extraterrestrial life and technologies.

“As our new agentic observatories reach out into the universe, what they find may have much more in common with themselves than with the humans who created them,” said Andrew Siemion, Bernard M. Oliver chair for SETI at the SETI Institute and principal investigator of Breakthrough Listen. “But this isn’t something to fear. Rather, it’s a breakthrough in translation.”

Terabits a Second, Processed in Flight

Radio telescopes generate enormous volumes of data, creating a challenge for scientists who need to identify potentially interesting signals as observations unfold.

At the Allen Telescope Array, DAQIRI, a Holoscan platform library, enables the telescope to pull terabits per second of sensor data straight into GPU memory so observations are processed in flight rather than filling hard drives. CyberEther, a signal-processing framework developed at the SETI Institute, does the math using NVIDIA CUDA-X libraries including cuFFT and cuBLAS.

Nexus, a web-based observatory orchestrator, acts as the agent harness. It gives agents a single source of truth for instrument state, compute resources and pipelines and lets researchers monitor packet loss, read live spectrograms and rewrite signal-processing blocks — all by prompting in natural language. The system can handle 2.4 terabits per second on NVIDIA RTX PRO 6000 Blackwell GPUs, with inference running on premises, next to the instrument.

Put together, the system enables an agent to steer a telescope’s antennae, calibrate and focus the instrument for a clean signal, catch and fix data errors and plot the result. Agents compress what would take humans weeks or years into hours.

Science Fact, Not Fiction

The pipeline has already proven itself, achieving the first real-time AI detection of a pulsar — a rapidly spinning stellar remnant that pulses like a cosmic lighthouse — in the Crab Nebula. 

Next, the team is evaluating open models including NVIDIA Nemotron for cases where data locality and turnaround matter most and for observatories that may someday run offline on the far side of the moon.

Learn more about NVIDIA AI for science.

Turning a simulation idea into a working application means assembling assets, connecting physics and rendering, and checking that the scene behaves as intended. Developers are combining frontier AI models with NVIDIA Omniverse libraries to help carry out that work — building applications for exploring scenarios, investigating failures and improving designs.

Developers direct AI agents through natural-language instructions, review results and guide changes. Omniverse libraries provide GPU-accelerated physics, rendering and sensor simulation capabilities.

Explore the projects below to see frontier AI models such as GPT-6 Astra at work, and check back for new examples from NVIDIA teams and developers across the ecosystem.

Create an Editable Building Model From Site Scans

Planning warehouse automation requires a model of the building itself, but shelving and machinery can obscure its structure in site scans. Omniverse brings together real-to-sim tools and agent skills to help developers turn real-world captures into editable starting environments for physical AI simulation.

David Wehr, from NVIDIA’s simulation technology team, built on that work using Astra to develop a workflow for digitally reconstructing a building without its contents. The goal is an editable, simulation-ready building model where developers can add or reposition shelving and machinery to evaluate warehouse layouts and automation plans before making physical changes.

Wehr guided Astra to interpret panoramic photos, create building geometry and align it with point-cloud measurements. OpenUSD stores the models, while Omniverse’s ovrtx library renders results for inspection.

Explore NVIDIA Omniverse agent skills to help agents build with Omniverse libraries.

Test Gas-Turbine Operating Scenarios With a Digital Twin

Before changing how industrial equipment operates, engineers need a way to explore the consequences. Pradeep Kaushik, a member of the NVIDIA Omniverse developer relations team, directed Astra to build a what-if analysis application around a simulated gas turbine and generator.

The application lets users adjust conditions such as load, ambient temperature and fuel composition, then compare power, efficiency, fuel consumption and equipment response. Six test procedures explore scenarios including load changes, a stuck fuel valve and an exhaust-sensor error.

The prototype gives engineers an interactive way to investigate how operating changes and faults could affect equipment behavior, supporting decisions about which scenarios warrant further testing.

Astra used tools like Cantera for thermochemistry and SciPy for equipment dynamics. The application maps solver results to rotor animation and color-coded temperature, pressure and flow-velocity indicators in a 3D turbine. NVIDIA Omniverse libraries update the scene (ovstage), render it with RTX (ovrtx) and stream the view to the browser (ovstream). Engineers can rotate and zoom the cutaway, switch between indicators, and pause or scrub through a simulated response alongside charts and virtual instruments.

Build Simulation Scenes With Open Source SimReady Assets

With SimReady Central Library, announced at GTC Berlin, developers can access more than 2,000 simulation-ready USD assets from equipment manufacturers and asset providers for robotics simulations and AI factory digital twins workflows.

Paul Kind, a member of NVIDIA’s Omniverse documentation team, pointed an AI agent to the library and guided scene assembly through conversation. His kitchen scene included 180 placements drawn from 63 unique library assets, alongside 18 custom geometry pieces. Kind reviewed renders produced with Isaac Sim and guided further adjustments.

For robotics and factory workflows, developers can select SimReady assets with the simulation properties embedded to fit their required task then test in an assembled environment.

Find assets in SimReady Central Library, and learn how to contribute.

Stress-Test a Warehouse Simulation at Scale

A warehouse simulation needs to keep running smoothly as more robot arms, conveyors and boxes fill the scene.

When it slows down, developers need to identify what’s holding it back. Bhavik Sanganee, principal engineer at NVIDIA, used Astra with NVIDIA Omniverse libraries to build a warehouse simulation, increase its complexity and investigate slowdowns.

Performance first fell below the target when the scene doubled in size. Running physics calculations, image rendering and video preparation concurrently restored performance and enabled further scaling.

With 80 conveyors, 40 arms and 800 boxes, the simulation sustained 30 frames per second at full HD resolution during a two-minute test. The arms completed 640 successful box placements.

Doubling the scene again reduced performance. Testing traced the slowdown to the physics and robot-control pipeline, giving Sanganee a specific area to improve.

Start building with NVIDIA Omniverse libraries.

Test Robot Control for Flexible Cable Routing

In electronics assembly, a robot may need to position a flexible cable around components before plugging it into a port. The cable bends as the robot moves it, and the connector must line up precisely to fit.

Oyindamola Omotuyi, robotics technical marketing manager at NVIDIA, directed Astra to build a robot simulation using NVIDIA Isaac Lab, Newton physics and Omniverse rendering with a Robotiq gripper handling the cable.

Astra also developed a reinforcement learning system that combined learned cable-routing movements with position-and-orientation feedback and corrective adjustments for USB-C insertion. The simulation gave Omotuyi a way to test cable handling and connector alignment before moving to physical-robot experiments.

The controller used information supplied directly by the simulation. Next steps include testing a friction-held grasp and using information a physical robot could obtain from cameras and sensors.

Get started with robot learning in NVIDIA Isaac Lab.

Turn Robot Computer-Aided Design Into a SimReady Asset

Before testing whether a robot can pick up and move a box, developers need a simulation model with joints that move correctly and with grippers that can hold the object. A computer-aided design (CAD) model provides the robot’s shape, but it still needs physics properties to perform the task.

Using an ABB Robotics YuMi robot, developers directed Astra with NVIDIA Omniverse content libraries to convert CAD files into OpenUSD, match the robot’s appearance to reference images, and configure joints and collision geometry. They checked the asset against SimReady Foundation requirements before testing it in NVIDIA Isaac Sim.

Both arms and grippers completed four cube pick-and-place cycles, using simulated finger contact and friction. These tests demonstrated behavior under the simulated conditions — estimated physical properties were not calibrated against the real robot.

Follow the five-step SimReady tutorial. 

Build a Humanoid Simulator for a Warehouse Environment

Explore a gamified, physics-based control of a humanoid robot in first- and third-person view.

Before automating warehouse tasks, developers need an interactive simulation environment to explore task behavior and evaluate how the work gets done. Frank DeLise, Omniverse product manager at NVIDIA, used Astra to turn a SimReady warehouse and humanoid robot into an interactive simulator with first- and third-person views.

DeLise directed Astra to connect NVIDIA Omniverse libraries for physics (ovphysx), scene updates (ovstage), rendering (ovrtx) and the user interface (ovui). He also used Astra with SimReady (simready-foundation) to create the physical scene in simulation. Astra then generated animation and application code to bring those capabilities together.

Learn how to prepare and validate SimReady robot assets with frontier AI models and NVIDIA Omniverse libraries.

Connect an Autonomous-Driving Testing Workflow

Changing a scene, sensor or driving model can significantly affect autonomous-vehicle simulations. Doyub Kim, a manager on the simulation technology team at NVIDIA, asked Astra to build Zero to Alpamayo — a reusable simulation environment based on San Francisco’s Market Street.

Kim directed Astra to map out the workflow, then connect asset creation, traffic, Omniverse RTX sensor simulation and Alpamayo driving in stages, checking each integration. The resulting prototype was a testing ground for comparing models and tracing how scene or sensor changes affect downstream driving behavior. A separate Cosmos3-Nano experiment varied weather and lighting in recorded simulation videos, allowing Kim to compare the driving model’s responses to the same scenario under different conditions.

Use Sensor Differences to Create and Improve Digital Twins

Comparing recorded and simulated camera and lidar outputs gives developers key performance indicator-based feedback for refining a scene.

To test robots and autonomous vehicles, developers need to know how closely simulated sensors match real ones. Ashley Reid, who works on RTX sensor validation at NVIDIA, directed Astra and Claude Fable 5 agents to compare ovrtx camera and raw LiDAR outputs with recorded data. The agents created two digital twins from scratch and improved two existing ones.

Over about three days, Reid guided an iterative workflow in which agents measured differences, created or modified OpenUSD scenes, and checked the results. Changes addressed missing objects, geometry and materials, with acceptance depending on camera and LiDAR metrics. This gives developers a way to use measured discrepancies to guide scene creation and improvement.

Start by rendering an OpenUSD scene with the ovrtx minimal Python example, then define a sensor measure to compare with recorded data.

Robo Olympics: Test Robot Skills With Simulation

Teaching robots new movements means checking whether those actions work under physical constraints. Tae Kim, who leads NVIDIA Omniverse engineering and product, used sports videos and natural-language instructions to guide Astra in building Robo Olympics, an experimental project that tests simulated Unitree G1 humanoids performing sports movements.

Under Kim’s direction, Astra built controllers and refined them through physics trials. Newton Physics Engine simulated behavior, the open source NVIDIA Warp framework accelerated calculations and ovrtx rendered scenes and virtual-camera images. In one experiment, the robot cleared a single hurdle in 64 of 100 simulation trials. The trials gave Kim feedback for improving the robot’s timing and control.

Watch Kim explore building simulations with Astra and NVIDIA Omniverse libraries.

Test Robotic Disassembly With Computer-Aided Design and Simulation

Before a robot can disassemble a product, developers need to know whether its tools can reach and remove the parts. Jens Jebens, a senior product manager for OpenUSD at NVIDIA, directed Astra to model a car suspension in PTC Onshape and configure it in NVIDIA Isaac Sim.

With Astra, Jebens explored computer-aided design and tooling revisions for robots informed by simulation. The agent measured the available space and designed a wrench the robot could use to reach the suspension’s bolts. Jebens reported successful removal of a suspension component in simulation. This connects design and tooling decisions to disassembly results, offering a starting point for robot policy training.

Explore the Onshape importer guide.

Bring the International Space Station Into the Browser

Turning 3D models into an application requires connecting assets, live data and an interface. Nic Johns, an engineering director at NVIDIA, prompted Astra to assemble NASA assets into an OpenUSD International Space Station model with telemetry. Johns built the application with a single prompt, then used a follow-up prompt to shift the scene to Earth’s daytime side so the planet was visible.

The workflow used Blender for asset preparation and Omniverse libraries for rendering (ovrtx), scene runtime (ovstage) and streaming (ovstream). The application brings 3D models and operational data into the browser, with Johns guiding its development through prompts and corrections.

Try the Omniverse Real-Time Viewer skill.

Turn Captured Rooms Into Testing Environments

A digitally reconstructed room needs editable objects and accurate physical behavior before developers can test interactions. Chirag Majithia, from the Isaac engineering applications team at NVIDIA, directed Astra to turn stereo camera captures into an editable OpenUSD studio.

The workflow combined PyCuSFM, FoundationStereo and nvblox for reconstruction, with user review guiding object selection and placement. Astra assembled generated and Blender-authored assets and used USD Content Agents to configure how objects move and interact in simulation. Isaac Sim tests guided collision and contact revisions for doors and drawers. The studio connects captured geometry to interaction testing, making gaps and object behavior easier to inspect. 

Explore USD Content Agents.

Have a simulation idea? Explore NVIDIA Omniverse libraries to start building with an AI agent.

This Halloween week, GFN Thursday is delivering a fresh batch of gaming treats.

Something wicked this blade comes. S-GAME’s Phantom Blade Zero launches on GeForce NOW today, bringing a scarily good blend of martial arts and punk fantasy to the cloud.

There’s also something on the horizon for adventurers: World of Warcraft: Forever arrives Wednesday, Nov. 4 at 3 p.m. PST, with a launch-week access reward coming for GeForce NOW members.

This week’s lineup also includes nine games streaming from the GeForce NOW library, alongside new mod support for Cyberpunk 2077 and The Witcher 3: Wild Hunt — Remastered. That means more options to play, with no downloads or installs to haunt gamers’ devices.

There’s another sweet update for gamers in India: The option to sign up for four-month Performance and Ultimate memberships is launching tomorrow. Visit the GeForce NOW website for more information.

Cold Steel Meets Cloud Power

Framed for his master’s murder and gravely wounded, a warrior named Soul has 66 days to uncover a conspiracy threatening the martial world in S-GAME’s Phantom Blade Zero. Powered by Unreal Engine 5, the game combines wuxia storytelling with cinematic action.

Master more than 30 primary weapons and over 20 Phantom Edges, chaining evasions, deflections and counterattacks into fluid combat. Traditional Chinese martial arts meet mechanical prosthetics, industrial ruins and rock music in a distinctive kungfupunk world.

Ultimate members can wield GeForce RTX 5080-class performance in the cloud across PCs, Macs, handhelds, mobile devices, browsers, compatible TVs and more. GeForce NOW handles the game’s downloads and storage, leaving more time for the next clash. Soul’s clock is ticking, but the download bar doesn’t have to. Cut to the chase and hop into the game today.

Azeroth Awaits

Get ready for Blizzard Entertainment’s World of Warcraft: Forever, arriving Wednesday, Nov. 4, at 3 p.m. PT, on GeForce NOW. Explore an iconic world reimagined with new and expanded zones, new gameplay enhancements, upgraded graphics and more.

A new reward will give eligible GeForce NOW members access to join beginning at launch and ending Wednesday, Nov. 11, at 11:59 p.m. PT. The reward is available across all GeForce NOW membership tiers for new WoW users who don’t already have a subscription, while supplies last, on a first-come, first-served basis.

Once available, members can sign in to their GeForce NOW account and head to the Account Portal to redeem. Mark the calendar and gather the party — launch week is approaching.

New Tricks and Treats

Familiar favorites have a few new tricks, too: Mod.io support is now available directly in game for Cyberpunk 2077 and The Witcher 3: Wild Hunt – Remastered, bringing more ways to make Night City and the Continent feel different.

And the treats keep coming with the following games joining the GeForce NOW library this week:

• Phantom Blade Zero (New release on Steam, Oct. 28)
• ATMOSFAR (New release on Steam, Oct. 29)
• Hunting Simulator 3 (New release on Steam, Oct. 29)
• Queen’s Domain (New release on Steam, Oct. 30)
• Dimraeth (Steam)
• Dressmaker (Steam)
• Halloween (Epic Games Store)
• Hell Let Loose: Vietnam (Epic Games Store)
• The Seance of Blake Manor (Steam)

Dates listed above reflect when games are released on their respective stores. GeForce NOW availability may vary, as games are onboarded after they’re released and added throughout the week. Keep an eye on GeForce NOW channels and GFN Thursdays for availability updates to announced titles.

Settle in for a scary-good gaming session: Grab a day pass to experience Performance or Ultimate cloud gaming. Gamers can then apply the cost applicable toward a first monthly membership upgrade.

What’s haunting the gaming playlist this weekend? Let us know on X or in the comments below.

NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do.

RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI. These powerful systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance. That puts the NVIDIA CUDA platform, RTX graphics and the capabilities of a personal AI supercomputer inside premium Windows laptops built to go anywhere.

New and expected RTX Spark updates, coming soon, include:

• NVIDIA Nemotron 3.5 Transcribe, an open weight speech model for transcription, speaker attribution and translation is available on RTX Spark. 
• The LFM3 family of open models with day-zero support. 
• Resource management controls in NVIDIA App to allocate GPU resources between foreground apps and background agents.
• Phantom Blade Zero, S-GAME’s highly-anticipated action role-playing game, launches with ray tracing and NVIDIA DLSS 4.5.
• Experience Minecraft with path tracing and DLSS with RTX Remix1.6, available now for RTX Spark.

Turn Recorded Audio Into Useful Information With NVIDIA Nemotron 3.5 Transcribe

Introduced today, NVIDIA Nemotron 3.5 Transcribe is a new, open weight speech model that turns recorded conversations into useful information. Instead of needing multiple models for transcription, speaker labels and translation, developers can use one promptable model that handles all three, plus questions about the audio. It transcribes 40 languages, including seven Indic languages.

Developers guide the model with plain-language prompts. For example, for a single recorded meeting, users can ask it to:

• “Transcribe this recording and label each speaker.”
• “Translate this audio into English.”
• “List what decisions were made in this meeting.”

Prompts can also include product terms and formatting conventions, which helps the model handle specialized vocabulary in fields like healthcare, financial services and customer service. Because the weights are open, developers can choose where audio is processed, from a desktop to the cloud.

Nemotron 3.5 Transcribe ranks No. 1 on the Hugging Face Open ASR and No. 2 among open weight models on Artificial Analysis — delivering leading accuracy with approximately 4.3 billion active parameters, 80% fewer than the No. 1 open weight model, making high-quality, intelligent transcription more practical on local hardware.

It’s available today on Hugging Face. Quantized versions are coming soon, so  developers can run Transcribe locally onNVIDIA RTX Spark and DGX Spark systems, Jetson AGX Thor devices or scale up on NVIDIA Blackwell data center GPUs. NVFP4 versions will be deployable through NeMo-Speech.cpp or vLLM.

Learn more in the NVIDIA technical blog.

Control How Agents and PC Apps Share GPU Resources

On RTX Spark, users can have long-running agents working alongside their favorite applications. Both share the system’s GPU compute, and the right balance of resources depends on what matters to users at that moment.

With the new resource management feature in NVIDIA App, users can now choose how GPU resources are shared. They can prioritize their foreground app for the best performance, or give their background-running agent more resources to get work done faster. 

Access control resource sharing from System > Advanced in the NVIDIA App. This feature requires hardware-accelerated GPU scheduling to be enabled in Windows Settings.

Liquid AI: One Model Family, Many Ways to Work

Liquid AI’s LFM3 provides a new generation of Liquid Foundation Models designed for specialized tasks. The family spans text, vision, audio, omni and decision models built on one shared backbone, with base and post-trained models available as open weights. The models are compact and fast: Liquid AI’s new 1.7-billion-parameter base model outperforms its previous 2.6-billion-parameter base model.

These models support local workflows such as agents running in Hermes Agent, Pi and OpenClaw to triage inboxes and manage calendars, read documents and app screens, transcribe and translate speech, answer spoken questions about handwritten math, and route support tickets or voice requests. 

Developers can run LFM3 models on RTX Spark with native support through llama.cpp and vLLM, and fine-tune the models locally.

‘Phantom Blade Zero’ Launches With DLSS 4.5

S-GAME’s Phantom Blade Zero launches tonight, Oct. 28, at 7 p.m. PT on Steam and the Epic Games Store. Players can take on the role of Soul, a swordsman framed for his master’s murder, as he travels the martial world to uncover a conspiracy.

NVIDIA worked with S-GAME Studio and S-GAME Publishing to add ray-traced effects to the title. On RTX Spark laptops and GeForce RTX GPU-powered PCs, ray tracing adds more accurate bounced lighting, detailed reflections and realistic shadows on foliage and terrain. The NVIDIA DLSS Dynamic Multi Frame Generation feature boosts frame rates with up to 6x frame generation, or adjusts the multiplier on the fly to hit a player’s target frame rate.

Read more on GeForce.com.

RTX Remix Breathes New Life Into Classics 

NVIDIA RTX Remix, the modding platform for remastering classic games with path tracing and DLSS, released version 1.6 last week. This update brought RTX Remix to the world’s largest gaming community: Minecraft. Both creators and players can render Minecraft Java Edition 1.21.1 through RTX Remix with DLSS-accelerated path tracing and take full advantage of realistic lighting and materials in their future creations.

The update also adds DLSS 5 with 3D-Guided Neural Rendering, up to 6x Multi Frame Generation, native RTX Spark support and agent-assisted workflows. Starting today, those upgrades come to Portal with RTX and the Half-Life 2 RTX demo. Both now support RTX Spark, with DLSS 3D-Guided Neural Rendering for more lifelike lighting and materials, a new Performance Mode for DLSS Frame Generation and new options that speed up path tracing. 

RTX Remix 1.6 also makes AI-powered modding simpler. A redesigned ComfyUI workflow lets modders generate physically based materials directly inside RTX Remix, with no application programming interface setup, batch processing for full projects and presets for less technical creators. Plus, with NVIDIA agent skills, compatible AI agents can work directly with RTX Remix to help speed up remastering projects.

Learn more in the RTX Remix blog. 

RTX Spark Laptops: Beautifully Designed, Built for Everything

RTX Spark laptops come in a range of designs, so users can choose one that fits how they work and play. Options include different screen sizes, colors, touchscreens, pen support and two-in-one convertibles. RTX Spark laptops are as thin as 14mm and as light as three pounds, with all-day battery life.

Such slim designs are possible because RTX Spark places the GPU, CPU and up to 128GB of unified memory on one power-efficient superchip. Creative projects and AI models draw from the same memory pool, so a laptop can take on work that used to call for a desktop workstation, even when unplugged. 

Here’s what this means for:

• AI developers: Run models and agents locally on NVIDIA CUDA — the same platform that powers AI in the cloud — with a path to deploy across more than 150 million NVIDIA RTX AI PCs and NVIDIA GPUs in the cloud.
• Creators: RTX Spark can edit camera native 4:2:2 video and render 3D scenes of up to 90GB. Adobe Photoshop and Premiere offer native RTX Spark support through beta releases.
• Gamers: Hundreds of games run on RTX Spark, with NVIDIA DLSS, ray tracing, Reflex and G-SYNC delivering sharper visuals and smoother, more responsive gameplay.

RTX Spark laptops are available now from ASUS, Dell, HP, Lenovo, Microsoft and MSI, starting at $2,599, with 128GB configurations on shelves. Find a laptop on the RTX Spark product page.

#ICYMI: More Updates From NVIDIA Local AI

Microducks learn to walk with help from RTX Spark: During the GTC Berlin keynote, NVIDIA founder and CEO Jensen Huang showed how AI agents running on RTX Spark can help train Hugging Face’s Microduck robot. A new NVIDIA technical blog walks through the workflow: Hermes Agent and Nemotron 3.5 Bolt plan an obstacle course on RTX Spark, Astra builds the 3D assets, NVIDIA Isaac Lab trains the navigation policy and RTX Spark validates it in NVIDIA Isaac Sim. Watch the demo and try the playbook.

NVIDIA Nemotron 3.5 Bolt E4B 4GB is now available, enabling natural conversations and multistep agent tasks with as little as 4GB of memory. The model powers local voice assistants, game characters and embedded interfaces on RTX Spark, DGX Spark, GeForce RTX GPUs and NVIDIA Jetson devices. It supports llama.cpp, vLLM, Ollama, Unsloth and LM Studio, and works with Hermes and OpenClaw. For more demanding agent workloads, Nemotron 3.5 Bolt 74B-A9B 24GB is designed to run local agents on RTX Spark.

Black Forest Labs’ FLUX 3 Image Klein is a 5.6-billion-parameter open weights model that lets users generate and edit images locally. NVIDIA-optimized versions run on RTX GPUs with as little as 8GB of VRAM. Developers can also fine-tune the model on RTX Spark using an NVIDIA NeMo AutoModel recipe to customize it for specific creative workflows.

NVIDIA DGX Spark 64GB is now available to purchase from manufacturer partners, giving developers and researchers another way to build and run AI locally. Powered by the GB10 Grace Blackwell Superchip, it combines 64GB of unified memory with DGX OS and the NVIDIA AI software stack for agent development, inference and fine-tuning. Learn more and explore purchase options.

Follow NVIDIA RTX Spark on X, Instagram, TikTok and Facebook — and stay informed by subscribing to the NVIDIA Local AI newsletter. Follow NVIDIA Workstation on LinkedIn and X. 

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There are 57 million people worldwide living with dementia today. For the researchers working to help them, the obstacle isn’t a lack of effort — it’s too much data, too few experts to synthesize it and a pace of discovery that can’t keep up with the disease. 

The Consortium for Biomedical Research and AI in Neurodegeneration (C-BRAIN), working with NVIDIA, is using AI to change that.

Alzheimer’s disease is one of today’s leading global health challenges, with over 10 million new dementia cases diagnosed each year. Despite a 7x increase in National Institutes of Health funding in the last 15 years — reaching $3.9 billion annually — no treatment has been found to stop, prevent or reverse the disease yet. 

The bottlenecks are not enough high impact scientific findings, too much data living in institutional silos and too slow a pace of synthesis to keep up with the research that is published. 

C-BRAIN, led by Dr. Randall Bateman and a consortium of 18 member institutions across academic, philanthropic, pharma and research organizations, launched in November 2025 to help scientists make better, faster discoveries by accelerating the research that makes new treatments possible. 

“We have a window of opportunity with artificial intelligence to do research fundamentally different than we’ve ever done before, in a way that accelerates discoveries to help patients,” said Bateman, founder and director of C-BRAIN and the Charles F. and Joanne Knight Distinguished Professor of Neurology at the Washington University School of Medicine in St. Louis. He described their work as “AI tools built by scientists, for scientists.”

The consortium is building a system that harnesses previously untapped Alzheimer’s research data to support scientists’ work.

Speeding Research From Weeks to Hours

Essential assets in Alzheimer’s research are datasets that have never been publicly accessible to AI tools or human researchers. This includes unpublished findings, negative results and governed proprietary data from member institutions. 

“A lot of life sciences data doesn’t make its way into the frontier models,” said Dr. Ade Ojewole, chief technology officer of C-BRAIN. “The consequence of that is we’re leaving a lot of intelligence on the table.” 

C-BRAIN refers to this as “dark data,” which slows down Alzheimer’s research.

Some dark data is directly deposited into secure computer systems for AI tools to access. To use this data without compromising patient privacy or institutional governance, C-BRAIN uses a hybrid centralized and federated approach — where AI models learn from data at each institution without that data ever having to leave its walls. This kind of infrastructure is technically demanding to build and scale, which is part of what NVIDIA brings to the collaboration.

C-BRAIN currently runs 64 NVIDIA data center GPUs on premises alongside cloud-based nodes on Microsoft Azure, with additional NVIDIA GPU capacity, including NVIDIA Blackwell systems, on the horizon as the consortium’s ambitions scale.

To explore how AI and accelerated computing can advance Alzheimer’s research while navigating its technical demands, C-BRAIN is working with NVIDIA to incorporate technologies including the NVIDIA BioNeMo open developer platform and the MONAI framework for C-BRAIN’s research pipelines.

“The C-BRAIN collaboration with NVIDIA aims to unlock those models in a way that they can deliver something quite impactful and important,” said Bateman. 

“A great deal of optimization is going to be needed as we push the boundaries of what today’s spatial omics machine learning architectures can model,” Ojewole said. “NVIDIA software is definitely going to come into play there.”

Researchers at C-BRAIN have already built two tools: OpenScientist — built with Lawrence Berkeley National Laboratory in an effort led by research scientist Justin Reese — and InsightEngine, built with 387 Labs. 

OpenScientist is an open source agentic AI co-scientist that takes a scientist’s research question and dataset, then semi-autonomously generates hypotheses, runs analyses and produces verifiable, auditable insights. InsightEngine is an open source research platform that uses agentic AI to test hypotheses against the scientific literature, analyzing user-supplied datasets, exploring a biomedical knowledge graph and checking every claim against evidence to catch hallucinations before researchers commit to costly experiments.

In published case studies, OpenScientist completed analyses in minutes that domain scientists estimated would take weeks to months, at a cost of under $10 per run. At C-BRAIN’s current reach of over 200 research groups, this capability could release substantial research capacity, potentially exceeding one million hours annually at scale.

From Correlation to Cause

Looking ahead, C-BRAIN aims to build a multimodal biological foundation model trained on the full breadth of its data — neuroimaging, multi-omics, fluid biomarkers and longitudinal clinical measures. 

By year three, it aims to discover new causal mechanisms of brain aging and Alzheimer’s disease, moving to testable scientific principles using the full scope of available research and data. 

Bateman said, “AI that can think and reason has the potential to accelerate discoveries that will lead to powerful and implementable preventions and treatments for the entire population.”

Kazakhstan’s AI ecosystem is moving from infrastructure plans to deployment. 

The shift is visible across the full AI stack — from infrastructure to models to applications.

The announcements below — coinciding with a visit to Astana by NVIDIA founder and CEO Jensen Huang — point to the nation’s increasing supply of accelerated computing, along with its growing demand for what comes next.

On Friday, Oct. 23, NVIDIA founder and CEO Jensen Huang visited Astana, meeting with President Kassym-Jomart Tokayev at the Akorda Presidential Palace and joining Deputy Prime Minister Zhaslan Madiyev — Kazakhstan’s minister of AI and digital development — for a public fireside chat at the Alem.AI program. 

Jensen Huang and Kazakhstan’s AI Minister Speak at Alem.AI

In their fireside chat at Alem.AI, Kazakhstan’s international center for artificial intelligence, Madiyev and Huang discussed the country’s push to become an AI nation — building the energy and computing infrastructure to support homegrown AI and applying it across the economy.

Madiyev said Kazakhstan wants AI that reflects its language and culture. “We want AI that understands and speaks Kazakh and captures cultural contexts and cultural specifics,” he said, describing the country’s work on Kazakh-language models. 

Huang said Kazakhstan could turn its energy resources into a foundation for AI production — and a new kind of export. “Instead of just exporting energy, in the future, you could transform energy into intelligence and export intelligence,” he said.

He also argued that local data is central to that ambition: it embodies a country’s culture, history and values. “Your country’s data is your natural resource. It belongs to you.” 

The conversation also turned to physical AI. Madiyev pointed to Kazakhstan’s factories, mines, agriculture, transport and logistics as potential areas for development, asking how the country could become a hub for training models for robots and other physical systems.

Huang called the idea “fantastic,” saying physical AI could transform industries from mining and mineral processing to agriculture. 

Huang said AI and robotics can help people do more, but ambition will shape how fully they seize that opportunity. He applied the idea to Kazakhstan, saying AI could help lift the country and its people.

“If you have ambition, and I believe you do, AI will lift your nation,” Huang said. “AI will lift your people.”

Huang also praised the founders of Higgsfield AI, the Kazakhstan-founded startup, and it was announced NVIDIA is making an investment into the startup during the event. “The first thing and the last thing is you invest in people, not companies,” he said, describing the founders as visionary and pointing to their effort to compete globally. 

Asked about AI security, Huang said the technology must be developed and deployed responsibly. He described NVIDIA’s OpenShell as a way to contain AI agents by limiting their access to files and tools and monitoring their activity.

For young people entering a fast-changing field, Huang recommended learning how AI works — not just how to use it. He said aspiring AI programmers should understand the technology’s principles, strengths and weaknesses, combining technical knowledge with the ability to work effectively with AI.

“I envy being 18 years old today… because the world is changing so rapidly, and there’s a complete reset because of technology.”

The Infrastructure Layer Is Scaling

The visits coincided with a signing ceremony marking Firebird’s investment agreement with the government of Kazakhstan, held at the presidential palace. 

Building on its first AI factory in Armenia, Firebird is expanding into Kazakhstan, where it has secured a U.S. export license for 45,000 NVIDIA Grace Blackwell GPUs. The first phase is under construction with Kazakhtelecom at Data Center Valley, targeting commercial launch in the third quarter of 2027.

A planned second phase would bring Firebird’s total potential AI infrastructure capacity in Kazakhstan to approximately 500 megawatts. Firebird also plans to establish Firebird Labs Kazakhstan to support local AI startups, research and talent development through funding, access to compute and research partnerships.

Firebird cofounder and CEO Razmig Hovaghimian attended the signing ceremony at the Akorda Presidential Palace alongside government officials.

“What began as a seed of an idea 180 days ago has grown into a major investment in Kazakhstan’s AI future,” said Razmig Hovaghimian, co-Founder and CEO of Firebird. “President Tokayev’s vision gave us the confidence to think bigger about what we can build together. With Jensen Huang here today, that ambition feels more tangible than ever. We’re building the infrastructure for Kazakhstan’s talent and ideas to reach the world.”

Kazakhtelecom, the nation’s largest telecommunications company, has already deployed 400 NVIDIA H200 and 480 NVIDIA B300 GPUs in Astana and Almaty.

Next year, the company plans to deploy more than 1,000 GPUs in the Data Center Valley, along with more than 1,000 NVIDIA Vera Rubin GPUs in the Ekibastuz region, serving diverse workloads with infrastructure that enterprises, developers and institutions can use at scale.

An Honorary Black Belt for Huang

At the end of their meeting, President Tokayev presented Huang with the Order of Dostyk, First Class, for his contribution to the development of artificial intelligence in Kazakhstan. Dostyk means friendship and the Order honors peace, friendship and cooperation.

Later that day, President of the International Judo Federation (IJF) Marius Vizer and Timur Kemell, IJF director of promotion and development and a member of the IJF Executive Committee, presented Huang with an honorary black belt in Judo. The belt, embroidered in gold stitching, was a special thanks for Huang’s support of Kazakhstan’s AI ambition and for leadership aligned with the spirit and rigor of the martial art.

AI-Native Companies Scale With Compute

And that development is accelerating. The nation’s AI-native companies are also building out their compute capacity — and the applications that run atop them.

Higgsfield is expanding its AI infrastructure with NVIDIA Vera Rubin running on cloud service providers, giving the company additional capacity as models, products and customer demand for its generative media applications grow.

inDrive and Pony.ai are also collaborating to explore robotaxi services in Kazakhstan, with Astana as the first deployment city. The companies will assess the feasibility of bringing Pony.ai’s seventh-generation robotaxis to the inDrive app, supported by NVIDIA AI infrastructure, built on the NVIDIA Hyperion sensor and compute platform with the NVIDIA Halos safety system.

More local capacity gives developers and companies room to train, fine-tune and serve increasingly capable models. Successful applications, in turn, create demand for more compute.

Kazakhstan’s AI opportunity isn’t defined by GPU counts alone. Connecting that infrastructure with developers, universities, startups and industries can turn accelerated computing into useful applications.

That was at the core of the discussion the NVIDIA team had with hundreds of developers from across Kazakhstan during a reception later in the evening. The gathering in this self-proclaimed AI nation was inspiring because of the ideas shared, the clear drive to build and a collective ambition to share it with the world. 

Today’s announcements represent different layers of the AI stack in Kazakhstan— infrastructure deployment, access to compute and AI-native product development — where the AI foundation is reaching new scale.

Watch the fireside chat between Jensen Huang and Deputy Prime Minister Zhaslan Madiyev, streamed live from Alem.AI in Astana.

Watch NVIDIA founder and CEO Jensen Huang’s GTC Berlin keynote in replay.

Launched today, NVIDIA Minerva is an AIOps platform built to help teams find problems earlier and keep AI factories productive. It brings together telemetry from infrastructure and AI workloads, then applies purpose-built AI models to turn that data into timely, actionable insight.

AI factories are among the most complex operations in the world, with vast numbers of GPUs working across high-speed networks to train models and serve AI applications. At that scale, a single degraded component can slow an entire job. The warning signs may be easy to miss, such as a GPU running hotter than its peers or an NVMe drive losing performance under load.

The platform focuses on AI factory yield, a measure of the useful work infrastructure produces through GPU hours, goodput, cluster uptime and token production.

Delivering a Dynamic View of an AI Factory

Traditional monitoring tools often divide the data center into separate views. Networking, compute, site reliability and machine learning teams each see a different part of the operation. Minerva connects those signals across NVIDIA GPUs, networking, storage, schedulers and workloads.

With these signals, Minerva builds a dynamic knowledge graph that logically maps the infrastructure — GPUs, SuperNICs, switches and cables — and the jobs running on it. This context helps operators trace a slow training job to the component affecting it, even across a factory with hundreds of thousands of GPUs.

It analyzes billions of data points per hour using supervised and unsupervised machine learning. It learns normal behavior, detects previously unseen anomalies, predicts emerging failures and correlates related events based on the factory’s topology. Instead of sending every symptom to an operator, it can group an event storm into a focused incident with a likely root cause.

Minerva comes with a native AI assistant and connects to external agentic AI tools. This adds a natural-language interface grounded in live telemetry. Operators can investigate cluster health and receive recommended remediation through conversation. Teams can keep a person in the loop to manually implement fixes or connect Minerva to automation through infrastructure application programming interfaces and external systems.

Results That Maximize AI Factory Yield

The ability to correlate workload and infrastructure issues along with its agentic AI capabilities help simplify AI factory operations. Instead of dealing with a blizzard of alerts, Minerva aggregates related problems into a single actionable item. This allows AIOps teams to move from reactive to proactive maintenance. And all of this helps AI factories maximize GPU uptime.

Minerva delivers value for the key metrics AI factories focus on:

• Longer time between interruptions: Minerva spots trouble before it happens, so jobs run longer without incident.
• Faster time to repair: When an incident does happen, Minerva pinpoints the problem, leading to a significantly quicker resolution.
• Greater insight: Modern AI infrastructure generates a flood of data; Minerva filters it out, leaving only the handful of alerts that actually matter.

IREN is among the NVIDIA partners using Minerva to optimize its AI factories. 

“As we scale our AI factories, the key to providing the best service for our customers is maximizing the productivity of our GPUs,” said Denis Skrinnikoff, chief technology officer of IREN. “NVIDIA Minerva gives our operations team one view across the network, the GPUs and the jobs running on them, so we focus more on proactive maintenance rather than troubleshooting. That visibility strengthens our operating model for the infrastructure we’re building next.”

Joining IREN, Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy Minerva. 

In addition to Dell Technologies, Lenovo and Supermicro will offer Minerva as part of their AI factory operations portfolios. 

Learn more about NVIDIA Minerva.

See notice regarding software product information.

Lock in and load out this GFN Thursday. Call of Duty: Modern Warfare 4 arrives on GeForce NOW at launch, bringing a new campaign, multiplayer action and DMZ, high-stakes extraction mode to supported devices. The campaign also gets an enhanced look: GeForce RTX 5080-class performance arrives for Ultimate members, bringing support for ray-tracing features in the cloud.

There’s a new way to join the cloud, too: Existing supported Fire TV Stick users can now purchase one-month Performance or Ultimate memberships directly through Amazon, making it easier to sign up and start streaming on the big screen.

Plus, Square Enix’s FINAL FANTASY RESONANCE brings cinematic pixel art and strategic battles to the cloud, and The Elder Scrolls Online’s Echoes of Skyrim Season adds the entire Greymoor Chapter free for all ESO Standard Edition owners.

It’s all part of six games joining the GeForce NOW library this week.

Deployment Without the Downloads

Squad up and answer the call on the cloud.

Activision and Infinity Ward’s Call of Duty: Modern Warfare 4 launches today on GeForce NOW. Members can drop into the trenches in Korea, close-quarter combat in New York, high-octane chases through Paris, night raids in Mumbai and city-wide engagements to reclaim occupied territory.

Fight as Private Park, a young South Korean soldier facing live combat for the first time alongside his squad as a routine patrol turns into a desperate fight for survival. Beyond the front lines, operator-turned-outlaw Captain Price is operating outside the system he once served, pursuing revenge through covert missions as the conflict spreads.

Multiplayer pairs precise combat with fluid movement, a dozen core 6v6 maps from around the world and battlegrounds that are shaped by the campaign’s global conflict. The DMZ extraction mode sends players into contested territory to recover valuable technology, weigh objectives and decide when to get out. Loot, fight, negotiate, betray and extract — it’s all fair in the cloud and battleground.

Ultimate members can now experience the campaign with ray tracing, powered by newly added GeForce RTX 5080-class performance across PCs, Macs, handhelds, mobile devices, browsers, supported TVs and more. With game downloads and installs handled by GeForce NOW, members can spend less time preparing a local rig and more time preparing the next loadout.

A New Way to Play

GeForce NOW in-app purchase is rolling out to existing supported Fire TV Stick users, making it possible to purchase a membership directly through Amazon using an existing account. Transactions are processed through Amazon’s billing system, creating a more seamless path from setup to gameplay.

GeForce NOW transforms Fire TV into a powerful PC gaming rig in the cloud. Members can stream supported PC games they already own or purchase new titles from digital stores including Steam, Epic Games Store, Ubisoft Connect, Xbox PC Game Pass, GOG and EA.

The library includes thousands of ready-to-play games, including many of the world’s most-played titles and more than 100 free-to-play games. No downloads, installs, patches or updates stand between a new game and playtime. An Amazon Fire TV Stick, the GeForce NOW app and an Ultimate membership are all it takes to level up the living room with GeForce RTX 5080-powered gaming.

A Pixel-Perfect Fantasy

Classic roots. A new adventure.

Square Enix’s FINAL FANTASY RESONANCE launches today on GeForce NOW, bringing the series’ classic roots into a cinematic pixel-art world.

Travel with companions bound by fate and uncover the truth behind a force threatening to bring everything to ruin. Strengthen the party with Visions of beloved FINAL FANTASY characters, each bringing unique skills and abilities to battle. Dynamic camera angles and pixelized 3D models give familiar fantasy a fresh perspective.

Exploit enemy weaknesses to stagger foes, break their defenses and seize extra turns before finishing the bonus phase with powerful Resonance attacks. Airships, crystals, espers and moogles bring familiar series favorites to a world filled with secrets to discover.

GeForce NOW handles the game’s downloads and storage in the cloud, leaving more space on supported devices for everyday essentials. Save the inventory management for potions and Phoenix Downs.

Step Into a New Season in Tamriel

Familiar lands. Fresh reasons to return.

The Elder Scrolls Online’s second season, Echoes of Skyrim, launched Wednesday, Oct. 21, free for all ESO players and ready to stream through GeForce NOW.

Season Two gives everyone who owns ESO Standard Edition access to its Skyrim content and the Antiquities treasure-hunting system. The Markarth downloadable content follows Monday, Nov. 9, rounding out the additions with two classic zones to explore. 

The Season also introduces Solo Dungeons, reimagining challenges previously designed for groups. Moon Hunter Keep and March of Sacrifices have been redesigned and rebalanced for players ready to tackle their dangers alone. Earn new rewards and take on new challenges at your own pace without needing to find a group.

A new Tamriel Tome also launched Oct. 21, offering both free and premium battle pass reward tracks. 

In addition, members can look for the following titles joining the GeForce NOW library this week:

• We Were Here Tomorrow (New release on Steam, Oct. 20)
• FINAL FANTASY RESONANCE (New release on Steam, Oct. 22)
• Call of Duty: Modern Warfare 4 (New release on Oct 23, available in the Call of Duty app)
• CONTROL Resonant (Epic Game Store)
• Crimson Desert (Epic Games Store)
• Planet Zoo 2 (Xbox app, supports XBOX Play Anywhere)

Dates listed above reflect when games are released on their respective stores. GeForce NOW availability may vary, as games are onboarded after release and added throughout the week. Keep an eye on GeForce NOW channels and GFN Thursdays for availability updates on announced titles.

Gamers who want to test out high-performance GeForce NOW cloud gaming can start with a day pass to try premium cloud gaming before committing to a membership. Better yet, the cost of the day pass can be applied toward a first monthly membership — making it easy to try GeForce RTX-powered cloud gaming with the latest releases, then level up for more.

What’s on the mission board this weekend? Let us know on X or in the comments below.

At GTC Berlin, banks, payment companies and financial data providers are showing what production-scale intelligence looks like — built on NVIDIA accelerated computing, powered by open weight models and running on their own infrastructure.

Revolut is rebuilding its technology stack on a single foundation model PRAGMA, powered by NVIDIA accelerated computing. Using the NVIDIA cuDF toolkit and Dynamo-Triton software on NVIDIA Blackwell Ultra GPUs, Revolut is scaling PRAGMA in production, servicing an initial 80 million customers and engineered to scale 3x.

“Revolut processes billions of transactions — the opportunity was always there, but the question was whether foundation models could operate at that scale and across domains. PRAGMA answers that,” said Pavel Nesterov, head of AI at Revolut. “Built in collaboration with NVIDIA, it’s the clearest proof yet that the gap between a research breakthrough and a production banking system can be closed.”

Training on Accelerated Infrastructure

Starling Bank, the first bank to train an agentic model on U.K. sovereign cloud infrastructure, has developed “Starling Intelligence” — the intelligence layer that underpins its AI capability — on Civo’s U.K. cloud using the bank’s own transaction data. 

Lloyds Banking Group built ATUM, a transaction foundation model that converts billions of banking transactions into reusable intelligence — accelerated with NVIDIA RTX PRO 6000 Blackwell GPUs and improving efficiency across bank-wide use cases.

SBS launched a banking sovereign intelligence platform so banks can run generative and agentic models without their data leaving their own walls.

Open Models at a Fraction of the Cost

Open weight models give European financial institutions proprietary intelligence built on data they own, at costs they control. NVIDIA’s accelerated computing platform is the engine running beneath them.

Neobank bunq’s agentic assistant Finn now runs on open weight models. Powered by NVIDIA accelerated computing, deploying on Nebius matches the quality of leading closed models at 60% of the cost per answer. 

NatWest is building technology designed to recognize the full range of British accents, dialects and financial terminology, developed with NVIDIA’s speech models, including Nemotron 3.5 ASR. 

Modulr is building specialized models for distinct parts of payment operations, based on the NVIDIA Nemotron-3-Ultra-550B open model.

Santander is an early adopter of the new NVIDIA Kumo family of open weight foundation models for structured financial data, using them to build specialized intelligence for core banking workloads.

Accelerated Computing Transforms Financial Research

LSEG is working with NVIDIA to help financial institutions analyze large volumes of historical tick data up to 10x faster with the Polars GPU engine, accelerated by NVIDIA cuDF on NVIDIA RTX PRO 6000 Blackwell GPUs. 

RavenPack built a dataset generation pipeline on Bigdata.com that takes a plain-language research hypothesis and returns a labeled, backtest-ready dataset — running on a fine-tuned NVIDIA Nemotron model. Investors can use these datasets to identify, analyze and visualize market trends.

Speeding Data Pipelines to Improve Fraud Detection 

RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA cuDF help accelerate data processing across multi-terabyte-scale, siloed, regulated sources, compressing the time from data to decision for fraud and other scenarios.

HSBC provides near-real-time client insights that support customer processes and on-demand decision-making while minimizing risk and fraud, powered by 5x faster data processing on Starburst Enterprise’s GPU Engine. 

Stripe, a global provider of programmable financial services, sees 42x faster data pipelines and 92% cost savings for its Stripe Radar fraud-detection platform with NVIDIA GPU-accelerated Ray Data in offline testing. This enables Stripe Radar’s machine learning pipelines to continuously optimize model training and improve fraud detection. 

Learn more about NVIDIA accelerated computing in financial services.

See notice regarding software product information. 

To help developers rapidly build world models, which help physical AI systems understand and simulate the real world, NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints. 

Ranking top five on six different open models leaderboards, the NVIDIA Cosmos platform gives world model builders the frontier open models — as well as data, training and optimization tools and recipes — to scale training data, adapt models, test behavior and optimize for performance before deployment. 

Build Better Training Data

Building world models begins with multimodal, multisensory data.

NVIDIA Cosmos Data Factory brings together the tools and skills for labeling, curating, augmenting and evaluating data at scale for training physical AI. Cosmos Curator, a GPU-accelerated solution for curating video training datasets, has now added support for multimodal multisensory inputs and processing blocks.

Cosmos Data Factory now offers the following five cloud-native blueprints and is available through GitHub, Microsoft Azure, Nebius and CoreWeave:

• Autonomous Vehicle Dataset Generation: Curates and labels driving data, then adds synthetic examples of hard-to-capture scenarios to expand training coverage.
• Image Attribute Augmentation: Varies object appearance to improve image search.
• Event Video Generation: Turns still images into synthetic videos of rare or unsafe events.
• Defect Image Generation: Generates printed circuit board, metal and glass defect images for inspection models.
• Video Data Augmentation: Changes weather and lighting in existing footage while preserving scene structure.

Cosmos Data Factory accelerates data curation, processing and synthetic data generation with agentic skills.

Companies are already putting Cosmos Data Factory blueprints to work to speed their physical AI development workflows. 

Porsche is leading the development of a shared AV data flywheel across Volkswagen Group brands, using Cosmos Data Factory through Voxel51 on Microsoft Azure. This is expected to reduce repeated physical test drives by up to 90% and shorten development cycle times by up to 85%.

Porsche searching and augmenting autonomous vehicle data footage in Voxel51, powered by Cosmos Data Factory.

ANYbotics generated over 1,500 synthetic video clips with Cosmos Data Factory blueprints, curating real-world data and validating outputs with Encord’s platform, to train its industrial-inspection robots to recognize liquid spills and other changes between facility visits.

ANYbotics generating anomaly clips with Encord powered by NVIDIA Cosmos Data Factory.

Ultralytics, a computer vision company, retrained its YOLO model with real images and targeted synthetic defects generated using Cosmos Data Factory, improving defect localization and recall.

Train, Adapt and Optimize Models

With training data prepared, developers may train their own architecture or start with a pretrained model.

At GTC Berlin, NVIDIA extended Cosmos Framework training, optimization and deployment tools and recipes as agent skills to more open models, along with Cosmos 3, giving world model builders a faster path from experimentation to deployment.

Developers and agents can now adapt open models like GR00T, Alpamayo, QWEN, Pi0.5 and other compatible Hugging Face models through fine-tuning and reinforcement learning, optimize the model with distillation and quantization, and deploy with high-performing inference.

To develop a world model specialized for egocentric manipulation, robotics research lab MicroAGI post-trained Cosmos 3 on videos of humans manipulating objects and distilled its 32-billion-parameter reasoner down to an 8-billion-parameter Nano model with no loss in accuracy. 

Developers can label data and post-train Cosmos 3 on Roboflow for their specific environments and tasks, then deploy those models to RoboFlow hosted or on-premises NVIDIA GPUs to bring video reasoning into factories and warehouses.

Test Policies and Run Models Faster 

Alongside these tools, NVIDIA continues advancing foundational technology through its work on Cosmos models.

New Cosmos 3 Nano model variants for simulation enable developers to test autonomous policies in repeatable, hard-to-capture, multiview multisensor scenarios. Accompanying code allows developers to adapt the models to train and evaluate their own policies for autonomous vehicles and bimanual robots in interactive simulations before real-world testing.

Developers can deploy core Cosmos models using updated NVIDIA Cosmos 3 NIM microservices, which deliver up to 2x faster inference with no quality loss in validated configurations, while continuous video analysis enables sub-second alerts. 

For vision AI developers, the new NVIDIA Metropolis Blueprint for video search and summarization (VSS) 3.3, built on Cosmos NIM microservices, turns one prompt into an efficient video AI application and helps teams monitor 40% more video streams with the same GPU. 

ADNOC uses Fogsphere’s MistIQ agentic platform, built with Cosmos and the NVIDIA Metropolis VSS Blueprint, to detect safety hazards in real time across 65 sites. For rare events, Fogsphere also uses the Cosmos Data Factory to generate synthetic training data, improving detection without needing to physically stage dangerous situations.

The Cosmos platform is fully modular and open. Developers can use Cosmos Data Factory to generate training datasets, Cosmos Framework to train and adapt world models, then deploy supported Cosmos models through NIM microservices.

Companies around the world are building on the NVIDIA DSX AI Factory Platform to plan, simulate and run the grid and the AI factory as one system, from the substation to the rack. Their adoption of the platform responds to an acute problem.

Power is now the biggest bottleneck in infrastructure. More than $300 billion in equipment is backlogged, and over 200 gigawatts of projects are waiting in U.S. interconnection queues. 

One root cause of the bottleneck is siloed systems. The grid, the facility and the compute inside have long been designed independently from one another, creating the conditions for delays, risk and stranded capacity.

To help address this, NVIDIA DSX brings these systems together:

• NVIDIA DSX Sim and SimReady assets from leading energy manufacturers let engineers simulate the grid, substation and AI factory together, validating generators, switchgear, power systems and cooling before breaking ground.
• NVIDIA DSX Flex syncs AI factory demand with grid conditions and onsite generation, turning AI factories from static loads into flexible grid assets.
• NVIDIA DSX MaxLPS and DSX Exchange squeeze more compute from every watt and securely share signals across compute, networking, energy, cooling and operations.

GE Vernova is deploying its grid-to-facility and rack power expertise to complement NVIDIA DSX systems, enabling customers to transform power constraints into competitive advantages while maximizing compute and token output, sustainability and grid reliability. 

Hitachi Energy’s battery energy storage system has qualified as NVIDIA DSX Ready,  helping customers identify power infrastructure that meets applicable NVIDIA DSX reference architecture design requirements for resilient, reliable and scalable AI factories.

Schneider Electric, together with AVEVA and ETAP, is advancing NVIDIA DSX with validated NVIDIA Vera Rubin power and cooling reference architectures and an NVIDIA Omniverse-powered AI factory digital twin. 

Combining AVEVA’s engineering software expertise with NVIDIA SimReady assets, OpenUSD and Omniverse enables connecting simulations, real-time operational data and AI-driven optimization across AVEVA, Schneider Electric and third-party solutions. Together, these capabilities help organizations design, simulate, operate and optimize data centers across their lifecycle, improving energy efficiency and workload performance while accelerating time to token. 

Siemens Energy is expanding its collaboration with NVIDIA by developing a reference design for resilient, scalable and grid-aware power infrastructure across power generation, transmission and distribution. This design is aligned with the requirements of the NVIDIA DSX Vera Rubin AI factory design. The company is also exploring usingNVIDIA technologies such as RAPIDS, Metropolis and Isaac Sim for its Noedra digital twin platform, evaluating how the technologies could enhance grid infrastructure monitoring, identify emerging risks and support unplanned outage prevention.

Phaidra integrated DSX Flex, DSX Max-Q and DSX Exchange into its self-learning agents, optimizing cooling and power allocation to maximize tokens per watt, improve grid flexibility and deliver about 10% more compute within the same power envelope.

Emerald AI integrated its Conductor platform into DSX Flex to help utilities tap underused grid capacity, an approach modeled to unlock up to 100 gigawatts on the existing U.S. grid.

Explore the NVIDIA Omniverse DSX Blueprint and the Vera Rubin DSX AI Factory Reference Design. Learn more from NVIDIA and energy leaders who are accelerating power-flexible AI factories to strengthen the grid at GTC Berlin. 

Visit Hitachi Energy (booth 7004, hall 7), Schneider Electric (booth 3041, hall 3) and Siemens Energy (booth 3048, hall 3) and tune into sessions with Schneider Electric on Wednesday, Oct. 21: From Grid to AI: Engineering the Future of AI Factories and Energy Intelligence: Powering the AI Era.

European retailers are using open models and physical intelligence across use cases — from a commerce platform cutting inference costs by 90% to to a fulfillment center where collaboration of different robotic fleets is simulated inside a digital twin.

Frontier Results With Open Models

Retail leaders and technology companies across Europe are using NVIDIA-accelerated open and frontier models to advance agentic commerce, digital product discovery and retail R&D.

Shopify post-trains open models to reach frontier-level accuracy at a fraction of the cost. Its engineers use the NVIDIA Megatron Bridge library for faster fine-tuning and NVIDIA NeMo RL for reinforcement learning. Shopify has post-trained NVIDIA Nemotron 3.5 Lightning for the GraphQL subagent integrated into Sidekick, Shopify’s AI assistant for merchants. The subagent retrieves store data for several Sidekick features, including Sidekick Pulse, which researches a merchant’s store and recommends ways to improve it. 

On Shopify’s internal benchmark, the post-trained model surpasses a leading frontier model in accuracy while cutting serving costs by 90% and delivering responses 7x faster. Beyond Sidekick, NVIDIA CCluster vLLM inference optimization software helped Shopify achieve 4.5x faster performance and up to 12x lower inference cost across five additional use cases: catalog enrichment, merchant assistant, e-commerce shopping simulation, product taxonomy classification, and merchant and buyer recommendations.

Zooplus, Europe’s leading online pet retailer, with Infosys and NVIDIA technologies rebuilt its product discovery stack for the agentic era. A production ready prototype deployed inside Zooplus’ Amazon Web Services (AWS) environment achieved 86% search accuracy using the NVIDIA Nemotron Retriever library for semantic search and the NVIDIA Blueprint for retail catalog enrichment for generated, search-ready product attributes.

Photoroom results from its deployment with Vestiaire Collective, where its Image Editing API now standardizes every primary image,  about 40,000 a day across 70 countries, at the point of listing. Listings processed by Photoroom sold at a 5% higher rate of liquidity, with 3% to 7% more unique views per product page. The pipeline runs on NVIDIA GPUs with TensorRT and Triton Inference Server, and NVIDIA Dynamo Snapshot can help users quickly respond to demand spikes.

Omi launched ProductDrop, a workflow that anchors every generated image to an approved digital twin, letting brands scale creative output across entire catalogs without introducing product errors. L’Oréal is using this approach, combining Omi’s digital twins with generated brand environments within its CreAItech program. Omi runs TRELLIS 3D asset generation on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, speeding up inference by 10x and reducing pipeline latency by 4x.

In addition, L’Oréal is accelerating the discovery of the best ingredient combinations, moving from months of experimentation to hours of computational screening, with the integration of NVIDIA ALCHEMI into its research workflow. 

L’Oréal is also testing this approach with CurieOS, Orbital Industries‘ operating system for science and engineering, using OrbMol-v2, its open source molecular simulation model. Integrated with the NVIDIA ALCHEMI Toolkit and powered by NVIDIA accelerated computing, OrbMol-v2 is five times faster than comparable models and delivers 21% lower error than the next-best model of its kind. This AI-based workflow enables researchers to identify the most promising ingredient combinations for solubilizing UV filters and optimizing performance before committing to laboratory testing.

Mirakl, a software company for intelligent commerce, runs open models on NVIDIA GPUs, both on its own infrastructure and through European cloud providers like Scaleway. This keeps its technology sovereign, avoids vendor lock-in and delivers the speed and scale its customers expect. Mirakl’s Catalog Transformer pairs fine-tuned open models with proprietary services and has onboarded more than 70 million products, turning messy vendor data into accurate, well-organized catalogs. 

Physical Intelligence Enables Training in Simulation

NVIDIA physical AI, digital twins and open models help simulate retail environments, automate warehouse operations and deliver virtual try-on experiences for European retailers and fashion brands.

Otto Group advanced its autonomous warehouse under human supervision initiative — built with Reply on NVIDIA Omniverse libraries and running on Google Cloud — to the group’s largest fulfillment center in Haldensleben, Germany. New capabilities let logistics teams design, test and optimize warehouse operations entirely in simulation before any physical implementation. 

ALLSIDES launched its 3D Digital Twin Factory: an automated pipeline that transforms physical objects into SimReady 3D digital twins in minutes, capturing sub-millimeter geometric accuracy, measured physical properties and fully relightable materials that maintain accuracy under any lighting condition. Those digital twins serve as the foundation for simulating entire store layouts in 3D, as well as for tracking products reliably throughout the shopping journey and at checkout regardless of ambient light. Built on NVIDIA CUDA, OptiX and TensorRT, the platform already serves brands including adidas, Amazon, Nike, Zalando and Zara.

CATCHES achieved production results from its RealFit deployment with AMIRI of conversion up 55%, add-to-cart up 80% and gross sales up 56%, all after seasonal adjustment against like-for-like styles. The company launched PredictiveFit, extending physics-based virtual try-on to retailers, as well as CASI, a personal fashion agent that shops, styles and sizes for customers over WhatsApp, powered by open weight models including NVIDIA Nemotron 3, built on NVIDIA Omniverse and the Newton Physics Engine.

Explore New NVIDIA Retail Blueprints at the Nebius Booth

At GTC Berlin, Nebius is showcasing three NVIDIA Blueprints for retail, all available in early access and running on Nebius infrastructure, accelerated by NVIDIA:

• Agent Harness for Employee Productivity: An agentic assistant that consolidates scheduling, task management, inventory alerts and operational insights for store managers. 
• Inventory Simulation and Optimization: A blueprint that can help retailers reduce overstock and stockouts by modeling demand scenarios, testing replenishment strategies and optimizing stock levels before users need to commit in the real world. 
• Agentic Finetuning Flywheel: A self-improving loop that uses agentic workflows to continuously generate training data, fine-tune open source models and feed real-world performance back into the next training cycle.

Request early access to the blueprints and learn more about NVIDIA technologies for retail and consumer packaged goods.

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Weather forecasts shape more than daily plans; timely weather information is crucial for energy producers, insurers and transport operators to plan around changing conditions. A global forecast can show a storm taking shape, but decisions about solar farms, shipping routes or insured property depend on what happens locally.

NVIDIA Earth-2 gives developers and researchers a family of open AI models and tools they can customize with their own data. Running on NVIDIA computing, these technologies support global weather prediction, detailed regional forecasts and environmental research.

StormScope EU is the latest model to join Earth-2 — a regional version of the StormScope model for short-term forecasting, trained over Europe weather and climate data. It uses EUMETSAT satellite observations to predict evolving cloud conditions across 16 Meteosat channels at 10-minute intervals.

At GTC Berlin, NVIDIA is showcasing a live StormScope EU pipeline in an interactive weather demo built in partnership with Earthmover. The demo uses Earth2Studio to run forecasts and Earthmover’s data platform to manage and serve the real-time results, which are updated as soon as new satellite observations come in, every 10 minutes. Earthmover serves these results as map tiles, letting visitors explore predictions across Europe without downloading entire datasets.

Developers can combine Earth-2 models in workflows tailored to regional needs, using StormScope for short-term forecasting and CorrDiff to add local detail to coarser weather data. Developers can also pick up these model architectures and train them for new environmental applications such as air-quality modeling. The open source NVIDIA PhysicsNeMo framework provides training and customization tools so teams can build applications that incorporate proprietary data and domain expertise to meet regional needs.

Organizations building on Earth-2 capabilities include:

AXA is using NVIDIA Earth-2’s FourCastNet 3 to study regional cyclone risk beyond the historical record across more than 1,000 simulated seasons, generating insights that can inform risk assessments and help customers target prevention efforts.

Vaisala Xweather is combining Earth-2 with its proprietary observations, lightning data and forecasting expertise to enhance its weather intelligence platforms for government, energy, maritime, transportation and insurance customers. Its work includes adapting StormScope with lightning data to advance short-term forecasting lead time and make these warnings accessible through Vaisala Xweather’s software-as-a-service offerings and AI-assistant integrations.

Video courtesy of Vaisala Xweather.

University of Manchester researchers adapted the Earth-2 CorrDiff model for U.K. air-quality modeling. It trained the model on the NVIDIA-powered Isambard-AI supercomputer and ran it locally on NVIDIA DGX Spark systems, bringing environmental modeling from a national supercomputer to the desktop.

TotalEnergies is exploring NVIDIA Earth-2 for solar-energy forecasting, evaluating StormScope-based solar-radiation nowcasts at two photovoltaic sites and developing models to provide weather information at subhourly intervals. The work explores how more detailed forecasts could help anticipate changes in solar-power generation.

Space42 developed SARDiff4Height using CorrDiff and NVIDIA PhysicsNeMo. The research estimates surface heights from a single radar image and produces uncertainty maps, helping analysts understand where estimates need closer scrutiny.

Explore NVIDIA Earth-2 open models and tools, and explore with Earth2Studio’s forecasting examples. Read AXA’s hurricane-risk case study and Manchester’s air-quality story to learn how teams are adapting weather AI to specialized challenges.

Governments need modern solutions to serve the public while keeping sensitive information under their control.

Open models provide a path forward, letting governments evaluate them, adapt them to local languages and public services, run them on infrastructure they control, apply their own safeguards and keep sensitive data within controlled environments. Some open models go further, with the datasets they were trained on published so governments can verify if training data was licensed, biased or compliant.

NVIDIA partners and European governments are teaming to bring  advanced models to boost the efficiency of public administration while retaining control of data and infrastructure. 

Several are also building editions of the NVIDIA Nemotron-Personas collection, the partner-grounded datasets behind NeMo User Sim, NVIDIA’s open source library for population-scale user simulation that dynamically evaluates and trains models and agents in local languages.

European Commission: Scaling Governed AI Applications

Berlin-based software company deepset is expanding its Haystack Enterprise Platform, the foundation of AI@EC, the European Commission’s shared platform for building and running AI applications on commission-managed infrastructure.

The update connects agent development, execution, optimization and governance for all customers, adding capabilities to compare operating costs, evaluate answer quality and trace problems. 

NVIDIA Triton Inference Server supports the shared services used to find and rank relevant information. The expansion builds on operating requirements demonstrated by deployments such as AI@EC, where reusable components, role-based access, audit trails and guardrails support applications including document summarization, knowledge management and multilingual policy analysis.

Poland: Advanced Computing for Healthcare and Employment

Deloitte is helping Poland build agentic systems for healthcare and employment services using open models. 

Poland’s eHealth Centre is building its Intelligent Services Platform for medical imaging with NVIDIA DGX BasePOD and the MONAI framework. The tool will help physicians analyze X-rays, CT scans and MRI images, detect abnormalities and prioritize urgent cases, reducing the time patients wait for results.

With Deloitte’s support, Poland’s Ministry of Family, Labour and Social Policy is developing an agentic factory on NVIDIA accelerated computing infrastructure. Designed to support more than 400 labor offices, it will run models including PLLuM, GPT OSS and Inkling for document search, summarization and digital assistants, helping staff more efficiently assist job seekers.

NVIDIA is also working with SpeakLeash in Poland and the University of Ljubljana in Slovenia on country-specific additions to its synthetic personas program, with localized datasets planned to follow.

UK: Data for Locally Relevant Advanced Modeling

London-based Locai Labs released Nemotron-Personas-UK, a dataset of 1 million synthetic personas reflecting the U.K.’s demographic and geographic diversity while reducing privacy risk. Potential uses include healthcare evaluation and bias testing across age groups, education levels and rural and urban populations. For developers building public-service models, it offers a way to test whether models work consistently across different communities.

Germany: Digital Assistants and AI for Classified Work

In Germany, Materna is introducing Sinfonie, an on-premises platform combining generative AI, voice interaction and 3D avatars, with NVIDIA Audio2Face available for animation. Built on a project for a German state administration, it helps employees access institutional knowledge through spoken questions while keeping data on their organization’s infrastructure.

For organizations handling classified information, secunet, NVIDIA, Red Hat and deepset are going a step further. The four companies are developing a sovereign cloud offering built on secunet’s proven SINA Cloud foundation, which includes security components that have been authorized for use with classified information. The planned offering combines four components: NVIDIA accelerated computing and NVIDIA Nemotron open models; Red Hat OpenShift AI for deploying and managing AI workloads; deepset’s Haystack AI agent orchestration; and infrastructure operated by secunet in German data centers. 

To support local development, the Sovereign Open Source Foundation Models research initiative (Soofi) is developing a family of AI models for German industry and the public sector. That includes Nemotron-Personas-Germany, an open dataset of 1 million records, each with six synthetic personas, grounded in the 2022 census across all 16 federal states and 400 districts. Created with NVIDIA NeMo Data Designer without real people’s private data, the dataset helps developers test whether public-service models work consistently across regions, age groups and communities. Like the U.K. edition, it can be used with NeMo User Sim so developers can evaluate and train models against simulated users based on synthetic German personas.

France: Advanced Open Models in the Public Sector Workspace

CANUT, France’s public purchasing body for digital services, selected LINAGORA and Docaposte to provide a sovereign collaborative workspace built on Twake.ai. 

More than 2,500 eligible organizations, including municipalities, schools and healthcare institutions, can access the framework without running their own tender. The workspace offers public employees email, document sharing and collaboration tools, with NVIDIA technology supporting speech transcription and infrastructure hosted in France. Région Occitanie is the preferred partner for scaling deployment.

Greece: A Local Agentic Ecosystem

Greek AI and energy company KIEFER is developing an agentic factory built on NVIDIA DGX SuperPOD with DGX GB200 alongside Sophea open weight models and Greek-language tools for knowledge retrieval, speech and digital assistants. These capabilities stand to make institutional information easier to find and use for Greek-speaking public sector teams, while giving organizations control over where agents run and how data is handled.

Codefest: Building the Next Applications

In an effort to support public sector organizations in integrating agentic AI in Europe and beyond, NVIDIA Open Models Codefest brings together startups, software providers and labs across the region to turn open models into locally controlled solutions for public sector challenges, including medical reasoning, AI tutoring and model governance. The top five teams are Pleias, Zehnlab.ai, Disseqt, Algoritmica GmbH and ABA Technology. Deutsche Telekom and T-Systems have provided the compute infrastructure for teams to train and deploy large language models. 

Learn how NVIDIA helps governments and communities accelerate secure advanced modeling and agent creation.

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At GTC Berlin, NVIDIA is showcasing its full lineup of local AI hardware. RTX Spark, which launched last week, powers personal agents on Windows PCs; DGX Spark supports developers building and scaling applications on Linux; and DGX Station serves enterprise teams running frontier workloads on Linux, with a Windows version arriving in December.

Announcements include:

• Underdog Desktop Agent now available for RTX Spark
• DGX Spark 64GB available on Friday, Oct. 23
• NVIDIA Sync to support eight DGX Sparks and add Model Launcher this month
• October DGX Spark software update adds Wi-Fi Assistant and updates DGX Spark Skills
• DGX Station improves manageability with seven new integrations
• DGX Station for Windows launches in December with support for many manufacturing apps and agent harnesses
• Three new open source reference workflows for robotics on DGX Station
• NVIDIA Cloud Functions is coming in early access to DGX Station
• Recursive Flint, a new local coding model, is now available for NVIDIA local hardware
• Day-0 support for new Nemotron, GLM, DeepSeek, Qwen, MiniMax and FLUX.3 AI models
• New accelerations for llama.cpp and vLLM inference engines and a new collection of checkpoints optimized for local AI hardware.

Learn more about these local AI announcements below, and check out the demos and sessions at GTC Berlin.

RTX Spark Brings Personal Agents to the Windows PC

RTX Spark is a new beginning for the PC — running personal agents, accelerated content creation and 1440p 100 frames-per-second gaming with up to 128GB of unified memory and 1 petaflop of AI compute.

Here’s what early reviewers are saying about the platform since its October 16 release:

“A dream machine for serious creators … ” — CGMag

“Designed for the future of AI … ” — The Verge

“An extremely capable, surprisingly portable and efficient machine … ” — PCMag

Underdog, a new desktop agent app built for fully local, private AI, is also bringing its experience to RTX Spark. Powered by its Husky model-specific inference engine, Underdog runs models entirely on device, so personal agents can get work done without user data ever leaving the PC. Underdog is available now for download.

To better enable developers and enthusiasts to build and customize local agents, many RTX Spark laptops come with an unlocked bootloader.

During the GTC keynote, NVIDIA founder and CEO Jensen Huang showed off how AI agents running on RTX Spark can be used to train Hugging Face’s Microduck robot using NVIDIA Isaac Lab, Hermes Agent and Nemotron 3.5 Bolt 74B.

ASUS, Dell Technologies, HP, Lenovo, Microsoft and MSI laptops are available today, with Acer coming soon.

Learn more about RTX Spark, and get started with the latest RTX Spark playbooks.

DGX Spark Gives Developers Room to Build

New DGX Spark 64GB systems — that can run models including Nemotron 3.5 Bolt 74B 24GB, Qwen3.8 27B and Gemma 4 26B-A4B — start rolling out from partners on Friday, Oct. 23.

When workloads require more capacity, developers can connect multiple DGX Spark systems via built-in NVIDIA ConnectX-7 networking and NVIDIA Sync Cluster Assistant. This allows users to run advanced coding models like Qwen 3.8 Flash Next, DeepSeek v4 Flash 0731 and GLM 5.3 Flash.

Now, DGX Spark supports clusters of up to eight units in manual configuration, enabling larger models, longer contexts, concurrent agents and distributed development. Ideal for small teams sharing a single cluster as a backend, with each developer running concurrent sessions against the same model, it delivers 1TB of unified memory — enough to run frontier-class models like GLM 5.3.

Get started with the DGX Spark cluster playbook.

NVIDIA Sync Model Launcher will also be available later this month. It simplifies access to local AI on a single DGX Spark or a cluster. With a few clicks, Model Launcher downloads a model and container suited to the configuration and connects the model to OpenCode. It automatically loads DGX Spark Skills for troubleshooting, device maintenance, access to official documentation and other workflow guidance.

The October DGX Spark software update brings a new Wi-Fi Assistant that lets users switch networks when DGX Spark is running in appliance mode. Plus, DGX Spark Skills provide workflow-focused guidance in troubleshooting and device maintenance directly into NVIDIA Sync via OpenCode.

DGX Spark systems are available directly from Acer, ASUS, Dell Technologies, GIGABYTE, HP, Lenovo, MSI and NVIDIA.

DGX Station Puts Frontier Intelligence at the Desk

For larger teams, DGX Station offers 748GB of coherent memory and up to 20 petaflops of AI performance, enabling developers to run advanced models like GLM 5.3 and serve 30+ users at speeds comparable to those provided by cloud models. Adding scale, NVIDIA Sync now supports DGX Station, simplifying connections between two Linux-based systems.

New integrations with Absolute Security, Splunk by Cisco, Fleet Device Management, Microsoft Intune, Perforce Puppet, Progress Software Chef and Tanium let IT teams manage DGX Station systems through enterprise setup, management and conformity processes, helping accelerate approvals and reduce deployment risk while minimizing time to first token.

With the upcoming Windows-based systems, developers can build workflows that harness professional applications, run on NVIDIA RTX PRO GPUs and operate from agents that run their large language models (LLMs) on NVIDIA GB300 systems — all in one machine.

DGX Station for Windows, expected from system manufacturer partners in December, extends local AI into the applications and operating environment enterprises already use. Engineers can run local models and agents in Windows-native software with access to AI models, tools and agents on the same system.

DGX Station for Windows will feature support for manufacturing applications from nTop, Dassault Systèmes, Keysight Technologies, MathWorks, Rockwell Automation, Siemens, Synopsys, Unity and Altera for AI-guided design, simulation, validation and engineering automation. Users will be able to run agents using Hermes Agent, Perplexity or Aible on premises for agentic control in enterprise environments.

The same local, controlled computing model extends to robotics. DGX Station gives robotics teams dedicated compute for post-training, simulation and evaluation, shortening iteration cycles without waiting for shared infrastructure.

NVIDIA released three open source reference workflows that bring GPT-6 Astra, NVIDIA Isaac Lab, Isaac Lab-Arena and Cosmos to NVIDIA DGX Station. Developers can use them to refine GR00T 1.7 models with reinforcement learning, train dexterous manipulation skills and post-train Cosmos world models for synthetic data generation.

NVIDIA Cloud Functions is coming in early access to Linux-based DGX Station. Teams can register local model endpoints in a shared catalog and access them through one OpenAI-compatible entry point. 

Developers can upgrade the model behind an endpoint without changing application code, while teammates can use shared models without direct machine access. This turns a DGX Station into a locally controlled inference service for a team.  

DGX Station brings local iteration to AI factory development. On the show floor at GTC Berlin, NVIDIA is previewing DSX OS running on DGX Station to simulate AI factory operations before full infrastructure is available. Another demo will showcase DGX Station systems powering a local token factory for more than 30 software developers.

DGX Station systems are shipping today from ASUS, Dell Technologies, GIGABYTE, HP, MSI, Supermicro and Exxact. DGX Station for Windows systems are expected to begin shipping later this year from ASUS, Dell Technologies, GIGABYTE, HP, MSI and Supermicro. 

Introducing Recursive Flint — A New Local Coding Model Optimized for RTX Spark

Today, frontier AI lab Recursive Superintelligence released Recursive Flint, a local LLM built for fast, accurate coding. A 40-billion-parameter mixture-of-experts model with 2.5 billion active parameters, Recursive Flint delivers fast inference and performs well in agentic coding benchmarks like SWE-Bench Pro v2, Terminal-Bench 2.1 and SWE-bench verified.

Recursive Flint was built using Recursive’s Eureka Machine, a system for large-scale automated AI research. It automates the full research loop: proposes ideas, implements them, runs experiments to validate ideas and uses the results to decide what to try next — creating specialized kernels that boosted Flint’s generation speed on RTX Spark by 55% without changing the model’s weights.

Developers can download the open weights on Hugging Face under the Apache 2.0 license and run the model on RTX Spark and DGX Spark systems, as well as GeForce RTX and RTX PRO GPUs. Developers can also post-train the model with NeMo Automodel for supervised fine-tuning and low-rank adaptation, or LoRa, as well as NeMo RL for group relative policy optimization. 

New Models With Day-Zero Support

NVIDIA is also adding day-zero model support for NVIDIA Nemotron Bolt E4B 4B, GLM, DeepSeek, Qwen, MiniMax, Liquid AI’s LFM2.5-24B-A2B, plus the FLUX.3 Klein diffusion model. 

Nemotron Bolt E4B 4GB packs natural conversation and multi-turn agentic skills into as little as 4GB of memory, powering fast local voice assistants, interactive game characters and embedded interfaces on NVIDIA GeForce RTX GPUs, NVIDIA Jetson devices, and RTX and DGX Spark. Bolt E4B 4GB releases on Friday, Oct. 23. 

Black Forest Labs’ FLUX 3 Image Klein is a 5.6-billion-parameter open weights image model for generation and editing. NVIDIA-optimized builds let it run on GeForce RTX GPUs with as little as 8GB of VRAM and create a 1-megapixel image in about four seconds on an RTX 5090 GPU.

MiniMax M3.1 Flash is a new open model built for NVIDIA DGX Station and DGX Spark, bringing long-context AI to the desktop. Liquid AI’s LFM2.5-24B-A2B is an open weights reasoning model that activates just 2 billion of its 24 billion parameters per token, making it fast enough to run private, tool-calling AI agents directly on PCs.

Plus, NVIDIA has released a new collection of model checkpoints optimized for RAM efficiency on local AI hardware, enabling frontier-class open models to run with sufficient memory left over for KV cache. Each checkpoint is quantized while staying close to full-precision accuracy, starting with Qwen3.8-Flash-Next-FP4-Local on a single DGX Spark 128GB or RTX PRO 6000 and GLM-5.3-Flash-FP4-Local on two systems.

Developers can post-train on day zero with NeMo AutoModel on both DGX Spark and RTX Spark, with example recipes for Nemotron Bolt E4B, Flux 3 Image Klein and more. 

Accelerated AI Inference

NVIDIA continues to work with the llama.cpp and vLLM open source communities to accelerate agentic workloads on local hardware.

In llama.cpp, new speculative decoding support with MTP for Qwen 3.8 Flash Next makes end-to-end agentic tasks up to 1.4x faster on NVIDIA RTX PRO 6000 Blackwell.

In vLLM, faster speculative decoding and new FlashInfer prefill kernels for Qwen’s linear-attention layers make agentic tasks with Qwen3.8 27B up to 1.3x faster on DGX Spark. On DGX Station, GLM 5.3 Flash gets up to 1.2x higher throughput.

Check out NVIDIA’s local AI inference recipes to try these latest optimizations.

Follow NVIDIA RTX Spark on X, Instagram, TikTok and Facebook — and stay informed by subscribing to the NVIDIA Local AI newsletter. Follow NVIDIA Workstation on LinkedIn and X. 

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General availability of the instances is coming next month, with planned expansion of availability to Europe — bringing an integrated platform for training, inference and large-scale reinforcement learning to cloud customers.

A5X instances bring together NVIDIA Vera Rubin NVL72, NVIDIA ConnectX-9 SuperNICs, Google Cloud Virgo Network and Google Cloud’s fourth-generation advanced liquid-cooling technology for demanding agentic inference and reinforcement learning workloads at scale. Google Cloud plans to scale its Vera Rubin-based infrastructure to as many as 960,000 Rubin GPUs across multiple sites.

Ineffable Intelligence, a London-based technology company founded by AlphaGo creator David Silver, was the first customer to access A5X. Ineffable is using the platform to advance large-scale reinforcement learning and shape “superlearner” systems that learn continuously from experience.

Learning through interaction requires a continuous cycle of generating experience, evaluating outcomes and updating models, requiring infrastructure that can support the entire process at scale. A5X brings Vera Rubin into Google Cloud’s AI Hypercomputer as an integrated system, combining compute, networking and storage to support experience-driven learning at scale.

“We’re building superlearners that continually improve through their own experience,” said David Silver, founder and CEO of Ineffable Intelligence. “That demands both substantial compute and a tight connection between generating experience and training on it, so each improvement in the agent can quickly shape its next experience. Vera Rubin’s combination of compute performance and high-bandwidth connectivity, between the Vera CPU and Rubin GPUs and between the Rubin GPUs within the rack, allow us to push the scale and ambition of our reinforcement learning research further.”

WPP, a London-based marketing and communications company, has also demonstrated its creative agent reference architecture on NVIDIA Vera Rubin-based A5X instances using NVIDIA Nemotron models and NVIDIA OpenShell, part of the NVIDIA OpenShell Reference Architecture for safe AI agents. 

The agents turn conversational requests into multistep creative workflows, such as video generation, upscaling and restyling, with human approval before execution. OpenShell provides governed sandboxes for agent execution, helping control access to sensitive brand assets and creative tools. In WPP’s initial tests, workloads that typically required a multi-node cluster ran on a single A5X instance.

As organizations deploy agentic systems, they need to protect the proprietary models and sensitive data that power these systems — even when running on infrastructure they don’t own.

Google Cloud will be among the first cloud providers to offer NVIDIA Confidential Computing at rack scale on bare metal instances such as A4X Max instances powered by NVIDIA GB300 NVL72. Additionally, Google Cloud is making Confidential Computing generally available on G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, offering a flexible platform for confidential inference and fine-tuning. Customers including Vertebrae, Polaris Cloud and Confidential Core AI are using Confidential G4 for conversational privacy, verifiable agent execution and policy-governed enterprise intelligence.

To support energy-efficient deployment at scale, Google Cloud and NVIDIA are exploring NVIDIA DSX MaxLPS capabilities for Vera Rubin-based infrastructure to help optimize performance per watt and expand capacity within available data-center power budgets.

Learn more about NVIDIA Vera Rubin and Google Cloud A5X instances.

Demand from AI-natives is pulling the European AI cloud ecosystem forward faster than ever. Developers need compute closer to their data, open models they can customize and serve at scale, and full-stack infrastructure that is secure by design. 

NVIDIA Cloud Partners across Europe, the Nordics, Africa, and Central Asia are expanding regional compute capacity for model training and inference, helping developers build agentic and physical AI applications, and establishing boundaries around what agents can access and do.

Together, they’re turning Europe’s energy, data, research and industrial expertise into productive intelligence — supporting new models, applications, organizations and jobs while giving people more control over where and how they develop and deploy AI.

Sovereign: AI Clouds Building European AI Infrastructure

Europe is home to world-class research, engineering talent, industrial expertise, data, the next generation of models and applications that requires large-scale computing close to where that knowledge resides. Regional AI clouds provide the infrastructure foundation on which model builders, enterprises and entire industries can grow.

As AI factories scale, power becomes a constraint which makes it critical for every watt to produce more intelligence. Nscale is using the NVIDIA DSX Platform to coordinate power allocation with workload requirements. In tests using NVIDIA GB300 NVL72 systems, Nscale demonstrated that NVIDIA DSX MaxLPS enabled 37% more GPUs and nearly 50% higher aggregate inference throughput within the same provisioned power budget. 

Nscale also plans to deploy NVIDIA Vera Rubin infrastructure for frontier-model training and production workloads to support leading model and application builders using its full-stack, vertically integrated platform with expansion across five European markets.

Crusoe is developing Spark, its liquid-cooled modular AI factories designed for the NVIDIA Vera Rubin platform, to bring Crusoe Cloud capacity closer to users for low-latency AI inference. Crusoe Cloud serves leading model and application builders across EMEA, including Base44, Gradium, LTX and Wonderful.

Aleria, Cassava Technologies, Firebird, G42, Radiant, SWI Compute and Vultr are also developing and expanding NVIDIA-powered AI infrastructure across Europe, the Nordics, Africa and Central Asia. 

Regional capacity from AI clouds combines the full-stack NVIDIA DSX AI Factory platform, including NVIDIA Vera Rubin, NVIDIA BlueField and NVIDIA Spectrum-X Ethernet networking, giving customers greater choice and control over where workloads and data reside while advancing sovereign computing services and economic growth. 

Open: Models and Libraries, From Training to Inference 

Across the ecosystem, AI clouds are expanding NVIDIA-powered infrastructure and services for developers to build applications with open models and libraries. This new capacity supports model training, fine-tuning for specialized tasks and inference at scale, including applications deployed at the edge.

For production inference, Nebius has adopted NVIDIA Groq 3 LPX, using Spectrum-X Ethernet networking to integrate it with Vera Rubin NVL72 on Nebius Token Factory. The combination will bring faster token generation to latency-sensitive applications and agentic systems through the platform’s managed serverless and dedicated endpoints and familiar application programming interfaces.

For robotics and physical AI, Nebius offers a purpose-built AI cloud solution that includes its full-stack Physical AI Workbench. Robotics companies including Rhoda AI, RoboForce and Sereact are building on the platform. The company also launched three NVIDIA retail agent blueprints — covering inventory, store operations, and fine-tuning —- into early access on Nebius infrastructure.

Nebius is expanding its cloud services widely across the continent with its Physical AI Living Lab, and in Germany as a computer supplier for the Next Frontier Challenge of SPRIND, the Federal Agency for Breakthrough Innovation.  

Baseten has developed a specialized model for code-review routing with Nemotron 3.5 Lightning used by CodeRabbit. Through supervised fine-tuning and reinforcement learning, CodeRabbit improved routing agreement from 75.8% to 80.7% while reducing estimated inference costs by 50% for the evaluated workload. The results demonstrate how AI developers can customize NVIDIA open models for specialized tasks while improving accuracy and inference efficiency.

Fireworks and Era4 are partnering to bring open models to enterprises across the U.K. and Europe. The collaboration combines Fireworks’ model training and inference platform with direct liquid-cooled capacity powered by NVIDIA Blackwell Ultra GPUs at Era4’s Chesterfield facility, supporting plans for enterprise AI solutions in financial services, insurance and life sciences.

Mistral is expanding its NVIDIA accelerated computing infrastructure to support model training, customization and inference. The expansion includes NVIDIA Vera Rubin infrastructure planned for delivery in 2027. Trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral-operated European data centers, Mistral Large 4 is the company’s latest frontier multimodal model, advancing capabilities across coding, agentic workflows and multimodal understanding.

Verda plans to deploy NVIDIA Vera Rubin, continuing to promote open weight models with NVIDIA Nemotron and NVIDIA Cosmos already in production, and using the NVIDIA Dynamo open source framework for inference optimization.

Safe: Secure Boundaries for AI Agents, From Silicon to Software

As developers connect models to tools and business systems, they also need controls over what agents can access and do.

Announced at GTC Berlin, the NVIDIA OpenShell Reference Architecture with support from AI cloud ecosystem partners. Built on the foundation of NVIDIA OpenShell secure runtime software, the architecture is designed to give organizations full-stack control to deploy AI agents with clear boundaries around what agents can access and do.

NVIDIA Vera Rubin NVL72 for model inference, NVIDIA Vera CPUs with OpenShell for agent harnesses and tool use, and NVIDIA BlueField-4 DPUs running NVIDIA Sentry for in-silicon enforcement. Each play a distinct role in delivering that control, working together so agents can achieve what they’re meant to do without moving beyond the boundaries people set for them.

Partners including Aleria, Baseten, Cassava, CoreWeave, Crusoe, DigitalOcean, Firebird, Fireworks, Firmus, G42, GMI Cloud, IREN, Lambda, Lightning AI, Nebius, Nscale, Radiant Verda and Vultr are supporting the OpenShell Reference Architecture to enable secure agentic deployments for their customers.

Together, European AI clouds are connecting regional infrastructure investment with the work of building and deploying AI. Expanded capacity gives builders more places to run workloads. Model and inference services help them put that capacity to use. Agent controls address what applications are allowed to do as they move into production.

Explore NVIDIA Cloud Partners for infrastructure and services, and the OpenShell Reference Architecture for agent deployment controls.

To increase productivity, accelerate innovation and create new opportunities for economic growth, Europe’s industrial leaders are tapping NVIDIA CUDA-X and Omniverse libraries, as well as NVIDIA Cosmos and Nemotron open models, across the engineering and manufacturing lifecycle. 

Bringing those capabilities together on the factory floor, the NVIDIA Factory Operations Blueprint (FOX) is now available in early access on GitHub, providing a path for developers to build and deploy always-on factory manager agents that coordinate work across machines, and specialist agents focused on use cases such as visual inspection, root cause analysis, report generation and fleet management.

These agents can connect to frontier models, including NVIDIA Cosmos and Nemotron, and foundational knowledge including computer-aided design, product lifecycle management, simulation software, and IT and operational technology systems. 

Building on the FOX blueprint enables the ecosystem to build industrial agents that monitor factory operations in real time, correlate visual findings and machine logs to investigate problems, and coordinate responses. With Cosmos in-context learning, these agents can adapt using examples and documentation, without fine-tuning.

Siemens is integrating agents built using FOX into Intelligence Center X, its agentic enterprise system, to help identify root causes and coordinate factory operations, for estimated cost savings of 20%. Siemens’ Process Preparation X agents use NVIDIA Cosmos Data Factory tools and skills to generate defect images for circuit-board inspection. At its Erlangen electronics factory, Siemens used this approach to reduce calibration time of inspection processes by 40%.

Accelerating Engineering From the First Design

NVIDIA CUDA-X libraries accelerate simulation, giving engineers faster feedback to explore design alternatives before committing to physical prototypes. At GTC Berlin, NVIDIA is releasing cuDSS for general availability, accelerating the demanding calculations behind the complex simulations of structural stress, fluid flow, electromagnetic fields and semiconductor device behavior.

Mercedes-Benz uses Siemens’ Simcenter STAR-CCM+ running on NVIDIA GPUs to compare vehicle designs through faster aerodynamic simulations. In factories, the company applies NVIDIA Cosmos 3 and the Metropolis VSS blueprint to build agents for vehicle inspection and windshield assembly verification, detecting deviations and ensuring consistently high quality. 

Cadence is benchmarking automotive fluid-dynamics workloads on NVIDIA GPUs to assess opportunities for faster design iteration, while Autodesk brings the NVIDIA PhysicsNeMo and Warp frameworks into NavPack, integrated with Autodesk Alias, to help designers like those at Bentley’s predict vehicle performance and iterate faster using simulation-trained AI.

imec and Infineon are accelerating semiconductor simulation with Synopsys software and NVIDIA accelerated computing. Benchmarks of S-Litho on imec’s model show up to 40x speedup, while Infineon uses Sentaurus Device with NVIDIA CUDA libraries to achieve a 12x speedup in 3D breakdown simulation for next-generation power devices.

Leonardo is working with Neural Concept on AI-driven aircraft design, tapping NVIDIA Omniverse libraries to bring aircraft geometry and aerodynamic simulation results together.

Car manufacturers use Dassault Systèmes’ LEO & AURA Virtual Companions, powered by NVIDIA Nemotron, and orchestrating CATIA AI Agents & SIMULIA AI Agents with SIMULIA solvers, accelerated by NVIDIA CUDA-X libraries, to rapidly design and virtually test vehicle doors before building physical prototypes.

UniversalAGI is working with Saab to protect sailors from life-threatending wave impacts at sea, using UniversalAGI’s Latent Interaction Field Transformer physics AI foundation models with accelerated processing by NVIDIA Warp to evaluate thousands of wave conditions and headings in real time instead of days. These capabilities enable vessels to anticipate dangerous motion and autonomously select safer routes to avoid putting sailors at risk, while NVIDIA Omniverse libraries help visualize vessel behavior at sea.

PhysicsX is working with GB1, Britain’s America’s Cup team, on AI-powered yacht design, using NVIDIA Omniverse libraries to visualize hull geometry and pressure fields, while Fermioniq’s Sofia agent helps Deltares and Dutch ministries model river flow by translating natural-language requests into simulation solvers built on NVIDIA Warp.

Mistral plans to use CUDA-X, Warp and Omniverse libraries to accelerate physics simulation and generate training data for engineering AI models.

Testing Factories and Robots Virtually

NVIDIA Omniverse and Isaac libraries enable digital twins and robotics simulation, helping manufacturers evaluate production systems and robot behavior before making changes on the factory floor.

Audi uses a battery-production digital twin built with NVIDIA Omniverse libraries to test production concepts before physical equipment is available. The company is also working with Dassault Systèmes and Agile Robots to simulate and validate mobile humanoid robots and  logistics processes in DELMIA virtual twins built with Omniverse libraries.

Schaeffler is integrating digital twins of its humanoid actuators — components that move robot joints — with NVIDIA Isaac simulation frameworks, helping its robotics partners evaluate component behavior in simulation and reduce physical testing.

At Volkswagen Sachsen’s Transparent Factory, Wandelbots uses NOVA with Isaac Sim, built on Omniverse and OpenUSD, to validate assembly workflows digitally and prepare applications for physical robots.

Falkor, a KONGSBERG company, is integrating agents and NVIDIA Omniverse libraries into its platform. With Microsoft Azure NCv6 virtual machines powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, agents with spatial intelligence combine engineering data, operating information and 3D equipment geometry to see, simulate and reason over industrial facilities, supporting inspection and maintenance use cases across the process and energy industries.

The same simulation approach extends to AI infrastructure. Schneider Electric, together with AVEVA, is developing an AI factory digital twin built for Nscale with Omniverse libraries to help engineers compare power, cooling and equipment configurations before making physical changes.

Explore NVIDIA technologies for industrial AI.