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A synthesis of 6 perspectives on AI, machine learning, models release, models benchmarks, trending AI products
AI-Generated Episode
On this episode of The NeuralNoise Podcast, we dive into Nvidia’s new Vera Rubin platform, the six‑chip “AI factory” unveiled at CES 2026—and how it ties into robotics, autonomous vehicles, and even the future of chip design itself.
Nvidia is no longer just selling GPUs—it’s selling entire AI factories.
The new Vera Rubin platform is built around six tightly integrated chips designed to operate as one AI supercomputer:
In Nvidia’s own framing, the data center—not a single server—is now the unit of compute. AI workloads have shifted from sporadic queries to always‑on “intelligence production”: long‑context reasoning, multi‑agent workflows, and massive “mixture‑of‑experts” (MoE) models running continuously.
Rubin responds with extreme co-design. The Vera CPU is a purpose‑built data engine: 88 custom Arm‑compatible cores, 1.2 TB/s of memory bandwidth, and 1.8 TB/s coherent NVLink‑C2C to the Rubin GPU. The Rubin GPU itself pushes up to 50 PFLOPS of low‑precision Transformer compute with HBM4 memory delivering up to 22 TB/s of bandwidth. NVLink 6 doubles GPU‑to‑GPU bandwidth to 3.6 TB/s per GPU, wiring 72 GPUs in a rack into what software sees as one accelerator.
This isn’t just theoretical uplift. Nvidia says Rubin‑based Vera Rubin NVL72 systems can:
Security and operations are baked in. BlueField‑4 DPUs run networking, storage, and security off the main CPUs/GPUs, and the platform supports third‑generation confidential computing at rack scale. Mission Control software ties it together with power‑aware scheduling, predictive maintenance, and zero‑downtime recovery.
At CES 2026, CEO Jensen Huang officially launched Rubin as Nvidia’s new flagship architecture, declaring, “Vera Rubin is designed to address this fundamental challenge that we have: The amount of computation necessary for AI is skyrocketing.”
Rubin will replace Blackwell and is already in full production, with volume ramping in the second half of 2026. According to TechCrunch and Business Insider, the platform is already spoken for by much of the cloud and AI ecosystem:
Nvidia claims Rubin offers roughly 3.5x faster training and 5x faster inference than Blackwell, with up to 8x more inference compute per watt—numbers that matter when Huang is projecting $3–4 trillion of global AI infrastructure spend over the next five years.
The Verge highlights another key claim: Rubin can train large MoE models in the same time as Blackwell while using just a quarter of the GPUs and at one‑seventh the token cost. That’s a direct attack on the economics of frontier model training.
Rubin is the centerpiece, but Nvidia is clearly building an end‑to‑end moat that extends into the physical world.
On the robotics side, Nvidia is pushing to become the “Android of generalist robotics.” At CES, it rolled out a full physical‑AI stack:
All of this is integrated with Hugging Face’s LeRobot framework, tying Nvidia’s 2 million robotics devs to Hugging Face’s 13 million AI builders. The aim is clear: make it cheap and standardized to build robots that can generalize across tasks—and ensure those robots run on Nvidia silicon.
In autonomous driving, Huang introduced Alpamayo, an open AI model and toolset meant to bring reasoning to vehicles. Mercedes‑Benz cars using the system are expected on roads as early as Q1 2026, with vision‑language‑action models handling rare edge‑case scenarios that defeat traditional rule‑based stacks.
Finally, Nvidia is turning its hardware inward. A new partnership with Siemens will port Siemens’ EDA (electronic design automation) tools to Nvidia GPUs, accelerating chip design itself.
The goal extends beyond speedups: Nvidia and Siemens want to build digital twins of everything from individual chips to entire Rubin racks before they exist physically. As Huang put it at the Siemens keynote, they want to “build that Vera Rubin in the future as a digital twin” first—simulate thermals, signal integrity, reliability, and performance, then build once.
In other words, the same GPU‑accelerated simulation stack that trains AI models will increasingly design the hardware those models run on.
Nvidia’s Vera Rubin launch isn’t just a faster GPU generation; it’s a declaration that AI has entered an industrial phase. Rubin treats the data center as a single machine, fuses compute and networking into an “AI factory,” and extends that model out into robots, cars, and the chip design tools that will define the next decade of hardware. If Rubin delivers on its promises—lower cost per token, higher tokens per watt, and more predictable scaling—it will set the baseline for what serious AI infrastructure looks like in the years ahead.