Tag: AI Factory

Spain-Based Startups Advance Regional Innovation With Barcelona Supercomputing Center

Posted on by faz_business



The Barcelona Supercomputing Center (BSC)’s AI Factory Accelerator Programme, now in full operation following a pilot initiative, gives startups access to sovereign compute, technical support and training to support innovation across industries. 

Many of these companies are also members of the NVIDIA Inception program for cutting-edge startups — and as of today, access to some of the BSC AI Factory services are available through the NVIDIA Inception portal. 

NVIDIA Inception complements the BSC AI Factory Accelerator Programme by providing members with training through NVIDIA Deep Learning Institute open courses, business mentoring, venture capital connections and other resources.

An upcoming extension of MareNostrum 5 — BSC’s public infrastructure serving scientific and technological innovation, powered by NVIDIA accelerated computing — will deliver supercomputing access through the BSC AI Factory and other AI Factories across Europe. BSC AI Factory is among Europe’s first AI factories and the first in Spain, and has delivered more than 800 services to private and public-sector clients since its April 2025 launch.
 

 

Three Inception members were recently named the BSC AI Factory Accelerator Programme’s AI Champions at the pilot initiative’s demo day:

Qatium’s water-network platform — already used by more than 250 utilities in 16 countries — is now in early production with its Neural Water Intelligence technology, powered by proprietary AI models trained on MareNostrum 5. Built on NVIDIA accelerated computing, the NVIDIA cuGraph library, the NVIDIA PhysicsNeMo framework and NVIDIA Nemotron open models, the technology helps water utilities make better operational decisions every day.

OneCareAI, a research spin-off from BSC, used NVIDIA NIM microservices to develop technology designed to identify signs of hidden atrial fibrillation — an irregular heart rhythm — in seemingly normal ECGs, helping clinicians identify patients who may be at risk of stroke before one occurs.

Avatia built a voice-first, multi-agent system that provides pharmaceutical companies with agentic coworkers that support sales representatives and give doctors transparent, curated information about medicines, ultimately helping the right treatment reach the right patient sooner. The system taps into NVIDIA NIM microservices, the NVIDIA Nemotron 3 open model family and the open source NVIDIA NeMo Curator library.

Other BSC AI Factory startups — Agrikola AI, Elna Health, Lokutor, Montevive AI, Oppy, Pack2Earth and Up2Smart — span precision agriculture, medical imaging and knowledge access, speech services, enterprise security, sales intelligence and sustainable packaging.

The cohort reflects the breadth of what advanced intelligence can now unlock: preventing strokes, detecting breast cancer subtypes before biopsy, putting medical knowledge at doctors’ fingertips faster, replacing petroleum-based plastic with biobased compostable materials, protecting crops with robots instead of chemicals and more.

Learn more about NVIDIA Inception and the BSC AI Factory Accelerator Programme.
 

Amid Ecosystem Adoption, NVIDIA Opens Vera Rubin Architecture to Help Scale AI Factories Worldwide

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As AI factories grow from megawatts to gigawatts, building them takes more than advanced computing. Open standards are helping the industry scale AI infrastructure faster, more efficiently and more reliably.

At the Open Compute Project (OCP) Global Summit, taking place in the San Jose McEnery Convention Center, NVIDIA announced new partnerships and contributions aimed at creating a common foundation for next-generation AI infrastructure. 

NVIDIA contributed key NVIDIA Vera Rubin rack and compute tray designs to OCP through the open NVIDIA MGX architecture, giving system builders a common foundation for developing next-generation AI factory infrastructure.

Together, these efforts span the AI factory itself, the utility grid that powers it and the rack-scale architecture at the heart of modern accelerated computing.

NVIDIA and Partners Announce Open Architecture Advances 

At the OCP event, NVIDIA and partners made a handful of announcements: 

  • NVIDIA and hyperscalers published open standards for next-generation AI factories, including AI factory design standards and grid resilience. 
  • NVIDIA launched new open source software components for NVIDIA DSX AI Factory. 
  • Meta and NVIDIA advanced open AI networking with NVIDIA Spectrum-X Ethernet SuperNIC and Spectrum-6 innovations.
  • Visionbay introduced an 800V DC AI factory design for the Vera Rubin era.
  • NVIDIA and Cisco announced the Ultra-Dense Small Form Factor Pluggable (USFP) standard, the industry’s first module ​for 400Gb/s-based networking fabrics.
  • NVIDIA and Google announced Falcon Transport support on future NVIDIA ConnectX-10 SuperNICs and BlueField DPUs.

Before a packed audience, Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA, discussed how open infrastructure is becoming a critical ingredient for scaling AI factories worldwide. 

The news at OCP spotlights how NVIDIA’s extreme codesign combined with open standards is accelerating deployment, improving power efficiency and driving tokens-per-watt performance.

 

Vera Rubin AI Factories Advance With MGX Rack Contributions at OCP 🔗

NVIDIA joined industry leaders in contributing to the third-generation MGX AI data center reference design through OCP’s Open DC Initiative.

Combined with open liquid-cooling technologies such as MQD and MQDB, the effort helps turn proven AI factory designs into industry standards that others can adopt, manufacture and scale.

For NVIDIA Vera Rubin AI factories, the updated MGX 1.2 specification extends the open architecture for AI factories with rack-scale blind-mate liquid cooling, interoperable rack-scale interconnect infrastructure and a liquid-cooled 5,000A busbar designed for higher-power AI systems. It also adds native 19-inch equipment support and higher-density rack designs.

MGX 1.2 further expands modularity with configurable I/O bays, fanless liquid-cooled compute trays, standardized power and cooling interfaces, and a common infrastructure that helps ecosystem partners build interoperable accelerated computing platforms. It improves assembly, test and service times to streamline manufacturing and field service to enable faster and more efficient infrastructure buildout. The architecture supports rack deployments up to 250kW while simplifying serviceability, deployment and infrastructure reuse.

The Vera CPU rack design specification extends the NVIDIA MGX open architecture beyond accelerated computing, providing a purpose-built platform for the growing agentic AI era. While Vera Rubin NVL72 serves as the foundation for large-scale training and inference, the NVIDIA Vera CPU rack is designed to support tool calling, orchestration and sandboxed execution, helping AI agents interact with applications, data and services in real time.

Built on the third-generation NVIDIA MGX rack architecture, the Vera CPU rack shares the same rack-scale design principles that unite compute, networking, power and cooling into a common modular platform. This approach enables partners to deploy specialized infrastructure while benefiting from a shared ecosystem, simplified integration and a consistent foundation for next-generation AI factories.

 

NVIDIA Launches New Open Source Software Components for DSX AI Factory 🔗

NVIDIA DSX AI factory platform applies extreme codesign to unify computing, networking, power, cooling, facilities and software, helping AI factories operate as a single optimized system, maximizing AI output within available infrastructure constraints. At the center of this unified system is DSX OS, the open operating layer that turns the DSX architecture into coordinated, repeatable operations at scale.

At OCP, NVIDIA expanded DSX OS with open source components for configuring networks, validating clusters, managing racks and provisioning bare metal: 

  • NVIDIA Config Manager: Automates switch configurations at scale, allowing customers to deploy, validate and maintain consistent AI factory network fabrics with minimal to zero manual intervention/
  • NVIDIA Cluster Readiness Engine: Helps eliminate costly job crashes by stress-testing GPU clusters with real workload simulations to guarantee hardware health before training begins
  • NVIDIA Rack Management Service: Offers a rack hardware abstraction and operations solutions that treats racks as first-class units for improved AI factory scale firmware management.
  • Cluster API Provider for NVIDIA Infrastructure Controller: Accelerates bare-metal deployment and tenant onboarding, turning raw AI hardware into production-ready clusters in hours instead of days.

Developed from NVIDIA’s experience running NVIDIA DGX Cloud, these open source components enable cloud providers and software partners to integrate AI factory capabilities directly into their multi-tenant platforms. 

Developers can visit the DSX AI Factory Github page to explore and contribute to both the new and existing open source software components from NVIDIA DSX AI Factory platform.

 

NVIDIA and Hyperscalers Publish Open Standards for Next-Generation AI Factories 🔗

For AI infrastructure to scale globally, it needs more than advanced hardware — it needs a common foundation that the entire industry can build on.

At OCP, NVIDIA joined AMD, Google and Meta to publish a new open specification that establishes that foundation. The Open Data Center Specification v0.7 defines a unified blueprint for next-generation AI factory facility design — from power architecture to cooling infrastructure to physical layout. 

The OCP Grid Resilience Specification, published by Google, Meta, Microsoft and NVIDIA, establishes how large-scale AI infrastructure should behave during utility grid disturbances: staying connected, responding automatically and restoring to near-full load within less than two seconds.

With these standards now open, manufacturers around the world have a common target to build for. Equipment makers, colocation providers and data center operators can begin designing, fabricating and delivering the infrastructure layer that powers AI factories under construction globally — without waiting for proprietary specifications from individual customers.

Being NVIDIA DSX reference design-compatible, these specifications provide a validated, deployable implementation of both specs, from facility layout and power architecture to the software that monitors and responds to grid signals in real time. For operators building Vera Rubin AI factories, there’s now a straight line from open community standard to a running system.

 

Visionbay.ai Designs 800VDC AI Factory for the Vera Rubin Era 🔗

As AI factories grow larger and more powerful, one challenge looms over them all: delivering enough power to the accelerated computing systems driving the next generation of AI.

At OCP, Visionbay unveiled plans for K-1, a next-generation AI factory platform designed around an 800 VDC architecture and built with future NVIDIA Vera Rubin deployments in mind. The project combines NVIDIA’s 800 VDC reference architecture with power and thermal technologies from Vertiv to create a scalable foundation for high-density AI infrastructure.

The design addresses a growing industry challenge. As AI training, inference and reasoning workloads expand, rack power densities continue to rise. Traditional low-voltage power distribution becomes increasingly difficult to scale, requiring more copper, more cabling and more power-conversion equipment.

By adopting NVIDIA’s 800 VDC architecture, K-1 creates a path toward higher-density AI infrastructure while preserving flexibility for existing deployments. Rather than requiring a complete transition on day one, the platform is designed to phase in 800 VDC distribution where it delivers the greatest benefit, helping operators prepare for future generations of accelerated computing without extensive facility redesign.

The architecture is designed around cluster-level power centers that centralize AC-to-DC conversion and distribute 800 VDC closer to compute resources. The approach simplifies downstream power delivery, supports modular expansion and creates repeatable building blocks for future AI factory growth.

K-1 also reflects the system-level design philosophy behind NVIDIA AI factories, where power, cooling and computing are engineered together. With support for liquid-cooled infrastructure, scalable power distribution and future Vera Rubin-class deployments, the project demonstrates how ecosystem partners are translating NVIDIA’s AI factory architecture into deployable infrastructure designed for the next era of accelerated computing.

 

At the event, Meta and NVIDIA announced that Meta is adopting NVIDIA Spectrum-6 102.4Tb/s Ethernet switches to build larger, higher-performance NVIDIA Vera Rubin AI factories on open networking. 

Spectrum-6 delivers twice the switching capacity and per-lane bandwidth of Spectrum-4, and provides a lossless Ethernet AI fabric at 200G per lane, with high effective bandwidth and performance isolation that keeps one job’s traffic from slowing another’s.

Meta’s Spectrum-6-based switch, N-IceCube, slots into IcePack, Meta’s 100T, liquid-cooled rack. N-IceCube runs the Facebook Open Switching System (FBOSS), Meta’s open source network operating system that contributed to OCP to support efficient networking at hyperscale. 

FBOSS is built on the Switch Abstraction Interface, an OCP standard NVIDIA helped develop, providing a vendor-independent way of controlling switch hardware. Together, these technologies support agentic AI factories of more than 100,000 GPUs that train and serve massive mixture-of-experts models.

The announcement marks the next step in a collaboration that combines Meta’s experience operating AI infrastructure at hyperscale with NVIDIA’s networking technology.

At the heart of this collaboration is Spectrum-X Multiplane technology, enabled by Spectrum-6 switches and ConnectX-9 SuperNICs. Multiplane distributes GPU traffic in hardware across up to eight independent network planes, enabling AI factories to scale beyond 100,000 GPUs with fewer switch tiers and lower infrastructure costs. Multiple paths between GPUs improve network utilization and resilience: when a component fails, traffic shifts to the remaining planes, keeping GPUs productive and AI applications running, even at reduced bandwidth.

Meta and NVIDIA worked together to strengthen that resilience through deployment experience and rapid engineering iteration. The teams validated improvements across on-premises and cloud environments. 

“We are excited about our close partnership with NVIDIA in making multiplane fabrics operationally scalable and performant, a critical requirement both for our own infrastructure builds and for our joint cloud partners,” said Omar Baldonado, senior director of data center and AI networking at Meta. “Extending FBOSS to Spectrum ASICs means we can run that same software stack across our fabrics, and Meta’s new Spectrum-based switch, N-IceCube, brings that into Icepack – our liquid-cooled 102.4T network rack for scale-out that connects upcoming generations of accelerators in our fleet.”

Together, Meta and NVIDIA are developing an architecture and operational playbook that other AI infrastructure operators can adopt, turning lessons from demanding production workloads into more scalable, resilient and efficient AI factories.

 

With USFP, NVIDIA and Cisco Reinvent Pluggable Optics for AI Factory Scale 🔗

As AI factories scale to support larger models and surging inference demand, every component of the infrastructure stack must deliver more performance, efficiency and simplicity — right down to the network connection itself. 

At OCP, NVIDIA and Cisco announced plans for USFP, a new optical pluggable transceiver module form factor designed to help AI factory builders scale next-generation AI fabrics while improving bandwidth density and power efficiency.

With approximately the same footprint as the widely adopted 200Gb/s-per-lane OSFP form factor, USFP will provide a path to 400Gb/s-per-lane networking and will extend a transceiver-based deployment model already proven across generations of data center networks. Packing 16 full-duplex lanes with plans to scale up to 32 lanes, USFP will deliver four times the bandwidth density of traditional 200Gb/s-per-lane pluggable optics. 

The result is the ability to connect more compute resources without adding network tiers that can increase latency, power consumption and complexity.

The new form factor is designed for the scale and efficiency demands of next generation AI factories, supporting higher-bandwidth fabrics while preserving the flexibility and serviceability that have made pluggable optics a mainstay of modern data centers. It also will deliver roughly 1.3x greater power efficiency, helping operators move more data with less energy.   

To help drive an open ecosystem, NVIDIA and Cisco are collaborating with TE Connectivity and Amphenol on cages, connectors and modules, with plans to advance USFP through a community-led, multi-source effort. Together, the companies are laying the foundation for more scalable, efficient and interoperable AI factory networking.

 

Google Falcon Transport Supports NVIDIA ConnectX-10 for Interoperable AI Networking 🔗

NVIDIA and Google also announced Falcon Transport support on future NVIDIA ConnectX-10 SuperNICs and BlueField DPUs, helping improve network efficiency for large-scale AI infrastructure. 

Falcon is a transport protocol designed to deliver data reliably over Ethernet networks, which can otherwise drop packets under congestion. It supports AI and storage workloads through multipath load balancing, programmable congestion control, hardware loss recovery and per-connection security.

This collaboration combines NVIDIA hardware acceleration with DOCA software to implement Falcon and extend NVIDIA networking to Google’s data center transport. NVIDIA and Google NICs can communicate over a common transport while applications retain standard RDMA programming interfaces.

By distributing traffic across paths, adapting to congestion, recovering dropped packets and protecting connections, Falcon helps workloads make better use of available bandwidth and maintain predictable performance as Ethernet fabrics scale.

Learn more by joining NVIDIA at OCP.

Securing the Infrastructure of Intelligence

Posted on by faz_business



AI factories are the defining infrastructure of the AI era — where compute transforms energy and data into intelligence that powers every business, industry and country.

In the AI economy, compute is revenue.

AI factories require a full stack of critical resources: advanced chips, packaging, memory, and networking — as well as land, power and shell.

Just as NVIDIA has used its scale, long-term visibility and supply-chain partnerships to secure critical semiconductor resources, we are now applying that same discipline to secure LPS capacity exclusively for NVIDIA AI factories.

Today, we are partnering with SB Energy to secure LPS capacity at the exceptional PORTS-Pike Technology Campus in Portsmouth, Ohio, to host NVIDIA compute. OpenAI will be the tenant.

LPS: The Next Strategic Resource

For the vast majority of NVIDIA customers, securing LPS has long been a part of their infrastructure strategy.

The world’s largest cloud service providers and investment-grade enterprises have balance sheets, infrastructure expertise, and long-term contracts to secure LPS independently. They build and operate AI factories using NVIDIA accelerated computing, networking, systems and software.

This model will continue to represent most of NVIDIA’s business.

But frontier AI labs are different.

Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support. They may have strong customer demand and rapidly growing revenue yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently.

Their growth is increasingly constrained not by algorithms or customer demand, but by the availability of compute.

For these companies, more compute means more intelligence, more products, more users and more revenue. NVIDIA is helping provide the infrastructure that powers this flywheel.

PORTS-Pike: A Site for Generations of NVIDIA Compute

OpenAI will build and operate a world-class AI factory at PORTS-Pike. The AI factory will use NVIDIA’s full-stack DSX AI factory platform, including GPUs, CPUs, networking, and infrastructure software.  

The initial deployment is expected to provide 4.25 gigawatts of AI factory capacity. Each generation of NVIDIA AI factory systems deployed at PORTS-Pike could represent approximately 1.5 million NVIDIA GPUs, or approximately $150 billion to $200 billion in NVIDIA revenue. Over 20 years, the site can support multiple upgrade cycles. 

This is the essential economic point: the LPS commitment secures a long-lived AI factory site, while the NVIDIA compute inside can be upgraded repeatedly. Each new generation can deliver greater production, more intelligence and better economics. 

NVIDIA may also choose to extend the arrangement at PORTS-Pike beyond the initial 4.25 gigawatts to secure the remaining capacity of 3.75 gigawatts. 

OpenAI and NVIDIA Expanding Compute Opportunity

More broadly, OpenAI has committed to substantial deployments of NVIDIA AI infrastructure through 2030. OpenAI’s existing and planned commitments represent approximately 12 gigawatts of NVIDIA compute, with an opportunity to expand to approximately 16 gigawatts if NVIDIA extends the PORTS-Pike arrangement beyond the initial 4.25 gigawatts. 

At these levels, the opportunity represents roughly $600 billion of NVIDIA compute through 2030. 

The Important Questions

What is NVIDIA guaranteeing, and for how long?

NVIDIA is supporting the LPS infrastructure at PORTS-Pike for approximately 4 gigawatts over a 20-year term, securing a site on which NVIDIA compute will be exclusively deployed.  

Our support is limited to defined portions of lease and power payments, along with a specified residual-value commitment — not the full cost of the site or all of the tenant’s obligations.   

The guarantee will become effective in phases as data centers are placed in service between 2028 and 2030.  As OpenAI makes lease payments and capacity comes online, NVIDIA’s remaining exposure declines.

Why is NVIDIA guaranteeing PORTS-Pike?

LPS has become a critical constraint on AI factory deployment. NVIDIA is selectively securing exceptional sites where we can host multiple generations of NVIDIA compute and serve durable customer demand. 

The productive life of the site extends through multiple generations of NVIDIA systems, each capable of producing more intelligence and more revenue than the generation before.

Is this circular financing?

No. OpenAI will pay the lease.   

NVIDIA uses its scale and long-term visibility to secure PORTS-Pike to host NVIDIA compute. This is the same discipline we apply to supply-chain management: we secure critical inputs when we have visibility into customer demand and when doing so enables long-term productive capacity.

What happens to PORTS-Pike if OpenAI does not use the site in the future?

NVIDIA compute is versatile, fungible and broadly adopted. The capacity can be resold to another qualified tenant across NVIDIA’s global ecosystem of cloud service providers, enterprises, AI labs and startups. 

CUDA makes NVIDIA compute more than hardware. It gives developers and NVIDIA engineers a common platform to continually improve installed systems. 

CUDA makes NVIDIA compute versatile. Versatility makes it fungible. Fungibility drives utilization and durability — making NVIDIA compute a productive asset: rentable and financeable. 

The value of an exceptional site, like PORTS-Pike, is not limited to one customer or one generation of compute. NVIDIA’s standardized platform, broad developer ecosystem and large market of potential users support the ability to redeploy productive capacity over time.

How much LPS will NVIDIA secure?

It will be strategic and disciplined. 

Most NVIDIA customers will continue to secure their own LPS.  The vast majority of LPS hosting NVIDIA compute will continue to be secured directly by CSPs, enterprises, sovereign AI builders and other customers. 

NVIDIA will focus selectively on exceptional sites where visible, durable demand can support multiple generations of NVIDIA compute.

The Infrastructure of Intelligence

PORTS-Pike represents the next step in NVIDIA’s journey. 

We began by building accelerated computing chips. We then expanded to systems, networking, CUDA and full-stack AI factories. Today, we are helping secure the critical infrastructure required to build these factories. 

NVIDIA is the full-stack AI infrastructure platform. 

We are investing in the long-lived foundations of AI factories so our customers can deploy the most productive compute platform in the world, generation after generation. 

By securing the critical resources needed to host NVIDIA compute, we can help the world’s most innovative companies build the AI factories that will power the age of intelligence.

NVIDIA Vera Rubin Maximizes Intelligence per Dollar for Post-Training

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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.  

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

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.

HPE AI Factory With NVIDIA Expands for the Era of Agents

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Enterprises are moving agentic AI from proof of concept to production — and the next generation of AI factories are built for the era of agents.

At HPE Discover Las Vegas, running through Thursday, June 18, NVIDIA and HPE are expanding the HPE AI Factory with NVIDIA, including NVIDIA Vera CPU and NVIDIA Agent Toolkit for HPE Private Cloud AI. 

Plus, NVIDIA Confidential Computing extends across HPE AI Factory and enhanced full-stack NVIDIA integration — with NVIDIA accelerated computing, NVIDIA AI software and NVIDIA networking — is available throughout the entire portfolio.

NVIDIA Vera CPU Available With HPE Private Cloud AI

The HPE ProLiant Compute DL394 Gen12 with the NVIDIA Vera CPU will be available in 2027 with HPE Private Cloud AI, a turnkey AI factory co-engineered with NVIDIA. Vera is the first CPU built for agents — designed for the tool calls, orchestration and real-time data processing required across the agent loop — bringing deterministic, low-latency performance into HPE Private Cloud AI.

The New York Stock Exchange, in collaboration with Redpanda and HPE, is an early enterprise customer exploring Vera CPU with the HPE ProLiant Compute DL394 Gen12 server.

The Vera CPU is part of the NVIDIA Vera Rubin platform, which is ramping into full production with the NVIDIA Vera Rubin NVL72 rack-scale system available from HPE. Vera Rubin was built for frontier-scale models larger than 1 trillion parameters and will ship with full-stack NVIDIA Confidential Computing across every chip. 

HPE is also bringing the HPE Compute XD700 — built on NVIDIA HGX Rubin NVL8 — to the HPE AI Factory, supporting up to 128 Rubin GPUs per rack.

NVIDIA Agent Toolkit Now Available With HPE Private Cloud AI

NVIDIA Agent Toolkit — including NVIDIA Nemotron open models, the NVIDIA OpenShell secure runtime and NVIDIA NemoClaw blueprints — will be available with HPE Private Cloud AI. Together, they give enterprises an agentic AI operating system for monitoring agent behavior, enforcing governance policies, and safely building and running autonomous, long-running multi-agent systems.

HPE Private Cloud AI adds secure local agent registration, letting customers approve AI models, skills and tools against centralized governance and security policies before they run. New HPE Zerto Software capabilities detect rogue agent actions and use continuous data protection to rewind to a clean state.

On the data side, HPE Alletra Storage MP X10000 — which achieved the foundation level of NVIDIA-Certified Storage — automatically applies metadata and governance policies to prepare unstructured data for AI pipelines, improving token throughput.

NVIDIA Confidential Computing Across All HPE AI Factory Solutions

NVIDIA Confidential Computing is now available across the HPE AI Factory through HPE Services — including HPE AI Factory at Scale, HPE Sovereign AI Factory and HPE Private Cloud AI. 

AI applications access and use private and sensitive data that needs to be protected and secured. In addition, models trained with proprietary data or techniques need to be safeguarded from exfiltration. Confidential computing is essential for these modern AI workloads, as it protects models and private data during execution for on-premises and sovereign deployments, establishing a chain of trust through cryptographic attestation and encryption at every stage.  

In addition, HPE ProLiant Compute DL380a achieved certification as part of the NVIDIA-Certified Systems for NVIDIA Confidential Computing program, which validates robust application performance with confidential computing. These systems provide hardware-based protection for AI workloads and sensitive data assets while maintaining optimal NVIDIA acceleration.

Across the HPE AI Factory solutions, NVIDIA BlueField DPUs and NVIDIA DOCA provide in-silicon zero-trust policy enforcement, runtime threat detection and network encryption — protecting AI workloads, agents and data without performance tradeoffs.

Enhanced Full-Stack NVIDIA Integration Across the Portfolio

HPE AI Factory at Scale, HPE Sovereign AI Factory and HPE Private Cloud AI are now available with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, NVIDIA Spectrum-X Ethernet networking, NVIDIA BlueField-3 DPUs and NVIDIA ConnectX-8 SuperNICs. 

For next-generation AI factories, every Vera Rubin NVL72 system will ship with NVIDIA networking built in — NVIDIA Vera BlueField-4 DPUs, NVIDIA ConnectX-9 SuperNICs and NVIDIA Spectrum-X Ethernet — with NVIDIA Spectrum-6 switching delivering 1.6x higher networking performance for AI communication versus off-the-shelf Ethernet. 

Spectrum-X Ethernet networking is the standard for HPE AI Factory with NVIDIA — including at-scale, sovereign and turnkey AI factory solutions available now. For large-scale and sovereign workloads, HPE announced at NVIDIA GTC in March that it’s also adding NVIDIA InfiniBand networking options — including NVIDIA Quantum-X800 InfiniBand with the HPE Cray Supercomputing GX5000.

These configurations are based on NVIDIA reference architectures and support use cases from AI development through production-scale deployment, with NVIDIA AI Enterprise software and the HPE Unleash AI ecosystem.

At HPE Discover this week, the Unleash AI partner program is expanding with nearly a dozen new AI software partners — including Aizen, BridgeTEK, deepset, Deliverance, Faclon Labs, Gallop, Rocket, Supervity, Thales, Trustwise and Vortiqx.

Attendees can explore these solutions all week at the show and learn more about the HPE AI Factory with NVIDIA, part of the NVIDIA AI Computing by HPE portfolio. 

NVIDIA and ServiceNow Partner on New Autonomous AI Agents for Enterprises

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

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

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

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

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

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

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

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

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

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

Open Models and Agent Skills Scale Enterprise AI

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

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

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

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

Efficient AI Factories

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

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

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

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

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

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

Everything Will Be Represented in a Virtual Twin, Jensen Huang Says at 3DEXPERIENCE World

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At 3DEXPERIENCE World in Houston, NVIDIA founder and CEO Jensen Huang and Dassault Systèmes CEO Pascal Daloz laid out a blueprint for industrial AI rooted in physics-based “world models” — systems designed to simulate products, factories and even biological systems before they’re built.

“Artificial intelligence will be infrastructure,”  like water, electricity, and the internet Huang told the crowd, playfully referring to the engineering-heavy audience as “Solid Workers,” a nod to Dassault Systèmes’ SolidWorks platform.

The announcement continues a collaboration spanning more than a quarter century between NVIDIA and Dassault Systèmes.

“This is the largest collaboration our two companies have ever had in over a quarter century,” Huang said. “We’re going to fuse these technologies so engineers can work at a scale that’s 100 times, 1,000 times — and eventually a million times greater than before.”

The new partnership brings NVIDIA accelerated computing and AI libraries together with Dassault Systèmes’ Virtual Twin platforms to move more engineering work into real-time digital workflows, powered by AI companions that help teams explore, validate, prototype and iterate faster.

Huang framed the shift as a reinvention of the computing stack: moving from hand-specified, structured digital designs to systems that can generate, simulate and optimize in software — at industrial scale.

From Digital Models to Industry World Models

Virtual twins are not applications, “they are knowledge factories,” Daloz said.

The partnership aims to establish industry world models — science-validated AI systems grounded in physics that can serve as mission-critical platforms across biology, materials science, engineering and manufacturing.

In Daloz’s framing, the value moves upstream: virtual twins become the place where knowledge is created, tested, and trusted — before anything is built in the physical world.

Dassault Systèmes, whose 3DEXPERIENCE platform serves more than 45 million users and 400,000 customers globally, has long been a leader in virtual twin technology — digital replicas that let engineers simulate products and processes before building them physically.

The collaboration brings together accelerated computing, AI and digital twin technologies so engineers can design not only geometry, but behavior — and explore radically larger design spaces earlier in development.

Together, the companies outlined how this shared architecture will show up across science, engineering and manufacturing workflows:

  • Advancing Biology and Materials Research​: The NVIDIA BioNeMo platform and BIOVIA science-validated world models accelerate the discovery of new molecules and next-generation materials.
  • AI-Driven Design and Engineering: SIMULIA AI-based Virtual Twin Physics Behavior leveraging NVIDIA CUDA-X libraries and AI physics libraries empowers designers and engineers to accurately and instantly predict outcomes.
  • Virtual Twins for Every Factory: NVIDIA Omniverse physical AI libraries integrated into the DELMIA Virtual Twin enable autonomous, software-defined production systems.
  • Virtual Companions Supercharge Dassault Systèmes’ Users: The 3DEXPERIENCE agentic platform, combining NVIDIA AI technologies and NVIDIA Nemotron open models with Dassault Systèmes’ Industry World Models, powers Virtual Companions to tap into deep industrial context, delivering trusted, actionable intelligence.

Huang said that in domains like biology and materials, the frontier is learning the underlying “language” of complex systems and then generating new options that can be evaluated and validated in simulation.

Designing and Operating the Factory in Software

A central theme of the discussion was how factories themselves are changing — from static physical assets to living systems that are designed, simulated and operated as virtual twins.

As part of the partnership, Dassault Systèmes is deploying NVIDIA-powered AI factories on three continents through its OUTSCALE sovereign cloud, enabling customers to run AI workloads while maintaining data residency and security requirements.

Both executives emphasized that the goal isn’t to replace engineers — it’s to amplify them. As AI agent companions take on more exploratory and repetitive tasks, designers and engineers gain leverage and creativity, not redundancy.

AI Companions That Expand Human Creativity

Every designer will have a “team of companions,” Huang said — a shift he described as fundamentally positive for engineers, software platforms and the broader ecosystem built on them.

For the tens of millions of engineers who use Dassault Systèmes tools to design everything from aircraft to consumer packaged goods, the shift isn’t about replacing human creativity — it’s about expanding it.

“Success is not about automation,” Daloz said. “[Engineers] don’t want to automate the past — they want to invent the future.”

Looking ahead, Daloz framed the partnership as about more than performance gains – it’s an effort to open new possibilities, help companies eliminate bad choices before they become expensive mistakes, and create entirely new categories of products.

“Virtual twins and the 3D Universes are not applications,” Daloz said. “They are knowledge factories.”

The fireside conversation between Huang and Daloz was broadcast live from 3DEXPERIENCE World.