Nvidia introduces Vera Rubin, a seven-chip AI platform with OpenAI, Anthropic and Meta on board

Nvidia on Monday took the wraps off Vera Rubin, a sweeping new computing platform built from seven chips now in full production — and backed by an extraordinary lineup of customers that includes Anthropic, OpenAI, Meta and Mistral AI, along with every major cloud provider.

The message to the AI industry, and to investors, was unmistakable: Nvidia is not slowing down. The Vera Rubin platform claims up to 10x more inference throughput per watt and one-tenth the cost per token compared with the Blackwell systems that only recently began shipping. CEO Jensen Huang, speaking at the company's annual GTC conference, called it "a generational leap" that would kick off "the greatest infrastructure buildout in history." Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure will all offer the platform, and more than 80 manufacturing partners are building systems around it.

"Vera Rubin is a generational leap — seven breakthrough chips, five racks, one giant supercomputer — built to power every phase of AI," Huang declared. "The agentic AI inflection point has arrived with Vera Rubin kicking off the greatest infrastructure buildout in history."

In any other industry, such rhetoric might be dismissed as keynote theater. But Nvidia occupies a singular position in the global economy — a company whose products have become so essential to the AI boom that its market capitalization now rivals the GDP of mid-sized nations. When Huang says the infrastructure buildout is historic, the CEOs of the companies actually writing the checks are standing behind him, nodding.

Dario Amodei, the chief executive of Anthropic, said Nvidia's platform "gives us the compute, networking and system design to keep delivering while advancing the safety and reliability our customers depend on." Sam Altman, the chief executive of OpenAI, said that "with Nvidia Vera Rubin, we'll run more powerful models and agents at massive scale and deliver faster, more reliable systems to hundreds of millions of people."

Inside the seven-chip architecture designed to power the age of AI agents

The Vera Rubin platform brings together the Nvidia Vera CPU, Rubin GPU, NVLink 6 Switch, ConnectX-9 SuperNIC, BlueField-4 DPU, Spectrum-6 Ethernet switch and the newly integrated Groq 3 LPU — a purpose-built inference accelerator. Nvidia organized these into five interlocking rack-scale systems that function as a unified supercomputer.

The flagship NVL72 rack integrates 72 Rubin GPUs and 36 Vera CPUs connected by NVLink 6. Nvidia says it can train large mixture-of-experts models using one-quarter the GPUs required on Blackwell, a claim that, if validated in production, would fundamentally alter the economics of building frontier AI systems.

The Vera CPU rack packs 256 liquid-cooled processors into a single rack, sustaining more than 22,500 concurrent CPU environments — the sandboxes where AI agents execute code, validate results and iterate. Nvidia describes the Vera CPU as the first processor purpose-built for agentic AI and reinforcement learning, featuring 88 custom-designed Olympus cores and LPDDR5X memory delivering 1.2 terabytes per second of bandwidth at half the power of conventional server CPUs.

The Groq 3 LPX rack, housing 256 inference processors with 128 gigabytes of on-chip SRAM, targets the low-latency demands of trillion-parameter models with million-token contexts. The BlueField-4 STX storage rack provides what Nvidia calls "context memory" — high-speed storage for the massive key-value caches that agentic systems generate as they reason across long, multi-step tasks. And the Spectrum-6 SPX Ethernet rack ties it all together with co-packaged optics delivering 5x greater optical power efficiency than traditional transceivers.

Why Nvidia is betting the future on autonomous AI agents — and rebuilding its stack around them

The strategic logic binding every announcement Monday into a single narrative is Nvidia’s conviction that the AI industry is crossing a threshold. The era of chatbots — AI that responds to a prompt and stops — is giving way to what Huang calls "agentic AI": systems that reason autonomously for hours or days, write and execute software, call external tools, and continuously improve.

This isn't just a branding exercise. It represents a genuine architectural shift in how computing infrastructure must be designed. A chatbot query might consume milliseconds of GPU time. An agentic system orchestrating a drug discovery pipeline or debugging a complex codebase might run continuously, consuming CPU cycles to execute code, GPU cycles to reason, and massive storage to maintain context across thousands of intermediate steps. That demands not just faster chips, but a fundamentally different balance of compute, memory, storage and networking.

Nvidia addressed this with the launch of its Agent Toolkit, which includes OpenShell, a new open-source runtime that enforces security and privacy guardrails for autonomous agents. The enterprise adoption list is remarkable: Adobe, Atlassian, Box, Cadence, Cisco, CrowdStrike, Dassault Systèmes, IQVIA, Red Hat, Salesforce, SAP, ServiceNow, Siemens and Synopsys are all integrating the toolkit into their platforms. Nvidia also launched NemoClaw, an open-source stack that lets users install its Nemotron models and OpenShell runtime in a single command to run secure, always-on AI assistants on everything from RTX laptops to DGX Station supercomputers.

The company separately announced Dynamo 1.0, open-source software it describes as the first "operating system" for AI inference at factory scale. Dynamo orchestrates GPU and memory resources across clusters and has already been adopted by AWS, Azure, Google Cloud, Oracle, Cursor, Perplexity, PayPal and Pinterest. Nvidia says it boosted Blackwell inference performance by up to 7x in recent benchmarks.

The Nemotron coalition and Nvidia’s play to shape the open-source AI landscape

If Vera Rubin represents Nvidia's hardware ambition, the Nemotron Coalition represents its software ambition. Announced Monday, the coalition is a global collaboration of AI labs that will jointly develop open frontier models trained on Nvidia's DGX Cloud. The inaugural members — Black Forest Labs, Cursor, LangChain, Mistral AI, Perplexity, Reflection AI, Sarvam and Thinking Machines Lab, the startup led by former OpenAI executive Mira Murati — will contribute data, evaluation frameworks and domain expertise.

The first model will be co-developed by Mistral AI and Nvidia and will underpin the upcoming Nemotron 4 family. "Open models are the lifeblood of innovation and the engine of global participation in the AI revolution," Huang said.

Nvidia also expanded its own open model portfolio significantly. Nemotron 3 Ultra delivers what the company calls frontier-level intelligence with 5x throughput efficiency on Blackwell. Nemotron 3 Omni integrates audio, vision and language understanding. Nemotron 3 VoiceChat supports real-time, simultaneous conversations. And the company previewed GR00T N2, a next-generation robot foundation model that it says helps robots succeed at new tasks in new environments more than twice as often as leading alternatives, currently ranking first on the MolmoSpaces and RoboArena benchmarks.

The open-model push serves a dual purpose. It cultivates the developer ecosystem that drives demand for Nvidia hardware, and it positions Nvidia as a neutral platform provider rather than a competitor to the AI labs building on its chips — a delicate balancing act that grows more complex as Nvidia's own models grow more capable.

From operating rooms to orbit: how Vera Rubin's reach extends far beyond the data center

The vertical breadth of Monday's announcements was almost disorienting. Roche revealed it is deploying more than 3,500 Blackwell GPUs across hybrid cloud and on-premises environments in the U.S. and Europe — the largest announced GPU footprint in the pharmaceuti