At this year’s SIGGRAPH conference, running through Thursday, July 23, in Los Angeles, attendees can discover how leading graphics research, neural rendering, simulation and AI are transforming how worlds are created and understood by people and machines.

The NVIDIA keynote, taking place today, July 20, at 3:45 p.m. PT, will feature NVIDIA AI research and engineering leaders Neil Ashton, Edward Liu and Ming-Yu Liu discussing neural rendering techniques, world models and simulation methods for AI, built by AI.

Read on for the latest from the SIGGRAPH conference, with NVIDIA and partners showcasing:

• Model Context Protocol connections bring agentic AI to content creation
• New Synthetic Video Detector NIM microservice
• Cosmos 3 Edge open world model for local physical AI
• NVIDIA NemoClaw on DGX Station with NVIDIA Agent Toolkit
• Research breakthroughs for virtual worlds and physical AI

AI Agents Expand Creative Tools to Millions

Leading creative applications are opening Model Context Protocol (MCP) connections that let AI agents work inside the tools where scenes, shots, timelines, assets and edits come to life — while creators stay in control.

For more than two decades, NVIDIA technologies — from GPU-accelerated viewports and CUDA-powered effects to NVIDIA RTX PRO ray tracing, AI denoising, neural rendering and real-time simulation — have helped accelerate the DCC tools that artists, studios and developers use to build the world’s games, films, television shows and advertising content. 

MCP is opening the next chapter of accelerated creativity: applications aren’t just getting faster. They’re becoming agent-ready.

From Acceleration to Action

With MCP-connected tools, an artist or technical director can ask an agent to inspect a scene for missing textures, flag inconsistent color management, prepare export variants, generate playblasts for dailies or validate a shot against pipeline rules, all while keeping creative decisions in human hands.

The same NVIDIA platform that accelerated viewports, rendering, simulation and AI effects can now power local agents, model inference and multi-application workflows on systems designed for professional creators.

NVIDIA RTX PRO workstations, DGX Spark and DGX Station systems are designed to bring accelerated AI performance closer to artists, developers and studio pipelines. Running models and agents locally can help improve responsiveness, reduce reliance on external services and keep sensitive creative data in controlled environments.

NVIDIA Agent Toolkit also supports MCP integration, including an MCP client for connecting to remote MCP servers and an MCP server for publishing tools to any MCP client.

The Creative Ecosystem Goes Agent-Ready

Across the creative ecosystem, creative applications and platforms are exposing MCP connections or MCP-ready workflows, giving AI agents more grounded access to real production context.

Adobe is expanding its creative agent across Firefly, Express and Creative Cloud, powering AI Assistant experiences that enable creators to describe the outcome they want while the assistant orchestrates multistep workflows. Adobe is also bringing its pro-grade creative tools to third-party AI platforms through the Adobe connector, extending its creative capabilities wherever people create and work. For developers, Adobe provides the Adobe Express Developer MCP Server, enabling AI coding assistants to build Adobe Express add-ons using official documentation and application programming interfaces (APIs).

Affinity by Canva has introduced an AI Connector for Claude that uses MCP to bring natural-language automation directly into Affinity. Designers can ask Claude to handle repetitive production tasks such as renaming layers and artboards, resizing and reformatting assets for multiple channels, applying bulk edits, optimizing vector paths and preparing files for delivery. Beyond individual tasks, Claude can also help users build reusable scripts and custom features tailored to their workflows, reducing production overhead and giving creative professionals more time to focus on design.

Blender offers a lightweight MCP server through Blender Lab, providing a natural-language interface to Blender’s Python API, documentation and complex setups. For independent artists and studios, Blender offers a strong example of how open creative tools can become agent-accessible without changing the creative center of gravity.

Boris FX Silhouette now includes an MCP server that lets AI assistants work directly inside your projects. Using Silhouette’s FX Scripting API as first-class MCP tools, assistants can inspect projects, build node trees, edit shapes and keyframes, and render frames. A new preferences panel simplifies setup by installing the MCP package, generating a ready-to-paste client configuration, and testing the connection. Interactive online mode connects to your active session, while offline mode runs headless instances for automation, batch processing, and large-scale workflows. 

Foundry Griptape natively supports MCP, providing AI orchestration specifically designed for professional VFX pipelines. This integration enables studios to securely manage multiple AI models and agents while maintaining the necessary traceability and creative control. By integrating with tools like Blender and Foundry Nuke, Griptape automates repetitive production tasks — such as cleanup, matte painting and quality control — all while ensuring artists remain in final command of the creative process.

SideFX is bringing MCP support to Houdini 22 through its new APEX Script workflow. AI assistants can access a curated collection of APEX Script syntax, functions, documentation and examples, helping artists generate and refine code for procedural character rigs. SideFX’s initial implementation focuses on APEX Script and character rigging, while community-developed MCP servers offer broader ways for agents to interact with Houdini.

Unreal Engine recently announced the ability to connect AI clients to Unreal Editor through MCP, enabling AI workflows that can interact with editor capabilities through a standardized protocol. For game developers, virtual production teams and real-time artists, this opens the door to assistants that can reason over scenes, assets and project state.

See how NVIDIA RTX PRO and DGX systems bring local AI agents closer to creative work at SIGGRAPH.

NVIDIA AI for Media Helps Newsrooms Detect Synthetic Video

Every day, video brings the world’s biggest stories into view — from breaking news across continents, to events reshaping communities, to moments that unite people across the globe. In a news cycle that moves around the clock, trustworthy video is the medium through which people see what’s happening, understand why it matters and stay connected and up to date.

For that reason, public trust in video is more essential than ever. At SIGGRAPH, NVIDIA announced the Synthetic Video Detector NVIDIA NIM microservice, part of the NVIDIA AI for Media platform, to bring an AI-assisted detection signal into editorial and media workflows. 

The NIM microservice analyzes video frame by frame to produce a classifier score of whether it contains synthetic content. Editorial teams can use that score to prioritize clips for review, flag or quarantine questionable footage, or escalate it for deeper analysis.

Rather than replacing established verification practices, the microservice provides another signal for time-sensitive decisions — helping teams move quickly while protecting editorial standards and ensuring public trust.

Synthetic Video Detector remains effective after the compression, resizing, cropping and re-encoding steps common in newsroom and social-video workflows. In NVIDIA testing, the model’s accuracy reached up to 92% on uncompressed video, 87% at 15% compression and 82% at 50% compression.

The NIM microservice can process 1080p video in as little as 22 milliseconds on NVIDIA RTX systems and approximately 30 milliseconds on NVIDIA L40 GPUs. 

Deploy Detection Where Video Lives

Organizations can deploy the NIM microservice closer to where sensitive video is captured, stored or distributed, including in on-premises, edge, hybrid and approved air-gapped environments. This flexibility helps teams maintain control over video data, access and operations.

Partner adoption is already helping move Synthetic Video Detector from model capability to deployable media infrastructure. Wowza is embedding the microservice through the Wowza Video Intelligence Framework, bringing real-time synthetic video detection into livestreaming workflows used across more than 35,000 deployments in over 170 countries. 

That scale matters because many of the organizations most exposed to synthetic media risk, including broadcasters, government agencies, financial institutions and critical infrastructure operators, also face strict requirements around data residency, security and operational control. 

By pairing Synthetic Video Detector with a video infrastructure layer customers already use, Wowza can help make AI-assisted verification available closer to ingest and streaming operations, allowing teams to flag questionable video in real time while keeping sensitive footage inside their own environments.

Try the NVIDIA Synthetic Video Detector NIM microservice.

See notice regarding software product information. 

Now Openly Available, NVIDIA Cosmos 3 Edge Brings Frontier World Models to Edge GPUs for Local Physical AI

Physical AI systems rely on world models to perceive, reason over and predict the physical environment. But the real world is vast, unpredictable and always changing. 

Whether a robot navigating a warehouse or a camera network monitoring a factory floor, physical AI systems need to understand what’s happening now, reason about what may happen next and act quickly enough to have an impact. Until now, delivering such frontier AI at the edge has often meant trading model capability for deployment efficiency. 

Now available, NVIDIA Cosmos 3 Edge helps eliminate that tradeoff. The 4-billion-parameter omnimodel is optimized for memory-efficient deployment and high throughput on NVIDIA Jetson, NVIDIA RTX PRO and NVIDIA DGX systems, as well as GeForce RTX GPUs.  

Extending NVIDIA Cosmos 3, the compact world foundation model can understand and generate text, image, video, ambient sound and action. Its mixture-of-transformers architecture enables physically grounded, real-time vision analytics and robot action on device. 

Cosmos 3 Edge delivers frontier physical AI at the edge — ranking No. 1 on VANTAGE-Bench for vision analytics success in its parameter class and enabling state-of-the-art robot learning through post-training. 

On-Device Physical AI Across Robotics, Autonomous Vehicles and Smart Infrastructure

Developers can post-train Cosmos 3 Edge on proprietary robot and sensor data using the NVIDIA DGX Station deskside AI supercomputer to build specialized world action models, then deploy them on NVIDIA Jetson Thor for real-time robot control policies including for manipulation or locomotion. Agile Robots, Doosan Robotics, Siemens and Skild AI are among the partners that are evaluating Cosmos 3 Edge for robotics workflows.

For autonomous vehicles, Cosmos 3 Edge supports road-scene understanding, traffic reasoning, object-intent prediction and policy-model distillation on resource-constrained hardware. The model could be used as a student backbone for automotive policy model distillation, including with NVIDIA Alpamayo vision language action models.

For smart infrastructure, Cosmos 3 Edge enables best-in-class throughput and accuracy with real-time inference on Jetson Thor for vision agents that reason across live video streams for traffic monitoring, public safety, logistics and industrial inspection. Developers can also run the 2-billion-parameter NVIDIA Nemotron-powered reasoning module independently on NVIDIA Jetson Orin 8GB. Centific, Vaidio and YUAN are evaluating Cosmos 3 Edge to accelerate vision agents running at the edge.

Cosmos Platform Now Openly Available

Cosmos 3 Edge is part of the broader NVIDIA Cosmos platform for developing physical AI world models. With Cosmos 3 available in Edge (4B), Nano (16B) and Super (64B) sizes, developers can choose the right model for each stage of development, from edge deployment to high-fidelity generation. 

Cosmos 3 Edge, Cosmos 3 Nano and Cosmos 3 Super are available now on Hugging Face, with inference and post-training frameworks and recipes on GitHub.

AI Agents Made Easy: Build and Run Personal AI Agents Locally on DGX Station With NVIDIA Agent Toolkit

Super agents have arrived on the desktop. NVIDIA DGX Station is the ultimate deskside supercomputer for the AI era, and with NVIDIA Agent Toolkit, setup takes just three steps, and the system can be running in roughly 30 minutes.

On DGX Station, NVIDIA Agent Toolkit brings together NVIDIA NemoClaw, the NVIDIA Nemotron 3 Ultra open model, NVIDIA Omniverse libraries as agent-accessible tools and skills, and a secure runtime in a single local system — no internet required. As workloads scale, developers can connect multiple systems together to serve concurrent users, more agents and bigger models.

This gives creatives and engineers the ability to own their own intelligence, with a system that comes ready to run locally. The full stack stack — model, agent, tools — provides a platform for creating and running domain-specific “super agents” that are customized with users’ own data and knowledge. 

The open NVIDIA Agent Toolkit stack on DGX Station includes:

• NVIDIA NemoClaw, open blueprints for building custom autonomous agents, packaging the model, harness and runtime together as a starting point for teams building specialized, domain-specific agents.
• NVIDIA Nemotron 3 Ultra, a frontier 550-billion-parameter open model, is optimized to run on DGX Station GB300 systems and serves as the model layer that teams can customize for their own domains.
• NVIDIA Omniverse libraries extend agent skills into physics simulation and 3D asset workflows, giving creative and engineering professionals tools that go well beyond general-purpose agent capabilities.
• NVIDIA OpenShell, the open source secure runtime, keeps agents sandboxed and governed according to defined policies for how agents interact with tools, systems and data.
• NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip delivers data-center-level performance from the desk on DGX Station, with up to 20 petaflops of FP4 AI compute and 748GB of coherent memory to run large models such as Nemotron Ultra.
• NVIDIA ConnectX-8 SuperNIC delivers up to 800GB/s of bandwidth in DGX Station, delivering extremely fast, efficient network connectivity, and supports linking up to two DGX Stations to further scale model capacity and performance.

Harness Efficiency at Scale

For teams running agents at scale the economics shift fundamentally on DGX Station. Nemotron 3 Ultra, tuned for an open harness, delivers leading-edge performance without the per-token cost after the hardware purchase, so users build once and can run as much as they need. 

NVIDIA has announced a blueprint for integrating NVIDIA Omniverse libraries in Blender — giving NemoClaw agents callable RTX sensor simulation and physics tools to prepare 3D scenes for physical AI workflows. 

On DGX Station, designers and engineers can run the core pieces of that workflow — frontier model, open harness, secure runtime, 3D tools — in one box, all connected and deployable through an open blueprint.

Frontier models can orchestrate NemoClaw as a specialized sub-agent, delegating domain-specific work to an agent running locally on DGX Station, with direct access to Omniverse tools and Blender. 

LangChain tuned its Deep Agents harness for Nemotron 3 Ultra, giving designers and engineers a production-ready path to benchmark-leading agentic performance at a fraction of the cost. 

Nous Research fine-tuned Nemotron 3 Ultra for its Hermes Agent harness and adopted it for production workloads — a direct demonstration of the value of owning intelligence. Tuning the model for a developer’s stack enables agents that are both faster and more capable for specific domains. Hermes Agent has also added Blender to its Model Context Protocol catalog, letting teams activate Blender directly from their agent — a live example of a tool-using NemoClaw agent that can run on DGX Station. 

For teams running OpenClaw, this stack extends what’s possible — bringing Nemotron 3 Ultra, Omniverse tools and local inference on DGX Station into an environment where OpenClaw’s persistent, long-running agents can act on them continuously. 

NVIDIA DGX Station is built and available to order from ASUS, Dell Technologies, Exxact, GIGABYTE, HP, MSI and Supermicro. 

Get started with NVIDIA NemoClaw and Nemotron Ultra on DGX Station.

NVIDIA Brings Graphics Research Breakthroughs to Simulation and Physical AI

At SIGGRAPH this year, NVIDIA’s research isn’t just focused on creating worlds that look real — but that behave realistically and respond in real time.

That shift is the throughline across NVIDIA’s 21 accepted technical papers — becoming the foundation of real-time systems that generate virtual worlds and drive machine training in the real world. 

Whether the output is a game, film, robot or factory digital twin, the goal is the same: expand the canvas of creativity with AI-generated worlds that are grounded in 3D, governed by physics and directed by creators.

The clearest proof is in MotionBricks: a real-time motion model — trained on more than 350,000 motion clips, running at game-engine speeds — that lets creators direct and connect character movements. The same model that drives the animated character on screen drives a Unitree G1 humanoid robot in the room, using computer graphics and simulation to accelerate physical AI development.

GPC, a framework for training generative controllers on large-scale motion datasets, extends that idea. NVIDIA pretrains a single controller on large-scale human motion, giving it transferable motor skills that carry over to new tasks. Think of it as the start of a foundation model for motor control.

To build virtual worlds in which to test these movements, ArtiFixer turns messy real-world 3D captures into clean, complete virtual scenes. It also includes a new method for predicting photoreal global illumination straight from a scene’s geometry — without tracing a single ray. 

To make those virtual worlds behave as they would in the real one, a new solver brings hard-to-simulate materials — such as snow, sand and elastic solids — to life inside the NVIDIA Newton physics engine. 

And to keep creators in control, the VideoNeuMat pipeline gives them reusable, relightable materials to pull out of generative video models, while the ARDY autoregressive diffusion model lets them steer 3D character motion in real time from a text prompt.

The papers linked above are openly available, with the code and models free to download. Learn more by joining NVIDIA at SIGGRAPH.

Erin Davis calls it the “SuperDuperPOD.” That’s two things in one name: pharmaceutical giant Bristol Myers Squibb (BMS) already runs one of the largest AI clusters in life sciences, with serious results to show for it. And they’re doubling down.

BMS announced today it is deploying its second NVIDIA DGX SuperPOD, this one built on eight DGX Vera Rubin NVL72 systems — the most powerful and energy-efficient AI cluster in life sciences.

“Instead of equipping a small group of researchers with access to the  supercomputer, we’re opening it up to literally every scientist,” says Davis, vice president of research business insights and technology at BMS. “No one has to wait, and no one is told they have a limit.”

The eight rack-scale systems, each comprising NVIDIA Vera CPUs and Rubin GPUs, deliver up to 10x the performance per megawatt of the infrastructure it replaces. It will give researchers at the global pharmaceutical giant access to a unified AI platform — including NVIDIA BioNeMo Agent Toolkit for biological AI — for running predictions, training models and powering agentic workflows across the full drug discovery pipeline.

What Davis and other top BMS researchers are really after is what that access makes possible: faster cycles, bigger chemical spaces and a full drug discovery pipeline where researchers think about the science, not the logistics of lining up resources.

The mandate, says Payal Sheth — a scientist who spent her career inside drug discovery labs before taking on an expanded role in January as senior vice president of therapeutic discovery sciences at BMS — is moving from “sort of this abstract position of what AI can do to actually translating that to measurable impact.”

BMS has operated a DGX SuperPOD for about three years, producing meaningful results. AI-enabled target identification already saves scientists weeks of manual work, freeing time to focus on the highest-value scientific decisions. BMS’s team has used AI to expand its library of CELMoD compounds — molecules engineered to selectively degrade cancer-causing proteins, with applications in blood cancer treatment and beyond. This has opened the door to new targets and new potential medicines across a wider range of diseases. AI is also applied in lead optimization stages of drug discovery using a methodology Sheth calls “Predict First,” which informs experimental gating based on design predictions.

“We use predictions as a way to prioritize synthesis of molecules with multi parameter optimization,” she explains, “to weed out molecules that wouldn’t necessarily meet the property landscape we’re working towards. This ensures precious laboratory experiments are aligned with progressing molecules that have the highest probability of success.

These research AI applications have significant impact on compute needs across the research organization.“We’re saturated,” Davis says. “We’re in production with some very large-scale predictions around large molecules. We’re building our own foundational models, and that takes a lot of GPUs.”

With the new system coming, Davis already has her pitch for researchers thinking about where to do their best work: “Welcome to Limitless Compute.”

A computational chemist by training, Davis spent years doing the science before concluding the technology wasn’t keeping up — and that she’d rather go fix it. She spent roughly 15 years on the vendor side, building enterprise platforms at ChemAxon, Schrödinger and X-Chem. At every company, pharma was wrestling with the same bottleneck.

“It’s not the technology,” she says. “The challenge is how to get that into the hands of actual scientists and learn from it.”

She knows what’s at stake personally. Her father died five years ago, she says, “a very horrible death of Alzheimer’s.” BMS has a significant investment in brain health — a notoriously hard area. “Even if he was still going to die,” Davis says, “if there was symptom remediation along the way, it would have saved suffering for everybody in the family. Dementia is especially cruel.”

Davis’s team is combining the existing DGX SuperPOD and the new DGX Vera Rubin NVL72-powered system into a unified environment — a single data plane, accessible from every BMS site globally. 

Barriers that made the earlier system hard to reach — site-specific restrictions left over from past acquisitions, the need for deep computational expertise — are being replaced with AI-native tooling managed through NVIDIA Mission Control. Researchers will be able to initiate complex predictions in plain English.

“The compute infrastructure is what connects all of our scientists together and ensures that our learnings are institutionalized,” Sheth explains. Datasets from a program run in Lawrenceville, New Jersey, feed models that a team in San Diego, California, can draw on. The learnings “can be applied in context of any program we work on.”

“There’s a cumulative learning loop today in drug discovery that did not exist when I first started my career,” Sheth explains. “Every project was treated differently, and there were discrete sets of learnings that did not compound into any kind of intelligence framework within discovery.”

Today, BMS is using AI to expand that learning loop into a discovery system where every experiment, clinical readout, and partnership compounds into higher-conviction scientific decisions, faster. 

Agentic workflows can further enhance the architecture of R&D.

“Agents don’t care,” Davis says. “They go all across. And that is a huge game-changer because now we can learn from decisions across the silos and across programs.”

“When you as a scientist can go to an army of well-vetted, fully trained virtual scientists that have BMS knowledge baked in now you’re a whole team in and of yourself.”

Human instincts, Sheth says, aren’t replaced, “they’re augmented with more quantitative insights and predictions.” The ability to scale that with compute, she says, “is where the excitement of the impact of AI is going to be fully realized.”

“You still have to have that human brain driving things,” Davis adds, “still looking for caveats and gotchas, still teaching them how to utilize knowledge. But this takes up the capabilities of individual humans substantially.”

Davis says the new system has a plan already mapped to it: a detailed allocation across modalities, from small and large molecule design to clinical applications to digital twins. “We didn’t just buy this to have the biggest compute,” she says. “The SuperDuperPOD is basically at every node along the way.”

When BMS Chief Digital and Technology Officer Greg Meyers asked Davis whether she was sure she could even saturate the SuperDuperPOD, her answer was direct.

“Just give us time,” she told him.

Featured image credit: Bristol Myers Squibb

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

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

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

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

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

Agentic Post-Training Demystified

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

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

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

Why Intelligence per Dollar Extends Cost per Token 

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

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

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

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

Maximizing Intelligence per Dollar: Post-Training Nemotron 3 Ultra

NVIDIA Nemotron 3 Ultra — an open weight, 550-billion-parameter mixture-of-experts (MoE) model, offers verifiable benchmarks and a fully disclosed post-training recipe run on NeMo RL. It scored 71.7% on a standard real-world coding benchmark, SWE-bench verified, where it produced a working fix for roughly seven in 10 real software bugs from open source projects, each one checked against the project’s own tests.  

The NVIDIA Blackwell platform lowers cost per run and makes the frequent post-training the agentic era demands economically viable. That intelligence is reaped across every token served.

The NVIDIA Vera Rubin platform extends the trajectory further, training the largest models with one-fourth the GPUs of the Blackwell generation. It was codesigned from end to end to maximize intelligence per dollar for the agentic post-training load: more rollouts per run, more environments in play and post-training cycles that never stop.

Post-Training Workflows in Action

Prime Intellect’s Lab continuously post-trains frontier open models on NVIDIA Blackwell and uses NVIDIA Dynamo for inference orchestration. With Vera Rubin, Prime Intellect plans to scale reinforcement learning environments, generate more rollouts per run and accelerate training-to-inference iteration loops to maximize intelligence per dollar for businesses.

Prime Intellect has optimized its sandbox infrastructure to integrate with NVIDIA Vera CPUs, enabling low-latency, energy-efficient reinforcement learning. Open source tools and models such as NVIDIA Nemotron and NVIDIA NeMo Gym are also integrated into its software stack. When comparing realistic RL sandbox workloads against alternative x86 architectures, Prime Intellect found that Vera delivers, on average, 30% greater throughput per CPU.

Perplexity’s RL post-training stack runs asynchronously across hundreds of NVIDIA GPUs, with an RDMA-based weight transfer engine that syncs trillion-parameter models in under two seconds between training and inference compute nodes. The resulting post-trained Qwen3 235B models are then served on NVIDIA GB200 NVL72 systems.

Together AI provides post-training as a service, including supervised fine-tuning, RL and direct preference optimization. The service is delivered via a feature-rich application programming interface and software development kit that supports the full range of post-training on its AI Native Cloud platform. It has been running on NVIDIA’s platform and optimized kernel libraries, and is looking to harness the Vera Rubin platform next.

Learn more about NVIDIA Vera Rubin, the platform for AI factories to maximize intelligence per dollar across workloads. And explore NVIDIA’s full-stack platform for training frontier models.

Onimusha: Way of the Sword is coming to GeForce NOW at launch, with the playable demo available this week. It’s joined by Denshattack! rolling in with five new games arriving in the cloud.

Plus, GeForce NOW officially launches in India, moving from beta to public availability — meaning gamers can sign up without a waitlist.

Pick Up the Oni Gauntlet

Capcom’s next legendary action adventure is coming to GeForce NOW. Onimusha: Way of the Sword will arrive on the cloud at launch on Thursday, Sept. 3. 

Members can jump into the action even sooner with the demo now available to stream.

Set in a dark fantasy vision of Edo Japan, follow a lone samurai wielding the legendary Oni Gauntlet against relentless demonic Genma. Master precise swordplay, unleash powerful abilities and absorb the souls of fallen enemies as supernatural battles unfold across a haunting world filled with mystery and danger.

Ultimate members can experience every clash with GeForce RTX 5080-class performance in the cloud across PCs, Macs, handhelds, mobile devices and TVs. From the first swing in the demo through launch day, GeForce NOW lets members step into battle the moment it’s available — no downloads, storage space or expensive hardware required.

Gamers can find the demo in the GeForce NOW app demo row and add the title to their wishlists today to be ready when the blade is drawn. 

India Levels Up

GeForce NOW has officially exited beta in India, bringing the world’s highest-performance cloud gaming service to more gamers across the country. 

This week, gamers can choose from monthly Performance and Ultimate memberships, flexible day passes or the basic, free offering — making it easier than ever to experience GeForce RTX-powered cloud gaming.

GeForce NOW now supports UPI payments, giving gamers across India a fast, secure and convenient way to purchase memberships and day passes. 

Premium memberships unlock GeForce RTX-powered features like ray tracing, NVIDIA DLSS and NVIDIA Reflex, along with higher resolutions, faster frame rates and priority access to streaming. With thousands of supported games — from blockbuster releases and free-to-play favorites to indie hits — members can enjoy PC games they already own without the cost of upgrading to high-end gaming hardware.

Join the conversation with the GeForce NOW community on Reddit. 

Go Off the Rails This Weekend

Denshattack! — the off-the-rails, train‑wrecking action game — is now available to stream on GeForce NOW, letting members embrace pure chaos from almost any device. 

Take control of runaway trains and send them barreling through crowded tracks, explosive obstacles and delightfully destructive layouts, chaining spectacular crashes with just the right mix of precision and pandemonium. Its colorful, tongue‑in‑cheek presentation and physics‑driven mayhem turn every run into a highlight reel of derailments — perfect for quick sessions, score‑chasing and laughing at just how wrong a single turn can go when the throttle never lets up.

In addition, members can look for the following to play this week:

• Denshattack! (New release on Steam and Xbox, available on Game Pass on July 15)
• The Mound: Omen of Cthulhu (New release on Steam, available July 15)
• Heave Ho 2 (New release on Steam, available July 16)
• Fogpiercer (New release on Steam and Xbox, available on Game Pass on July 17)
• Onimusha: Way of the Sword DEMO (Steam)

The GeForce NOW community keeps discovering new games to play, with members discussing the most beautiful games on the service.

What game has impressed you most on GeForce NOW? Let us know on X or in the comments below.

General-purpose robots and autonomous machines are moving from research labs to real-world mass-market deployment, creating demand for compact, power-efficient AI supercomputers capable of running foundation models at the edge. 

To meet that need, NVIDIA today introduced the T3000 and T2000, new modules based on the NVIDIA Thor architecture that enable mass-market robotics and edge AI applications at scale.

Jetson AGX Thor is powering this next generation of humanoid and robotic systems, with growing adoption across industries. Leading companies — including 1X, Agile Robots, Amazon Robotics, Boston Dynamics, FANUC, Hitachi and Techman Robot — are building on the platform.

Unlocking Humanoid and Robotics Deployment With T3000

The hardware underpinning those capabilities starts with the Jetson and IGX T3000 modules, which delivers 865 FP4 teraflops of AI compute in a compact form factor roughly half the size and power of the T5000. Jetson T3000 combines an NVIDIA Blackwell GPU, an eight-core Neoverse Arm CPU, 32GB of LPDDR5X memory and 273GB/s of memory bandwidth, along with 25 GbE connectivity. IGX T3000 delivers the same performance with integrated functional safety while seamlessly running the NVIDIA Halos for Robotics full-stack safety system for robots operating alongside humans.

Despite its smaller footprint, the T3000 achieves similar inference performance of the T5000 for multimodal workloads, including large language models, vision language models, vision language action models and world foundation models. Migrating to T3000 helps reduce costs amid high memory prices. 

Going Wide on Edge AI With T2000

The Jetson T2000 brings Thor architecture to a broader range of edge AI systems. With 400 FP4 teraflops of compute and 16GB of memory, it provides an entry point for developers building visual AI agents, autonomous mobile robots, industrial manipulators and other intelligent machines.

With the introduction of the new NVIDIA Jetson modules, NVIDIA now offers a scalable edge AI platform spanning performance from 70 TOPS to 2,000 teraflops, enabling developers to address virtually any edge AI workload.

New Agent Skills Automate Memory Optimization Across All Jetson Devices

AI agents are transforming developer productivity by automating memory optimization, system configuration and deployment tasks that previously required manual effort and deep domain expertise.

With the newly released Jetson agent skills, developers can optimize the entire software stack and achieve significant memory savings in days instead of weeks. These skills support the entire Jetson portfolio, including Jetson Thor and Jetson Orin, enabling developers to run more capable workloads on lower-memory configurations. 

The result is lower system cost, faster deployment and the flexibility to move down one memory SKU within the same product tier without compromising performance.

Companies across industries and regions have accelerated development while achieving substantial memory savings through software optimization.

Humanoid robotics leaders including UBTech and Agile Robots, along with industrial solutions provider Connect Tech, have reduced memory usage by up to 15GB, enabling them to move from NVIDIA Jetson AGX Orin 64GB to the 32GB module.

In smart retail, SandStar reduced memory usage by up to 4GB, enabling deployment on the NVIDIA Jetson Orin NX 8GB module instead of the 16GB configuration. In companion robotics, GROOVE X, creator of the LOVOT robot, uses Jetson’s heterogeneous AI accelerators to optimize workload distribution, reducing memory usage and enabling deployment on lower-memory configurations. 

In intelligent transportation, NoTraffic reduced memory usage by 30% on Jetson TX2 NX, creating headroom to add more AI capabilities into its smart traffic platform without increasing hardware requirements.

With agent skills simplifying development and NVIDIA NemoClaw blueprints orchestrating intelligent agents, Jetson is an agentic-ready platform for physical AI, enabling advanced reasoning, autonomous decision-making and task automation at scale.

Delivering Cosmos 3 Edge to NVIDIA Thor Lineup

NVIDIA today expanded its NVIDIA Cosmos 3 frontier open world foundation model family — built as a robot foundation model for embodied systems — with a lightweight model compatible with NVIDIA Thor platforms. Cosmos 3 Edge is a 4-billion-parameter model helping embodied systems see the world, reason over it in real time, and predict and generate actions through on-device inference. Using the open Cosmos framework, developers can post-train Cosmos 3 Edge for specific embodiments and sensors in about a day — closing the sim-to-real gap — then deploy on Jetson Thor for real-time vision analysis and on-device robot policy.

Start Development Today With Emulation Mode

Sharing the same chip architecture and software stack in the NVIDIA Thor family, the new modules provide a seamless development path. Developers can begin building today using the Jetson AGX Thor developer kit available through channel partners and emulate the performance of T3000 and T2000 modules.

Using NVIDIA’s full physical AI software stack — including NVIDIA Isaac for robotics simulation and perception — alongside open models such as NVIDIA Nemotron, Cosmos 3 and Isaac GR00T, developers can accelerate the development of next-generation robots, autonomous machines and visual AI agents.

Developers can begin using T3000 emulation mode later this month with JetPack 7.2.1. Support for T2000 emulation mode will follow in a future release. The Jetson T3000 and T2000 modules are scheduled to become available in Q1 2027.

ADLINK, Advantech, AAEON, Aetina, Auvidea, AVerMedia, Connect Tech, ForeCR, JWIPC, NEXCOM Robotic Solutions, Realtimes, Seeed Studio, Twowin, TZTEK and YUAN are among other partners in the Jetson ecosystem already providing Thor-based solutions. Software partners such as Antmicro, Neurealm, REBOTNIX and RidgeRun will provide emulation and migration solutions for customers transitioning to the new modules.

As physical AI and embodied AI move toward mainstream deployment, the new NVIDIA Thor computers give developers a scalable foundation for bringing intelligent humanoids and autonomous machines into the real world.

Find a Jetson AGX Thor Developer Kit on the NVIDIA marketplace and start developing today.

Home to leading manufacturers, robotics pioneers, infrastructure builders and iconic gaming companies, of course, Japan is one of the world’s centers of AI — building across the full stack with NVIDIA technologies. This week NVIDIA and its partners in Japan are showcasing the AI ecosystem’s latest advancements. Check back here for updates.

This week, NVIDIA founder and CEO Jensen Huang met with Japan’s AI leaders and enthusiasts — together showcasing the future of technology in the nation.

Thursday, July 16, 11:00 a.m. PT

Huang Drops In on Build-a-Claw

The first thing Huang did after landing in Tokyo wasn’t a press conference or a CEO meeting. It was a surprise visit to a room full of builders.

Happo-en is a traditional Japanese garden in the heart of Tokyo — moss, still water, centuries-old trees — and Studio Koku is the modern event space built within it. On Wednesday afternoon, it was full of robots. 

At NVIDIA’s Build-a-Claw event, Japan’s developers had been putting physical AI through its paces — using open models and NVIDIA’s platform to build robots that can pick things up. 

When Huang walked in unannounced, what followed was the kind of moment that’s hard to engineer: a founder on the floor, talking to builders, watching the technology work.

“Forty years ago was the beginning of the PC revolution. Now, forty years later, instead of a personal computer, you can now have your own personal AI,” he said. “I’m happy that all of you are here to build your own claw, your own agent.”

Huang gave away two autographed NVIDIA DGX Spark personal AI supercomputers to winners of a lucky draw.

“With this computer, you can build your own personal AI,” he said. “This is a work of love of mine.” 

This is what NVIDIA’s physical AI platform looks like in practice: a claw that moves, a model that responds, a builder who made it happen. Japan’s open model ecosystem is a central part of NVIDIA’s global physical AI story, and Build-a-Claw showcased that story.

At a press event later that day, Huang spoke about Japan’s long leadership in global technology manufacturing — and why the nation is in an excellent position to apply that expertise to AI and robotics to create a new economic engine for the country.

“Japan has historically been very good at precision manufacturing and very large-scale manufacturing, but now we have AI,” Huang said to gathered reporters. “You can combine the two technologies and create robotics. The future of intelligent manufacturing, the future of robotics can now start.”

This combination can also help address Japan’s worker shortage, Huang added.

“With AI and robotics, you can augment the workers you have and increase national productivity,” Huang said. “AI is manufacturing intelligence. It’s manufacturing all the time, and it’s running all the time.”

Thursday, July 16, 11:00 a.m. PT

Supply Chain Leaders Key to Building AI Future for Japan and Beyond

Japan sits in an extraordinary position as AI brings a reset to industries across economies and the globe. That was the message NVIDIA founder and CEO Jensen Huang delivered Wednesday in Tokyo to developers, scientists, entrepreneurs and investors.

With world-leading manufacturing, mechatronics, physical sciences, quantum physics and more, Japan has the opportunity to fuse its strengths with the help of AI and build the future.

But the future needs a supply chain.  Japan’s is exceptional — and hungry.

At an izakaya in Tokyo’s Kanda district, Huang hosted more than 30 senior executives from 16 of Japan’s premier supply chain companies spanning semiconductor equipment, memory, materials, and electronic components.

Over many skewers (there might have been beer too) leaders from ADVANTEST Corporation, Tokyo Electron (TEL), KYOCERA Corporation, Mitsubishi Electric, Murata Manufacturing, Panasonic, Renesas Electronics Corporation, Sumitomo Electric Industries, TAIYO YUDEN, TDK Corporation, Kioxia Corporation, Mitsui, Asahi Kasei, Nittobo, Shin-Etsu Chemical and Shibaura discussed building that AI-led future for Japan and the rest of the world.

Thursday, July 16, 2:30 p.m. PT

CEOs Gather to Discuss Building Physical AI Into Japan’s Factories

At Tokyo restaurant Tonkatsu Fumizen, Huang sat down for lunch Thursday with the CEOs of Fujitsu, Kawasaki Heavy Industries, FANUC and Yaskawa — four companies building robots and industrial systems that run in factories across Japan and the world.

These are the companies that will move physical AI from national ambition into the manufacturing floor. Fujitsu brings enterprise scale and AI infrastructure. Kawasaki, FANUC and Yaskawa are the robotics backbone of Japanese industry. Each is now building on the NVIDIA platform. Each is announcing its commitment to that path. 

“Just before coming into this venue, the five of us enjoyed a wonderful tonkatsu lunch together,” said Takahito Tokita, the president and CEO of Fujitsu Limited, at a press event that followed. “Although we come from different countries and industries, we share the same values. We make business decisions not only for the benefit of our own companies, but also with the sustainable development of our industries and ultimately the world in mind.”

Thursday, July 16, 2:30 p.m. PT

Japan’s Physical AI Initiative — A National Commitment

Prince Park Tower sits in the shadow of Tokyo Tower — the city’s most recognizable landmark. It was the right place for a national commitment. 

At the Physical AI Initiative kick-off event, NVIDIA CEO Jensen Huang joined Ryosei Akazawa, Japan’s Minister of Economy, Trade and Industry, as the country launched a government-backed physical AI initiative.

Bringing together manufacturing expertise, real-world industrial data and global technology leaders, the initiative will develop open multimodal foundation models for AI agents, digital twins, robotics and physical AI applications.

Thursday, July 16, 2:30 p.m. PT

Japan’s AI Startup Ecosystem, Together in One Room

Later, Huang moved to Happo-en — meaning “beautiful from all angles” — a garden that has hosted gatherings of consequence for centuries. 

There, an NVIDIA Japan AI ecosystem celebration brought together startups, partners, government officials, policymakers and press for Huang’s remarks and a startup showcase. 

Huang and Japan’s Minister of Education, Culture, Sports, Science and Technology offered a toast. 

The room held the full arc of Japan’s AI ecosystem — from early-stage founders building on NVIDIA’s platform to the institutions shaping Japan’s AI policy.

Friday, July 17, 10 a.m. PT

SoftBank and NVIDIA Advance AI‑Native Network Collaboration

During his visit, Huang met with SoftBank Corp. President and CEO Junichi Miyakawa and the company’s leadership team to align on the next stage of their work on AI-native networks and physical AI in Japan. 

SoftBank showed how it’s already using NVIDIA’s full stack — from GB200-class AI infrastructure and NVIDIA RTX PRO-based AI RAN with the NVIDIA AI Aerial platform to NVIDIA Nemotron-based large telecom models — to turn its communications network into an intelligence delivery network. 

The companies also confirmed joint initiatives to drive Japanese innovation using open models such as NVIDIA Nemotron, together with SB Intuitions’ homegrown generative AI model series Sarashina, while advancing physical AI with partners such as Yaskawa Electric Corporation using NVIDIA Cosmos and Isaac GR00T.

Wednesday, July 15, 4 p.m. PT

Japan’s Leaders Advance Healthcare and Life Sciences With NVIDIA Agentic and Physical AI

Japan built the world’s most trusted names in medical technology and biopharma. Now the country’s healthcare leaders are engineering the next generational leap with AI, powered by NVIDIA.

From autonomous surgical robots to AI-accelerated CT systems, and from agentic drug discovery platforms to virtual cell models, Japanese innovators are deploying NVIDIA technology to reshape medicine at every level. 

Agentic AI Accelerates Japanese Drug Discovery

Japan’s pharmaceutical leaders are uniting around AI-powered drug discovery. Tokyo-1, the AI drug discovery consortium and platform operated by Xeureka, continues to expand, with Eisai joining this past April, bringing together leading pharma companies —  Astellas, Daiichi Sankyo and Ono Pharmaceuticals — all advancing drug discovery using NVIDIA BioNeMo.

Astellas has deployed nearly all BioNeMo NIM microservices within NVIDIA’s digital biology portfolio and is running BioNeMo Agent Toolkit, NVIDIA’s open platform that turns any AI agent into an autonomous life sciences scientist. It gives AI agents, software platforms and biopharma systems immediate access to NVIDIA’s full life sciences stack. 

Ono Pharmaceuticals is using the Boltz-2 NIM microservice to streamline and accelerate internal drug discovery. Daiichi Sankyo is conducting ultralarge-scale virtual screening on Tokyo-1 and leveraging NVIDIA RAPIDS to accelerate large-scale data processing. Xeureka is using NVIDIA BioNeMo to power its AI-driven drug discovery efforts, enabling researchers the flexibility to use the most appropriate models and tools across diverse discovery programs.

SyntheticGestalt announced two products: the molecular AI foundation model ZAO and the molecular generative model KOYA. ZAO is a foundation model that converts small molecules into data AI can use, through a “4D” representation that captures the multiple 3D conformations a molecule actually adopts; as a single general-purpose model, it ranked No. 1 on nine public drug-discovery benchmark tasks, achieving the world’s best performance. 

KOYA is a molecular generative model that designs novel, high-affinity ligands for a target protein while closely reflecting the user’s intent. Both products can be called from the NVIDIA BioNeMo Agent Toolkit, enabling AI agents to carry out everything from evaluating molecules to designing them, and to accelerate drug discovery in collaboration with researchers.

Biomy is pioneering a virtual cell foundation model with a massive clinical dataset from the Japanese Foundation for Cancer Research. Using NVIDIA single-cell RAPIDS, Biomy achieved 90% faster spatial transcriptomics analysis. Biomy will use NVIDIA Nemotron-powered agents to autonomously propose and orchestrate complex virtual experiments for drug development. 

Takeda recently announced a collaboration with Boltz to deploy the BoltzMol-1 and BoltzProt-1 biomolecular models across its research organization, giving scientists tools for structure prediction, affinity estimation and generative design that integrate into existing discovery workflows. NVIDIA accelerates these models through NVIDIA BioNeMo with libraries such as cuEquivariance. 

Physical AI Enters the Operating Room

Kawasaki Heavy Industries provides technology designed to improve the overall efficiency of hospital operations, including with its FORRO, Nyokkey and NURABOT robots.

The company plans to use NVIDIA Holoscan IGX, Isaac for Healthcare, Isaac GR00T and Cosmos to develop surgical support functions, nursing assistant and hospital transport robots.

Direava is developing a surgical vision language model for real-time surgical video understanding and natural language interaction with surgical scenes. Direava aims to evolve this technology into an intelligence layer for future surgical AI and physical AI in the operating room. 

NVIDIA Accelerated Computing Powers Japan’s Next-Generation CT

Two of Japan’s leading medical imaging companies are now shipping next-generation CT systems built on NVIDIA GPUs. 

Canon launched Japan’s first NVIDIA-accelerated photon-counting CT system, marking a step forward for the country’s next generation of medical imaging.

Fujifilm has commercialized Japan’s first whole-body CT system powered by NVIDIA Blackwell, using diffusion-based deep learning reconstruction to improve image quality.

The integration of AI and accelerated computing into medical imaging equipment contributes to improved image quality, enhanced accuracy, early detection and higher standards of medical care.

Together, these advances signal a new era: AI, and not just accelerated computing, is no longer an experiment in Japanese healthcare. It’s infrastructure. 

Wednesday, July 15, 4 p.m. PT

NVIDIA Metropolis Provides Developers Agent-Ready Libraries to Build NVIDIA Cosmos-Powered Vision AI Agents Faster 

As enterprises capture more video data across the physical world, vision AI is transforming beyond passive perception and dashboards into agentic systems that can understand, reason and act in real time. Powered by reasoning vision language models (VLMs) such as the NVIDIA Cosmos of open models, these agentic systems extract rich insights from video, whether on operations, environmental context or root causes for issues.

Building production-ready, high-accuracy vision AI agents can require thousands of developer hours across data collection, model training, validation and deployment. NVIDIA Metropolis now packages more than 80 new skills, including NVIDIA VSS Blueprint 3.2, NVIDIA DeepStream 9.1, NVIDIA TAO 7 and Physical AI Data Factory, that help developers use coding agents to speed that process by at least 6x.

Japan’s industrial and smart-space leaders including Asilla, AWL, Fujitsu, Hitachi, OMRON, Shimizu Corporation and Yazaki North America are using Metropolis to bring vision AI agents into factories, construction sites, stories, buildings and public spaces.

Metropolis Open Libraries and Skills Span the Vision AI Lifecycle

Metropolis provides a comprehensive set of open libraries and skills that span the entire vision AI development lifecycle, from creating data pipelines to generating synthetic data, fine-tuning models and deploying agents at scale. 

New libraries include:

• NVIDIA VSS Blueprint 3.2 helps developers build and operate vision AI agents that can see, reason and act over live or recorded video using natural language. New skills for coding agents make it faster to build and operate custom, always-on video agents that alert, summarize and search across large camera networks.
• NVIDIA DeepStream 9.1 helps developers create and deploy real-time, multi-sensor video analytics pipelines from edge to cloud for large-scale ingestion, multi-camera tracking and operations analytics.
• NVIDIA TAO 7 helps developers customize and optimize NVIDIA Cosmos and other vision AI models with agent skills for labeling, performance diagnostics, fine-tuning, data generation and automated machine learning. 
• NVIDIA Physical AI Data Factory skills help developers use NVIDIA Cosmos to automatically generate and augment synthetic image and video data to fill training gaps for rare or new product defects, environmental changes and other edge cases, pushing vision model accuracy to new levels.

Companies Advance Agentic Vision AI With NVIDIA Metropolis

Japan-based companies are using the new NVIDIA Metropolis technologies to bring real-time intelligence to physical operations.

For industrial inspection and operations, OMRON is enhancing automated inspections with VSS-powered video analytics agents.

DeepHow is helping Yazaki North America automate time and motion studies, reducing the current process from weeks to days and unlocking millions of dollars in annual savings.

For smart spaces and public transportation, several Hitachi HMAX solutions use VSS-powered agents to identify issues and generate actionable insights in buildings and rail infrastructure. Fujitsu Kozuchi AI platform combines VSS with its Agentic Memory technology to transform long-duration video into operational knowledge, accelerating decision-making across manufacturing, logistics, retail and smart spaces. Meanwhile, Shimizu Corporation is piloting VSS for construction worker safety.

With DeepStream and VLMs, Asilla is monitoring public spaces and commercial facilities to detect incidents and improve response time, while AWL is building retail and manufacturing solutions with DeepStream.  

Developers can access NVIDIA VSS Blueprint 3.2 skills, NVIDIA DeepStream 9.1 skills and NVIDIA TAO 7 skills on GitHub. NVIDIA Physical AI Data Factory and synthetic data generation skills are available through GitHub and can be explored using Physical AI Launchables on NVIDIA Brev.

Wednesday, July 15, 4:00 p.m. PT

Japanese Megabanks Build Financial Intelligence With NVIDIA Nemotron and NVIDIA Agent Toolkit

Across Japan, leading banks and financial technology companies are building AI factories and models to deliver financial intelligence. NVIDIA Nemotron open models and NVIDIA Agent Toolkit are helping them turn regulated financial data into valuable intelligence.

In banking, the most powerful AI applications may not look like chatbots. They look like safer payments, smarter fraud detection, faster software development and more personalized financial services, all built on trusted data. 

Mizuho plans to build what is expected to be the largest on-premises AI factory in Japan’s financial industry, starting with NVIDIA DGX B200 systems and scaling toward a larger cluster. For a bank handling sensitive financial workloads, being on premises matters: it gives teams a foundation to develop agents with NVIDIA Agent Toolkit and NVIDIA NemoClaw blueprints, while keeping critical data close and secure.

With this secure foundation, Mizuho aims to safely expand the operational scope of these autonomous agents into core workflows, including information gathering, document creation, analysis and system development support, while ensuring rigorous governance and auditability. 

As the core IT company of SMBC Group, the Japan Research Institute (JRI) deployed an AI factory to transform financial data into intelligence using NVIDIA Nemotron open models. As one of Japan’s largest financial groups, SMBC Group’s adoption shows how open models and accelerated infrastructure can help established institutions move AI from experimentation into production-ready enterprise workflows. The initiative serves as a foundation for scaling AI adoption across the SMBC Group, improving productivity, accelerating innovation and delivering better financial services to customers.

Rakuten Bank brings digital-native scale to the same transformation. Using the Rakuten Group’s ecosystem, which spans more than 70 services and includes over 18 million banking accounts, 33 million credit cards and 14 million brokerage accounts, Rakuten Bank will develop transaction foundation models built with NVIDIA Agent Toolkit, helping turn high-volume consumer financial data into specialized intelligence for banking services.

Ippu Senkin is collaborating with a financial institution to build sovereign financial intelligence with NVIDIA Blackwell GPUs and Local AI Agent, a local coding agent developed by Ippu Senkin using NVIDIA Agent Toolkit, Nemotron and NemoClaw for secure payment operations within the institution’s group. The effort points to a broader ecosystem motion with AI-native partners helping financial services companies build local agents and applications that can run on local AI factories.

Japan’s financial services industry is moving from model pilots to AI infrastructure that can support regulated, domain-specific intelligence. Banks need performance, governance and proximity to data; digital banks need model-building capacity at transaction scale; and AI-native partners need a platform for local financial agents. 

NVIDIA provides a full stack across those paths, from accelerated computing and AI factory architecture to Nemotron open models and Agent Toolkit for building agents and specialized financial intelligence.

Learn more about how financial institutions are transforming financial data into intelligence with transaction foundation models.

Wednesday, July 15, 4:00 p.m. PT

NVIDIA Advances Japan’s World-Class Quantum and AI for Science Capabilities

NVIDIA is advancing a historic partnership between the U.S. and Japan, its first international partner in the Genesis Mission. 

Genesis Mission’s large-scale initiative to harness AI for scientific discovery calls on U.S. labs and industry, as well as international collaboration. 

NVIDIA and Japan are answering the call — from AI to quantum computing.  

NVIDIA and RIKEN Driving AI for Science 

At RIKEN, Japan’s leading national comprehensive research institute, two supercomputers driven by NVIDIA GB200 and NVIDIA Quantum-X800 are beginning operations. 

RIKYU, a new supercomputer for “AI for Science” development, deploying 1,600 NVIDIA Blackwell GPUs using the GB200 NVL4 platform, will support RIKEN’s development of open foundation models and contribute to accelerating AI adoption across broad fields, including life sciences, materials science and laboratory automation.  

JHPC-quantum GPU supercomputer “ROQUO” is a quantum-HPC system tightly integrating quantum processors with accelerated computing from 540 Blackwell GPUs accessed through the GB200 NVL4 platform. ROQUO is connected to on-premises quantum computers at RIKEN’s facilities in Wako and Kobe, Japan — including Quantinuum’s trapped-ion Reimei system, enabling hybrid quantum-HPC workloads. In ROQUO’s first months of operation, researchers are beginning to explore an evolutionary AI framework, developed with NVIDIA and integrated with the NVIDIA CUDA-Q platform for quantum-classical computing, to generate quantum circuits for the Reimei system. 

Building an Ecosystem That Brings AI to Quantum 

AI is the unlocking technology for scaling quantum processors into useful quantum-GPU supercomputers, but the adoption of AI in quantum computing workflows remains a key challenge.  

At the National Institute of Advanced Industrial Science and Technology’s (AIST) Global Research and Development Center for Business by Quantum-AI Technology (AIST G-QuAT), NVIDIA is working to bring state-of-the-art AI to the center’s current and future quantum processor systems. NVIDIA NVQLink provides the low-latency connection between GPUs and quantum processors, while NVIDIA Ising open models support automated QPU calibration and AI-based decoding for quantum error correction. 

Advancing Quantum Chemistry 

High-accuracy simulations of chemical systems are fundamental for next-generation research in areas such as materials science and drug discovery. AI approaches can expand what quantum algorithms are capable of, improving how these simulations scale. 

Mitsubishi Chemical, Mizuho Bank, Keio University, AIST, the University of Toronto and NVIDIA have demonstrated an AI- and GPU-driven workflow for harnessing quantum processors in molecular spectral analysis — a key tool for understanding the electronic structure and properties of molecules and materials. NVIDIA GPUs achieved a 13.4x speedup for this workflow over CPU-only nodes. Accelerating this analysis lets researchers apply it more quickly to early targets, like extreme ultraviolet photoresist for semiconductor manufacturing.

Developing useful quantum chemistry applications also means building workflows suitable for tomorrow’s large-scale hybrid quantum-GPU supercomputing systems. Fujitsu and NVIDIA are now investigating efficient ways to use NVIDIA CUDA-Q for large-scale quantum-chemistry simulation. Through the collaboration, Fujitsu has started the trial of NVQLink to determine if it can be utilized to realize efficient control of their quantum-classical hybrid computing environment.

Together, the U.S. and Japan are building on the NVIDIA platform to develop a shared foundation for useful, large-scale quantum computing and AI-driven science, and uniting industry, academia and government.

Wednesday, July 15, 4:00 p.m. PT

NVIDIA Expands Partnership With Toyota to Advance Physical AI Across Automotive, Robotics and Cities 

From self-driving cars to cities, the next era of mobility will be defined by AI-enabled systems that can perceive, reason and safely act in the physical world. Toyota and NVIDIA are working together to build that future — connecting AI across vehicles, infrastructure and industrial operations.

This builds on last year’s announcement that Toyota will develop next-generation vehicles with advanced driver-assistance capabilities (L2++) built on NVIDIA DRIVE AGX and running the safety-certified NVIDIA DriveOS operating system. 

NVIDIA has enabled Toyota to tap into NVIDIA accelerated computing, AI software and simulation technologies to develop safer, more intelligent vehicles, optimize automotive engineering workflows, fine-tune factory operations and power urban intelligence systems, in support of the company’s vision for safer mobility. 

“Physical AI will bring intelligence to every moving machine from cars, robots and trucks to the cities and factories they operate in,” said Rishi Dhall, vice president of automotive at NVIDIA. “Together, Toyota and NVIDIA are building the AI infrastructure for a new era of mobility, where vehicles can become more autonomous, manufacturing more AI-defined and urban environments more intelligent, responsive and safe.”

NVIDIA and Toyota’s latest work spans:

• Accelerating safe, intelligent vehicles: Toyota is building next-generation vehicles with advanced driver assistance capabilities using NVIDIA DRIVE AGX running the safety-certified NVIDIA DriveOS operating system. These vehicles will deliver L2++ functionality, enabling more intelligent, context-aware driving while maintaining Toyota’s rigorous safety standards.
• Software engineering: As vehicles become increasingly software-defined, Toyota is accelerating vehicle software engineering with a MISRA-compliant Code Assistant AI model, trained and fine-tuned using NVIDIA Megatron-LM, and referencing various datasets including NVIDIA Nemotron. By applying a custom automotive AI model to improve automotive-specific code generation and review, Toyota engineers can generate, review and validate safety-critical code more efficiently, accelerating development while adhering to stringent automotive compliance.
• Factory simulation: Toyota is bringing simulation to the manufacturing floor using NVIDIA Omniverse libraries and the NVIDIA Isaac Sim open framework for factory and robotics workflows, robot movement simulation and broader digital twin environments to optimize manufacturing operations. This simulation-first approach reduces downtime, improves efficiency, lowers costs and enables continuous optimization across production environments.
• Multimodal Vision Language Model: Woven by Toyota (a Toyota subsidiary) has developed a multimodal vision language model for urban traffic intelligence, using NVIDIA H100 Tensor Core GPUs and Megatron-Core. The model is designed to help interpret real-world conditions, anticipate what happens next and support responses across mobility and infrastructure systems. 

Wednesday, July 15, 3 a.m. PT

NVIDIA and SEGA Celebrate 30 Years of Innovation, Bringing ‘VIRTUA FIGHTER CROSSROADS’ and Other Legendary SEGA Games to NVIDIA RTX Spark

NVIDIA and SEGA are celebrating more than three decades of collaboration by bringing VIRTUA FIGHTER CROSSROADS and future SEGA titles to NVIDIA RTX Spark — a new superchip for slim Windows laptops and compact desktop PCs. 

This builds on the companies’ long-standing relationship, which began 30 years ago when NVIDIA worked with SEGA on burgeoning graphics technology for arcade systems and gaming consoles — with the NVIDIA NV1 chip powering the first Virtua Fighter title on PC, among the world’s first 3D fighting games.

SEGA will support RTX Spark, giving gamers new ways to experience SEGA’s iconic franchises, including the upcoming VIRTUA FIGHTER CROSSROADS. 

Announced from the heart of Akihabara, a global gaming technology hub, at the original SEGA Akihabara Arcade (now GiGO Akihabara 3), VIRTUA FIGHTER CROSSROADS coming to RTX Spark reinforces the companies’ commitment to innovation and shows a glimpse of the future of gaming on a new era of Windows PCs designed for personal agents, AI, creating and gaming.

NVIDIA founder and CEO Jensen Huang joined SEGA CEO Haruki Satomi; SEGA chief operating officer Shuji Utsumi; Yu Suzuki, creator of Virtua Fighter; and former SEGA President Shoichiro Irimajiri, at the birthplace of countless arcade memories to celebrate the milestone. 

They showcased how technology partnerships can evolve across generations of hardware and software, connecting the gaming industry’s heritage with its future. 

The expanding NVIDIA RTX Spark ecosystem — including SEGA and other industry leaders — will offer gamers new experiences harnessing NVIDIA ray tracing, DLSS and AI technologies, while preserving and celebrating the iconic franchises they know and love.

Learn more about NVIDIA RTX Spark.

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

Enterprises have plenty of powerful models to choose from. The real test is whether the AI an enterprise builds uniquely addresses the needs of the business: improving workflows, tapping into domain knowledge and exceeding standards for accuracy and trust. 

Increasingly, competitive AI advantage comes from how organizations build with available models, more than which one they choose. 

Open models like NVIDIA Nemotron are built for customization — helping enterprises and nations build AI that’s controllable, trustworthy and tailored to their needs. 

From Using AI to Owning Intelligence

Specialized AI, such as autonomous agents and applications, are built with customized open models. These agents are built to do a defined task well, as the models used are tuned on proprietary knowledge and evaluated against real business outcomes.

That requires access to the model itself. Closed models advance what’s possible and continue to push forward the frontier of general intelligence, but also set a ceiling on what enterprises can inspect, tune and improve. Open models remove that barrier — providing complete ownership and control.

The most effective agentic AI applications are systems of models where open models work alongside leading frontier models, each fulfilling the job it does best. High-performance reasoning models can handle complex planning while smaller models execute on specialized tasks. This lets enterprises right-size inference costs, improve accuracy on specific tasks and maintain flexibility as workflows evolve. 

Customization Enterprises Can Trust

Open models give enterprises something closed models cannot: full control to customize, inspect and improve AI against business needs. Public benchmarks measure general capability — but business-specific evaluation lets teams test against their own data, workflows and definition of accuracy — then improve from there.

For example, the cost of a wrong answer is high for industries like healthcare and legal, where teams handle sensitive data and face strict accuracy requirements. Organizations in these sectors must have visibility into how a model was trained, how it performs and the ability to improve it when necessary. 

With open models, teams can inspect their applications, run private evaluations against their own criteria and stand up reinforcement learning environments tuned to their own workflows. No routing of their proprietary data through a third party is required.

Companies across industries are already specializing Nemotron for their domains:

• Abridge is customizing Nemotron to build the first foundation model purpose-built for clinical conversations.
• Glean built Waldo, an agentic search model that pairs Nemotron with larger closed models to deliver enterprise search at significantly lower latency and with fewer tokens.
• H Company built Holotron 3 Nano by post-training Nemotron 3 Nano Omni on proprietary computer-use data, achieving higher than 76% accuracy on OSWorld-Verified — a benchmark on computer tasks — and matching other leading frontier models at a fraction of the cost.
• Harvey post-trained Nemotron 3 Ultra on its legal benchmark and reached frontier-class accuracy — matching leading closed models on complex legal tasks at at least 10x lower cost per run.
• Heidi Health is delivering frontier-quality outcomes in clinical documentation without needing frontier-scale compute.
• YTL AI Labs post-trained a Nemotron model for the Malaysian language, putting locally customized AI in the hands of Malaysia’s developer community to further its AI capabilities.

Fine-Tuning Environments and Optimal Run Costs

Customization improves accuracy. When models are tuned for a specific harness or domain, they run more efficiently too. 

The NVIDIA NeMo suite of open libraries accelerates model customization and evaluation, in addition to agent optimization and governance. 

Partners like Prime Intellect and Unsloth are already enabling AI customization for enterprises building post-training pipelines on Nemotron, making it practical to run specialized AI at scale. 

LangChain tuned its Deep Agents harness for Nemotron 3 Ultra — adjusting prompts, tools and middleware, with no model retraining — and achieved top agent accuracy among open models at approximately 10x lower cost per run than leading closed alternatives.

Those cost advantages extend to infrastructure for optimal scalability. By post-training Nemotron on the NVIDIA Blackwell platform, Arcee AI achieved inference costs of roughly 90 cents per million output tokens — approximately 20x cheaper than comparable closed frontier models — while ranking second on PinchBench and remaining fully open weight.

Cost savings enable broader experimentation, more deployments and faster iteration.

Ecosystem Building on an Open Foundation

The shift from AI adoption to AI ownership is underway. The NVIDIA Nemotron Coalition is helping turn open model development into an ecosystem effort, bringing model builders and developers together to improve Nemotron through shared data, evaluations and domain expertise. In addition, hackathon submissions and community contributions generate reusable proof assets across industries.

Builders are adding Nemotron to their AI systems, proving value and sharing what works. The foundation is entirely open.

Learn more about NVIDIA Nemotron open models and try them at build.nvidia.com.

Power is AI infrastructure’s inescapable constraint. How many tokens an AI factory can generate within a fixed power budget determines its revenue and profitability. Because of this, performance per watt — a metric that can’t be gamed, only earned through real-world results — is the foundation for AI factories. 

As agentic AI drives token demand higher, the infrastructure decisions organizations make today will determine who scales and who doesn’t in a power-constrained world.

Virtually every frontier AI model today runs on a mixture-of-experts (MoE) architecture. Serving these large-scale models efficiently means GPU domain size — the number of GPUs connected over an ultrafast, scale-up interconnect — matters, and bigger is better. 

While the NVIDIA Hopper generation set the standard with an eight-GPU domain, the scale of frontier AI today has outgrown it. Serving MoE with a 72-GPU domain demands full-stack codesign and the operational depth earned from running these models under real production load.

With the NVIDIA Blackwell NVL72 platform, that foundation is already built and proven, delivering the highest performance per watt to maximize revenues and the lowest token cost to maximize profit margins. It’s this foundation that the NVIDIA Vera Rubin platform builds upon next to further elevate rack-scale energy efficiency.

Maximizing Performance per Watt for Frontier AI 

Each new generation of frontier models brings architectural changes that unlock greater intelligence while demanding new optimizations to run efficiently at scale. 

Across the newest generation of leading open models, NVIDIA GB300 NVL72 delivers up to 25x performance per watt compared with the NVIDIA Hopper generation — showcasing that MoE performance improves when moving from an 8-GPU to 72-GPU domain size. These numbers reflect where Blackwell stands today, a starting point that continues to improve. 

Any single number only tells part of the story. Different workloads demand different operating points: some optimize for latency, others for throughput and cost — and most need to move between the two. 

To best represent these operating points, NVIDIA showcases Pareto curves for each model rather than a single point and provides tools such as DynoSim to help teams find their optimal point on the Pareto frontier before spending a single GPU-hour on validation.

The performance per watt NVIDIA Blackwell delivers is a result of extreme codesign: every component of the rack-scale system, from silicon to software, designed together to maximize token throughput for AI inference workloads. That codesign touches every layer of the stack.  

For example, NVIDIA NVLink Switch, critical for rack-scale performance, is purpose-built to unlock massive scale-up GPU domains, not adapted from general-purpose networking. Now in its sixth generation with the Vera Rubin platform, its capabilities are designed specifically for AI workloads such as SHARP, which performs in-network computing directly in the switch, offloading work from the GPUs themselves.

NVIDIA’s inference software stack, including NVIDIA Dynamo and TensorRT LLM, as well as SGLang and vLLM, is built to run the full range of optimizations: NVFP4 quantization, disaggregated serving, large-scale expert parallelism, KV-aware routing, KV cache offloading and more. These stack together to multiply the performance each GPU delivers. Moreover, software keeps improving performance over time: On DeepSeek V4, performance per watt improved by up to 5x in a single month.

In AI factories, power lost to cooling and rack-level inefficiencies can mean only about 60% of the electricity pulled from the grid turns into useful AI work. NVIDIA DSX MaxLPS, the power-and-efficiency software in the NVIDIA DSX platform, closes that gap by shifting power between GPUs and racks in real time, supporting warm-water liquid cooling and using techniques like power steering to wring more performance. This enables operators to run up to 40% more GPUs within the same power budget.

Production Is Where It Counts

Rack-scale reliability at AI factory scale is hard-won. Rack-scale systems introduce failure modes that single-node deployments never encounter, and handling them requires engineering rigor and time in production.

NVIDIA Blackwell NVL72 systems continues to set the standard across a diverse range of models and production use cases delivering sustained performance, rack-level reliability and economics that hold under real traffic day after day. 

That’s why leading AI labs such as Anthropic, OpenAI and SpaceXAI use NVIDIA Blackwell NVL72 systems to run inference.

In addition, a variety of inference service providers and AI natives use the Blackwell platform to deploy open models in production.

CoreWeave has deployed Kimi K2.6 on NVIDIA GB300 NVL72, combining NVFP4 quantization and EAGLE3 speculative decoding to maximize inference performance. 

Perplexity runs Qwen3 235B and post-trained Qwen3.5-397B-A17B on NVIDIA GB200 NVL72 for its AI agent platform, serving millions of queries daily with the latency and reliability that consumers need.

Fireworks AI deploys GLM 5.2 on the NVIDIA Blackwell platform, enabling production deployments for customers including Cursor and Factory AI.

This accumulated production experience, built across generations of frontier models and real-world deployments, is what gives NVIDIA Vera Rubin its head start.

Learn more about the NVIDIA Vera Rubin platform in this technical blog and find details on the NVIDIA DSX AI factory-scale platform and DSX MaxLPS.

This GFN Thursday brings more games, more power and more ways to play on GeForce NOW. 

The cloud gaming service is expanding with a new GeForce RTX 5080-powered server in Toronto, bringing dedicated high performance in the cloud closer to members across the region.

NTE: Neverness to Everness also gets an update in the cloud, making it even easier to jump into the latest content from the supernatural adventure without a single download or more storage space needed. It leads the way for GeForce NOW bringing native touch control to the game, coming soon.

There’s even more to explore with three new games joining the GeForce NOW library this week. 

Take It to the Next Level in Toronto

The forecast is looking cloudy in Canada.

A new GeForce RTX 5080-powered GeForce NOW server is coming to Toronto, expanding service in the region and bringing dedicated cloud gaming performance closer to local members. The new server will roll out within days, giving more players access to top-tier cloud gaming across Canada.

Ultimate members can stream across PCs, Macs, handhelds, mobile devices, TVs and more with GeForce RTX 5080-class power in the cloud. Enjoy up to 4K resolution and beyond on supported ultrawide displays, up to 120 frames per second, plus NVIDIA DLSS, ray tracing and NVIDIA Reflex technologies. 

Bring on the ‘999 Nights’

Step into the surreal world of NTE: Neverness to Everness with the NTE Version 1.2 “999 Nights” update. 

This version introduces a massive gameplay evolution, plunging players into an immersive, tabletop-inspired fantasy role-playing game on the Warren Continent — a new permanent game mode featuring its own dedicated progression system.

This narrative and mechanical expansion is elevated by the debut of two powerful characters, Shinku and Iroi, alongside an unprecedented fashion upgrade featuring a sweeping collection of 19 new character outfits.

To top it off, exploration gets a high-octane upgrade with Draco, a revolutionary new motorcycle vehicle, making this version an absolute playground for combat strategy, stylish customization and high-speed urban traversal. 

Plus, GeForce NOW will soon be rolling out native touch controls to NTE: Neverness to Everness, which will make it even easier to explore the city’s mysteries from supported mobile devices.

Look for the game in the GeForce NOW app to seamlessly jump between devices and continue the adventure — no downloads, storage space or expensive additional hardware required.

Embark on Expanded Adventures

The journey continues in Granblue Fantasy: Relink with the Endless Ragnarok expansion. Known for its dynamic combat, diverse roster of Skyfarers and thrilling online co-op, Relink returns with fresh story content as mysterious beings known as the Ragnalia threaten the Zegagrande Skydom. Strange gateways, powerful new foes — including the mighty Beelzebub — and fresh challenges await across the skies.

The expansion also introduces additional ways to play, including summon abilities that add another layer of combat strategy, co-op quest tiers and a solo mode filled with unpredictable encounters. Master traits offer more opportunities to customize favorite characters, giving both longtime Skyfarers and newcomers plenty of reasons to take flight.

Jump into the latest action with the following games to play this week:

• Esports Manager 2026 (New release on Steam, available July 6)
• Assassin’s Creed Black Flag Resynced (New release on Steam and Ubisoft Connect, available July 9)
• Granblue Fantasy: Relink – Endless Ragnarok Demo (Steam)

As GeForce NOW continues to expand, so does the community discovering cloud gaming.

One new Ultimate member summed up their first impressions:

“Finally caved and got GFN Ultimate… and wow.”

Another longtime PC gamer shared how GeForce NOW completely changed their perspective after years of skepticism, calling it “a good alternative” for casual players and encouraging others to “try the one month and upgrade to the yearly before the sale ends.”

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

NVIDIA Nemotron 3 Ultra is offering leading performance at lower cost than top closed models with the largest and most widely adopted AI agent orchestration platform. 

LangChain tuned its Deep Agents harness for NVIDIA Nemotron 3 Ultra, achieving the highest accuracy among open models, while completing more tasks at higher throughput and running at 10x lower inference cost per run than leading closed models. 

Measured against LangChain’s Deep Agents benchmark, Nemotron 3 Ultra also achieved business task parity with the highest-scoring closed models. No model retraining was required. Every gain came from engineering the environment around the model, not the model itself. 

At a tenth of the cost, teams harnessing NVIDIA Nemotron 3 Ultra can run evaluations continuously, experiment faster and build specialized agents across more of their business. 

LangChain’s agent engineering platform has more than 200 million monthly downloads. By tuning its Deep Agents harness specifically for NVIDIA Nemotron 3 Ultra, it allows for high-performing agents that complete more tasks, run faster and give enterprises a fully open stack they can customize, own and run anywhere.

“The way to build better agents is to keep improving the system around the model,” said Harrison Chase, cofounder and CEO of LangChain. “Memory, tool use, evaluation and model behavior compound when teams can tune them together. Our work with NVIDIA shows that enterprises can get strong performance from an open stack while keeping control over the agent systems they are building.”

Abridge, Amdocs and Box are embedding specialized agents directly into their platforms and global systems integrator EY is expanding its NVIDIA implementation capabilities around NVIDIA NemoClaw blueprints for LangChain Deep Agents, helping clients customize, evaluate and govern specialized agents across high-value workflows. 

NVIDIA founder and CEO Jensen Huang recently sat down with Chase to discuss why the last six months have seen a leap in useful AI for enterprises.

Harness Engineering, Not Fine-Tuning

LangChain’s team ran Nemotron 3 Ultra against its public Deep Agents benchmark suite, then analyzed the deep agent’s execution traces to find exactly where it lost points. Instead of retraining the model, the team tuned the harness around it — adjusting system prompts, tool descriptions and middleware.

Every developer using LangChain Deep Agents with Nemotron 3 Ultra can put this to work today — the tuned profile is available directly through LangChain.

An Open Stack Built to Own

NVIDIA NemoClaw for LangChain Deep Agents is the open reference blueprint that packages this work for enterprises building their own specialized AI — systems of models, tools and runtime — tuned for their own workflows. It combines LangChain Deep Agents Code, tuned for Nemotron 3 Ultra, with the NVIDIA OpenShell secure runtime for executing agent actions safely.

An open model, an open harness and an open secure runtime means enterprises own the full stack, end to end. They can customize it around the expertise that sets their business apart, keep improving it and run it anywhere — their own infrastructure, their own cloud, their own governance. 

That distinction matters more as agents take on higher-stakes work. The shift from AI assistants that answer questions to agents that take action inside core systems changes what businesses get from their AI. 

NemoClaw for LangChain Deep Agents and the tuned Nemotron 3 Ultra model profile are available now. Developers can pull the tuned Deep Agents harness directly from LangChain, or use the NemoClaw for LangChain Deep Agents blueprint as a starting point for building specialized agents from scratch. 

How to Get Started

LangChain developers can access Nemotron 3 Ultra on Baseten, Crusoe Cloud, DeepInfra, Fireworks, Nebius and Together AI  platforms, giving them a direct, hosted path to the tuned harness in production.

EY can help enterprises start building their own specialized agents today, using this open software stack.  

Learn more about NVIDIA NemoClaw for LangChain Deep Agents and NVIDIA Nemotron. 

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

Explore self-paced video tutorials and livestreams.

Max single-threaded CPUs at scale are a new category of CPUs built for the agentic AI era. 

Across the creation and deployment of an agentic system, the CPU is on the critical path for reasoning, response time and learning. CPUs are the processor which executes the work the AI model commands: the tool calling, code execution, data processing, KV-cache and result analysis. 

For agents in AI factories, speed matters. 

The faster the CPU can run the tool, the faster the agent can perform the task at hand. 

For the AI factory, the utilization of GPU is the most valuable resource in the data center so any time waiting for a task to complete constrains the revenue of an AI factory — or worse, impacts the GPU utilization waiting for the CPU to finish its task. AI factories need a CPU with max single-threaded performance to maximize AI factory revenue and agent performance.

Today’s data center CPUs are not designed for speed at scale. 

While the world has fast CPUs for PCs and workstations, data center CPUs have been evolving in directions away from single-threaded performance. The advent of the cloud has pushed CPU makers to build higher core-count CPUs while minimizing cost at the expense of performance.  

Building CPUs that optimize costs per rentable core increased the number of cores per chip while taking away silicon area from what makes those cores run fast — like high-performance memory fabrics and faster instruction processing per core. The move to chiplet architectures further reduced cost but created a “chiplet tax” where each CPU’s cores can no longer can get access to the full memory performance of the chip.

AI agents need a CPU designed for max single-threaded performance at scale.

A max single-threaded CPU at scale keeps each agent step fast while the system is fully loaded. Every core completes the agent task at full performance without other cores slowing it down. Max single-threaded CPUs at scale are designed differently to deliver:

• Strong performance per core under load
• Enough memory bandwidth per core to keep active cores supplied with data 
• Predictable latency  

Every core can finish its task without any other core slowing it down, delivering excellent throughput and, more importantly, the fastest possible single-core task performance possible.

NVIDIA Vera exemplifies this new class of CPU design. 

How Max Single-Threaded CPUs at Scale Are Built to Run the Agentic Loop

An AI agent doesn’t stop running after a single request. It acts in a loop. The model reasons about the next step. The CPU executes the work around the model. The result comes back. The model decides what to do next. Then the loop runs again. 

That pattern creates a demand profile for which conventional CPUs were not optimized. Traditional CPU work is intermittent and user-driven, made up of short interactions triggered by people. Agentic work is persistent and parallel: swarms of agents running continuously, each advancing through a chain of steps where each step depends on the result of the one before it.

More cores in a CPU means more agent tasks per CPU, and data center CPUs need lots of cores to maximize throughput of tasks.

However, adding more cores to a CPU cannot shorten the time for each step inside a single agent loop. More cores can’t make any one task run faster. In fact, CPUs designed to maximize core count can even slow down the performance of each core as they contend for resources.   

Individual per-core performance matters to drive the speed of each step’s completion. The throughput of additional cores is useful but insufficient. And since each action is dependent on the previous result, per-core speed determines how fast the loop advances.

In the end, the best agentic CPU needs the best single-threaded performance per core, and every core needs to deliver that performance without compromise. The world counts in seconds. Agents count in nanoseconds. NVIDIA Vera is built for this new category — and speed — of work.

NVIDIA Vera Is the Max Single-Threaded CPU at Scale for Agents

NVIDIA Vera is a max single-threaded CPU at scale, designed from the ground up for the agent loop: the work that happens between model calls as agents use tools, process data, run code and check results.

At the core of Vera is Olympus, NVIDIA’s custom CPU core, which delivers 50% higher instructions per cycle than NVIDIA Grace. That matters because many agent steps are sequential. A tool call, code execution, test run or data-processing step must finish before the next model call can use the result. Faster cores move each loop forward faster.

Vera pairs those faster cores with up to 1.2TB/s of LPDDR5X memory bandwidth at less than 40 watts of memory power, plus a monolithic compute die that helps active cores stay fed and keeps data movement predictable with 3.4TB/s of core-to-core bandwidth, 3x greater than any other data center CPU. This enables all 88 cores with the full memory performance of the CPU without creating bottlenecks that slows down every core.

The result is faster agent loops. In loaded CPU workloads that represent agentic execution, Vera delivers 1.8x the sustained per-core performance of x86.

Those gains compound across tool calls, code executions, data-processing steps and verification passes, helping AI factories complete more agent work with the GPUs they already operate.

Perplexity tested Vera on the agentic work it runs every day. Running a real coding workflow — cloning a repository and running its test suite in sandboxes — Vera completed the job about 1.5x faster than x86, and started concurrent sandboxes up to 1.9x faster. Perplexity is now looking to deploy Vera in its upcoming production system. 

Agents also depend on data. They query, retrieve, filter and move information constantly, and Vera runs those CPU-side data workloads faster. Partners have measured 3x faster large-scale SQL analytics with Starburst and up to 6x lower latency on real-time streaming with Redpanda, both against leading x86 server CPUs.

Agent work isn’t one workload. An agent runs tools and sandboxes, processes data, serves requests and trains the next model with reinforcement learning — and all of it leans on the same strengths.

One Vera handles the whole range, rather than requiring a different CPU for each kind of work. And because Vera is the same CPU that hosts the GPUs in NVIDIA Vera Rubin and powers the NVIDIA BlueField-4 STX storage processor, the whole AI factory runs on one architecture and one toolchain.

And NVIDIA’s not done. NVIDIA’s next-generation Rosa CPU with the Rigel core will continue the company’s CPU roadmap for the agentic AI era. Rigel is NVIDIA’s next-generation Arm v9.2 CPU core, delivering higher per-core performance than Olympus while keeping the same silicon footprint. Key improvements include better instruction delivery, a larger L2 cache and more efficient memory handling.

Built for the Speed of Agents

In the agentic AI era, there will be billions of agents, and every one of them will turn to a CPU to act, check, retrieve, execute and verify. In this new market, completed agent work is the product. Faster agent loops help every GPU spend more time generating revenue producing work and less time waiting.

NVIDIA Vera is the CPU built for that future.

Learn more about the NVIDIA Vera CPU.

Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated by costly and fragmented resources, from large datasets and robot foundation models to simulation, compute and validation tools.

NVIDIA and Hugging Face are collaborating to bring the NVIDIA Isaac GR00T 1.7 open, reasoning vision language action (VLA) model for humanoid robots and the NVIDIA Isaac Teleop framework to LeRobot — Hugging Face’s open source library for robotics — with NVIDIA Cosmos 3, a frontier model for physical AI, planned soon. Together, these integrations give developers a more accessible and standardized path for end-to-end robot development while driving innovation and collaboration across the open robotics community.

“Open source is how a field turns advanced research into something people can study, adapt and build on,” said Thomas Wolf, cofounder and chief science officer at Hugging Face. “With NVIDIA Isaac GR00T 1.7 and Isaac TeleOp in LeRobot today, robotics developers can use shared models, data and workflows to train and evaluate robots in the open. And with NVIDIA Cosmos 3 planned next, the community will have a path to bring frontier world models into that same collaborative loop.” 

An Open Pipeline for Robot Foundation Models

Hugging Face LeRobot is an open source robotics library for training, running and sharing robot datasets, models, policies and workflows. NVIDIA’s continued partnership with Hugging Face connects NVIDIA’s 3 million robotics developers with Hugging Face’s 16 million AI builders, expanding access to frontier physical AI tools through open workflows.

Bringing NVIDIA physical AI capabilities into LeRobot gives developers a common way to collect and standardize data, train and fine-tune robot foundation models, evaluate performance and deploy models through open workflows. The integrations include:

• NVIDIA Isaac Teleop, an open source framework for robot data collection, helps developers capture high-quality human demonstrations from external devices using standardized, interoperable formats, then expand and share datasets with the community, all directly in LeRobot. 
• NVIDIA Isaac GR00T 1.7, the first open and commercially viable robot foundation model, makes it easier to post-train and deploy models through LeRobot workflows, helping developers adapt GR00T to new robot embodiments and tasks with benchmarked performance.
• NVIDIA Cosmos 3, a frontier world foundation model for physical AI coming soon to LeRobot, will help developers generate and augment robotics data, simulate scenarios and support policy development when real-world data is limited or too expensive to collect.

These integrations build on a broader set of NVIDIA resources already connected to LeRobot to support the full robotics development loop, including: 

• The largest open source physical AI dataset, downloaded more than 15 million times, which includes more than 350,000 real and simulated trajectories and 57 million grasps to help developers kickstart their robotics workflows.
• NVIDIA Isaac Sim– and Isaac Lab-based simulation frameworks to help developers set up environments, generate robot data, test policies and validate behaviors before moving to physical robots.
• NVIDIA Isaac Lab-Arena in LeRobot Environment Hub to enable developers to quickly prototype complex simulation environments, register them in LeRobot EnvHub and seamlessly use them within the LeRobot ecosystem to train and evaluate generalist robot policies such as GR00T, Pi and SmolVLA.
• NVIDIA Jetson Thor integration with LeRobot’s Reachy 2 to support deployment of VLA models on open source humanoid robots.

Learn more about how to use Isaac Teleop, Isaac GR00T 1.7 and Isaac Lab-Arena with LeRobot for end-to-end humanoid development and explore detailed LeRobot integration workflows.

Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. 

This year’s accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done.

NVIDIA had 74 papers accepted at ICML 2026. Approximately 2,000 accepted papers cite NVIDIA GPUs, and 145 cite NVIDIA Nemotron — a family of open models, including open datasets — as the foundation for new research. Hundreds more draw on NVIDIA Cosmos, NVIDIA Isaac GR00T, BioNeMo and other NVIDIA open model families, spanning physical AI, robotics, autonomous vehicles and biomedical research.

The Themes Defining This Year’s Research

Areas including vision and video generation, reinforcement learning for large language models (LLMs) and agent training as well as AI inference remained prominent themes across this year’s papers, reflecting sustained investment these fields command — while several new areas also broke through.

Robot world models drew significant attention, with papers like DreamDojo pushing the boundary of how AI systems learn to reason about and act in physical environments. DreamDojo, for example, learns how the physical world behaves from human video and builds on NVIDIA Cosmos open frontier models to predict how a robot would handle objects and operate in environments it was never trained on. It lets researchers evaluate policies, plan actions and teleoperate a virtual robot, accelerating development without the costs and risks of physical deployment.

AI for life sciences was fueled by NVIDIA BioNeMo open models and research contributions that help researchers understand protein function, molecular behavior and genetic code. Papers like FLIP2 introduce public benchmarks for testing how well AI predicts the effects of protein mutations. KERMT is a new BioNeMo open model for predicting molecular properties important to drug discovery. 

Synthetic data generation (SDG) drew particular interest at ICML this year with several Nemotron and physical AI open datasets, reflecting a broader shift in how researchers are thinking about training at scale without relying solely on human-labeled data.

The Open Research Stack

Open infrastructure gives researchers the tools to accelerate breakthroughs. 

The papers show Nemotron being used less like a single model release and more like a research stack: open weights to evaluate against, open datasets to train and adapt with, and open recipes for reasoning, tool use, safety, data curation and efficient inference.

Alongside the models, NeMo Curator and the open datasets it supports gives researchers a reproducible foundation for training data curation. SDG tools enable creating high-quality training sets at a scale and speed that would’ve been impractical just a few years ago.

The Cosmos 3 family of open, frontier omnimodels gives researchers and developers a generational leap in the ability to build robots, autonomous vehicles and vision AI that perceive, reason, plan and act in the physical world.

In addition, the NVIDIA Alpamayo open model family for autonomous vehicle development, NVIDIA Isaac GR00T for robotics and NVIDIA BioNeMo for biomedical help accelerate research and development across industries.

The Ecosystem Building on Top

The momentum extends beyond NVIDIA’s own research labs.

Basecamp Research developed a new DNA foundation model, EDEN, that helps researchers interpret and design genetic sequences.

Merck & Co., uses KERMT to predict how potential drug molecules may behave in the body, including whether they are likely to be effective, safe and developable.

Sakana AI — attending ICML this year — built its Fugu and Fugu-Ultra models directly on Nemotron 3 Ultra, using the open foundation to push forward its work on AI research automation.

KiloCode integrated Nemotron into its code-routing architecture, reporting token cost reductions of up to 90% — a result with real implications for the economics of deploying AI in production.

NAVER developed its own model using the Nemotron architecture, extending the foundation for Korean-language AI research.

Together AI is hosting Nemotron models on its platform, making them more accessible to researchers who need reliable, seamless access to open inference.

Humanoid, LG Electronics, NEURA Robotics and Noble Machines are adopting NVIDIA Isaac GR00T  models to accelerate industrial deployments of their humanoids, while 1X, Agility, Agile Robots, Boston Dynamics, Hexagon Robotics, and Mentee are building the next generation of humanoids using Cosmos world models, Isaac Sim and Isaac Lab to accelerate the development and validation of their robots.

Explore NVIDIA’s open models on Hugging Face.

Explore genomics and biology research at ICML’s GenBio Workshop on Friday, July 10.

Nations have long invested in domestic infrastructure to advance their economies, protect and use their data, and take advantage of technology opportunities in areas such as transportation, communications, commerce, entertainment and healthcare.

AI, the most important technology of our time, is turbocharging innovation across every facet of society. Countries are investing in AI capabilities so they can design, train and deploy models and applications, using domestic infrastructure, local datasets and homegrown expertise. This approach ensures AI solutions are tailored to local citizens, services and regulations.

Why AI Capabilities Matter

The urgency for countries to build and deploy AI capabilities has grown with the rise of generative and agentic AI, which is reshaping markets, inspiring new industries and transforming existing ones — from gaming to healthcare. It’s changing how people work, as many professions now use AI-powered copilots.

These efforts span physical infrastructure and data infrastructure. On the data side, countries are developing foundation models, such as large language models, built by local teams and trained on local datasets. This helps reflect regional dialects, cultural context and specific domains in the models’ outputs.

For example, speech AI models can help preserve, promote and revitalize indigenous languages. 

Large language models are not only used to understand and generate human language; they can also write software code, aid in drug discovery, help protect consumers from financial fraud, teach robots physical skills and much more.

As AI and accelerated computing become increasingly important for tackling climate change, boosting energy efficiency and defending against cybersecurity threats, national AI capabilities play a critical role in enabling every country to strengthen its resilience and sustainability.

Factoring In AI Factories

A new class of essential infrastructure for AI production has emerged: AI factories, where data comes in and intelligence comes out. These are next-generation data centers that host advanced, full-stack accelerated computing platforms for the most computationally intensive tasks.

Countries are building domestic computing capacity through various models. Some are procuring and operating AI clouds in collaboration with state-owned telecommunications providers or utilities. Others are sponsoring local cloud partners to provide shared AI computing platforms for public-private use.

“The AI factory will become the bedrock of modern economies across the world,” NVIDIA founder and CEO Jensen Huang said in a media Q&A.

Ingredients of a National AI Strategy

There are five ingredients of a national AI strategy:

• AI Imperative: Domestic AI capabilities are critical to economic growth, national security, cultural preservation and innovation — with responsible, trustworthy AI aligned to local policies as well as national goals.
• AI-Ready Workforce: A wide spectrum of local AI skills and talent, plus basic AI literacy across the population. Education is important at all levels, from early STEM programs through applied AI across industries.
• AI Models and Data: Foundation models and large language models trained and fine-tuned with local data, hosted and run on local infrastructure, subject only to local laws. The localization of models will ensure that AI factory outputs are fine-tuned to the language, culture and context for the intended workloads. 
• AI Ecosystem: A local ecosystem of AI investors, developers, scientists, entrepreneurs, enterprise customers and government organizations. 
• AI Factories: Highlighted above, AI factories are locally owned, operated and governed AI clouds for training and inference. The greatest utility of AI factories comes from the public-private partnerships that can scale infrastructure to meet the needs of growing innovation within countries and industries.

National AI Strategies Underway

Countries around the world are investing in AI capabilities tailored to their national needs. AI investments can help grow economies while delivering tangible social and environmental benefits for citizens.

Since 2019, NVIDIA’s AI Nations initiative has helped countries in every region build out their AI ecosystems and workforce, creating the conditions for engineers, developers, scientists, entrepreneurs, creators and public sector officials to pursue their AI ambitions at home.

In Europe, AI agents from ThinkDeep, built on the NVIDIA AI platform, are helping France’s Ministry of Economy and Finance automate complex public‑service workflows by processing millions of documents and data sources, cutting document search times from two days to two minutes, saving 2 million euros for 10,000 employees and reducing energy use through more efficient, in‑country infrastructure control. 

In Asia, India’s Sarvam platform, powered by NVIDIA GPUs and built entirely on domestic infrastructure, is delivering multilingual AI models and voice agents optimized for the country’s 22 official languages, enabling government and enterprise services to reach hundreds of millions of people in their own languages while keeping data, compute and governance under national control. 

In Latin America, AI solutions from Widelabs, running on NVIDIA‑accelerated infrastructure, are helping modernize and expand access to legal services for the Public Ministry of Rio Grande do Sul in Brazil, streamlining internal investigations and making justice records easier to find and use for more than 8 million citizens across nearly 500 municipalities — aligning advanced computing with more efficient, transparent and inclusive public administration. 

These efforts demonstrate how AI turns domestic infrastructure, local data and homegrown talent into solutions that can be used for social good.

Learn more by joining NVIDIA at the AI for Good Summit, running July 7-10 in Geneva, Switzerland, and read more about NVIDIA’s commitment to trustworthy AI.

Summer is heating up — and GeForce NOW is taking players along for the ride.

Start the month with Monopoly: Star Wars Heroes vs. Villains, bringing a galaxy far, far away to the iconic board-game franchise, alongside 12 new games joining the cloud this month. 

Plus, don’t let the sun set on the biggest GeForce NOW savings of the year. Level up for less before the deals disappear.

Light Side, Dark Side, Cloud Side 

Rule the board, you must. Choose a side in Monopoly: Star Wars Heroes vs. Villains, the classic property-trading board game reimagined with legendary characters, locations and rivalries from across the Star Wars universe.

Play as iconic heroes or infamous villains — each with unique abilities — to assemble a team and experience cinematic moments while competing with family and friends across locations from every era of the saga. Every roll of the dice brings new opportunities to build an empire and claim victory.

GeForce NOW makes it easy to take the battle between the light and dark sides across nearly any device. Jump into a match on a low-powered PC, Mac, phone, TV, tablet or handheld device and keep the fun going across the galaxy.

Check out what’s available this week:

• Monopoly: Star Wars Heroes vs. Villains (New release on Steam and Ubisoft, available June 30)
• Meccha Chameleon (Steam)

And look forward to the games coming throughout the month:

• Assassin’s Creed Black Flag Resynced (New release on Steam and Ubisoft Connect, available July 9)
• Denshattack! (New release on Steam and Xbox, available on Game Pass July 15)
• The Mound: Omen of Cthulhu (New release on Steam, available July 15)
• Heave Ho 2 (New release on Steam, available July 16)
• Fogpiercer (New release on Steam and Xbox, available on Game Pass July 17)
• ZeroSpace (New release on Steam, available July 20)
• The Planet Crafter (New release on Xbox, Available on Game Pass July 21)
• Carnival Hunt (New release on Steam, available July 23)
• The Ranchers (New release on Steam, available July 30)
• Corsair Cove (New release on Steam and Xbox, available on Game Pass July 31)

Juicy Extras From June

In addition to the 18 games announced last month, 10 more came to the cloud. 

• Deer & Boy (Steam)
• DOOM Eternal (Epic Games Store)
• Embers of the Uncrowned Demo (Steam)
• EMPULSE (Steam)
• The Elder Scrolls Online (Xbox, available on Game Pass)
• NBA THE RUN (Steam)
• SAND: Raiders of Sophie (Steam)
• Voidling Bound (Steam)
• Witchspire (Steam)
• World of Tanks: HEAT (Wargaming)

Last Call for Cloud Summer Savings

The final days of the GeForce NOW Summer Sale are here. Before the savings disappear, gamers can save $35 on a 12-month Performance membership or $70 on a 12-month Ultimate membership — unlocking GeForce RTX-powered gaming in the cloud across devices they already own.

The Performance membership delivers smooth, high-quality gaming with RTX-powered servers, making it easy to jump into favorite titles across PCs, Macs, phones, handhelds and TVs.

The Ultimate membership takes cloud gaming to the max with RTX 4080‑ or 5080‑class performance. Experience cinematic visuals, ultralow latency and responsive gameplay powered by technologies like NVIDIA DLSS, ray tracing and NVIDIA Reflex — all without the cost of a new gaming rig.

Hear directly from the GeForce NOW Community.

One GeForce NOW member recently called the Summer Sale “quite significant” after realizing the savings were even larger than expected in their local currency. By locking in a year of Ultimate, they calculated their monthly cost dropped from roughly 29 CAD to 17 CAD – showcasing how GeForce NOW continues to help gamers around the world enjoy the games they love, wherever they choose to play.

Plus, check out this spreadsheet, made by a community member, featuring discounted games streaming on GeForce NOW  and build out a bigger library at the best bargains during the Steam Summer Sale.

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

As AI moves from model development to production inference, compute demand is accelerating and shifting toward continuously operating AI factories that generate tokens at scale. This shift requires access to large‑scale, multi‑tenant accelerated computing that can come online quickly, stay highly utilized and support the economics of token‑scale AI services. 

Emerging AI companies historically have had limited access to capital-intensive infrastructure, with even long-term commitments insufficient to unlock financing for compute.

To address this, NVIDIA is introducing a new business model that opens up compute access to the fast‑growing AI ecosystem of startups, model builders, enterprises, research organizations and regional AI players. 

This new model enables AI clouds to procure NVIDIA infrastructure for AI-native, enterprise and ISV customers through economic alignment with a revenue-sharing and credit-support model. Through the partnership, AI clouds will sell NVIDIA-powered cloud services, with NVIDIA earning both standard product revenue and a share of the cloud revenue on the supported capacity. This structure accelerates adoption of NVIDIA platforms among the high-growth, high-conviction AI native sector, and provides NVIDIA with a recurring, usage-linked earnings stream.

For model builders, inference providers, agent platforms and enterprises scaling AI, it can mean faster access to full-stack accelerated computing without waiting through site selection, power procurement, construction and hardware bring-up.

NVIDIA AI Factory Capacity Built Around Demand

The initiative is already taking shape, with AI cloud companies building DSX AI factories designed to serve customers and workloads across regions. 

Sharon AI and Firmus are among the first companies to work with NVIDIA on this new business model. 

Sharon AI is deploying up to 40,000 NVIDIA Grace Blackwell GB300 GPUs.

“This strategic collaboration with NVIDIA marks a pivotal moment in Sharon AI’s mission to deliver sovereign, large-scale AI compute infrastructure,” said James Manning, cofounder and CEO of Sharon AI. 

Firmus is building a DSX AI factory campus in Batam, Indonesia. The campus is expected to scale to 360 megawatts and up to 170,000 NVIDIA GPUs.

“AI-native companies need access to scalable, energy- and cost-efficient compute infrastructure to compete globally,” said Tim Rosenfield, co-CEO of Firmus Technologies. “Firmus AI cloud is building a NVIDIA DSX-aligned AI factory, which will enable our cloud to help more customers access the compute they need to build and scale AI.”

AI natives such as Baseten, Fireworks AI and Together AI show where compute demand is headed: they need immediate access to AI cloud capacity to run model training, post-training, fine-tuning and high-volume agentic inference for developers, digital natives and enterprises building with AI.

Their customers need reliable access to large-scale NVIDIA accelerated computing as usage grows, but they also need commercial flexibility as products move from pilot to production. 

To secure compute capacity and build and deploy AI models, contact Sharon AI and Firmus. 

 Learn more about NVIDIA Cloud Partners and AI factories. 

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

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

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

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

Advancing the Agentic Era of Scientific Discovery

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

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

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

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

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

A Scientific Foundation Built for Agents

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

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

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

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

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

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

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