The revolution of massive computing: how Vera Rubin's "nuclear bomb" redefines the AI race

The Unignorable Challenge: Moore’s Law Slows Down, AI Demand Explodes

The industry faces an uncomfortable paradox: while silicon improvement speeds are decelerating, AI models demand exponential performance increases each year. For a 1 GW data center costing $50 billion, the difference between an old architecture and a new one can mean directly doubling its revenue-generating capacity.

Jensen Huang, CEO of one of the world’s leading tech companies, openly acknowledges this dilemma: traditional optimization methods can no longer keep pace. That’s why, instead of replacing just 1 or 2 chips per generation as before, this time they opted for a comprehensive redesign of 6 key components of the Vera Rubin computing platform, which is already in mass production.

Vera Rubin: The Architecture That Rewrites the Rules of the Game

The true protagonist of this cycle is not a conventional graphics card but a complete processing ecosystem. Named after the astronomer who discovered dark matter, Vera Rubin represents a mindset shift: innovating simultaneously at all levels of the platform.

The 6 pillars of this architecture are:

Vera CPU provides intelligence and coordination. Equipped with 88 custom Olympus cores, it supports 176 simultaneous threads thanks to spatial multithreading technology. The 1.8 TB/s NVLink C2C bandwidth and 1.5 TB system memory (triple compared to the previous generation) ensure no bottlenecks in fundamental operations. With 227 billion transistors, it integrates the processing power needed to coordinate massive operations.

Rubin GPU, the computational heart, reaches 50 PFLOPS of inference power in reduced precision, five times higher than the Blackwell architecture. With 336 billion transistors, it incorporates the third generation of Transformer engines that dynamically adjust precision based on the model’s specific needs.

The ConnectX-9 network card offers ultra-fast connectivity with 800 Gb/s Ethernet based on 200G PAM4 technology. It includes programmable RDMA and data routing accelerators, along with CNSA and FIPS security certifications, with its 23 billion transistors.

BlueField-4 DPU emerges as the next-generation AI storage processor. With 800 Gb/s SmartNIC capacity, it combines the 64-core Grace CPU with ConnectX-9, integrating 126 billion transistors dedicated to this critical function.

The NVLink-6 switch chip is the orchestrator of the internal network. It can connect 18 compute nodes and coordinate up to 72 Rubin GPUs functioning as a single cohesive system. With NVLink 6 architecture, each GPU achieves 3.6 TB/s all-to-all bandwidth, enabling ultrafast collective communication within the network.

Finally, the Spectrum-6 optical switch handles 512 channels of 200Gbps each for transfers exceeding conventional speeds. Fabricated with integrated silicon photonics technology via TSMC COOP, it offers 352 billion transistors dedicated to optical copackaged interconnection.

Numbers That Speak: Unprecedented Performance Improvements

The resulting NVL72 system from this deep integration sets new standards. In reduced-precision inference tasks, it reaches 3.6 EFLOPS, fivefold the previous generation. For training, it hits 2.5 EFLOPS, a 3.5x increase.

Available memory has tripled: 54TB of LPDDR5X in the main system versus 20.7TB of high-bandwidth HBM. The HBM4 bandwidth reaches 1.6 PB/s (2.8 times higher), while the Scale-Up bandwidth hits 260 TB/s, double the previous generation.

Most notably: these performance leaps were achieved with only 1.7 times more transistors (2.2 trillion in total), demonstrating that architectural innovation is as important as silicon density.

From Digital to Physical: The Next Frontier

Although the numbers are impressive, their true impact lies in applications. AI now needs to transition from the digital world to the physical. For this, three types of integrated computing are required:

The training computer built with architectures like GB300 that generate base models. The inference computer, the “cerebellum” operating in robots or autonomous vehicles in real time. And the simulation computer, including platforms like Omniverse and Cosmos, which provide virtual environments where AI learns physical feedback before operating in the real world.

Alpamayo: Autonomous Driving That Thinks

Based on this triple-computer architecture, Alpamayo emerges as the first autonomous driving system with genuine reasoning capabilities. Unlike traditional systems executing rigid instructions, Alpamayo reasons like a human driver. It can explain what it will do next and why it makes that decision.

The Mercedes CLA equipped with this technology will be officially launched in the United States in the first quarter of this year, later expanding to Europe and Asia. This vehicle was rated by NCAP as the safest in the world, thanks to the “double safety stack” architecture that alternates between end-to-end AI systems and traditional safety protocols when confidence diminishes.

Robotics: Beyond Humanoids

The strategy extends to humanoid and quadruped robots, all equipped with the Jetson mini-computer and trained in the Isaac simulator. Integration also reaches industrial systems such as tools from Synopsys, Cadence, and Siemens.

Jensen Huang joked during the presentation: “The biggest robot is the factory itself. Robots will be designed in computers, manufactured in computers, and even tested and verified virtually in computers before facing real gravity.”

The Broader Context: $10 Trillion in Modernization

Over the past decade, approximately $10 trillion in global computing infrastructure is being completely modernized. But this is not just hardware updates. It represents a paradigm shift in how software is developed and deployed.

The rise of open models, exemplified by systems like DeepSeek that surprised the world with their inference efficiency, has catalyzed a wave of innovation. Although these models may lag 6 months behind the most advanced, every half-year a new generation emerges with competitive capabilities.

This rapid iteration keeps startups, tech giants, and researchers in constant motion. The Nemotron open-source model platform covers biomedicine, physical AI, intelligent agents, robotics, and autonomous driving, with multiple versions ranking highly in independent rankings and widely adopted by companies of all sizes.

Efficiency That Pays Off: Tokens per Watt and Dollar

Although Vera Rubin consumes twice the energy of its predecessors, performance multiplies disproportionately. The critical metric is tokens generated per watt and per dollar: a tenfold increase.

For a 1 GW data center, this means Spectrum-X improves throughput by 25%, equivalent to saving $5 billion in infrastructure. “This network system is practically free,” said the executive.

Solving the KV Cache: The Biggest Obstacle in Generative AI

The industry’s real headache is the “KV Cache,” the working memory AI consumes during long conversations. As models grow and conversations expand, available HBM memory runs out.

Vera Rubin addresses this by deploying BlueField-4 processors inside each rack. Each node contains 4 BlueField-4s, providing 150TB of distributed context memory among GPUs, with an additional 16TB per GPU maintaining 200Gbps bandwidth. Thus, thousands of dispersed GPUs across dozens of racks function as a single coherent memory.

The Geopolitical Significance of This “Nuclear Bomb”

The Vera Rubin presentation represents something deeper than technical innovation. In an era of skepticism about whether the AI bubble is sustainable, Jensen Huang needed to demonstrate with concrete facts what real capabilities AI enables: from safe autonomous driving to industrial robots, from protein synthesis to open-source systems that democratize technology.

Years ago, the company sold “shovels in the gold rush.” Now, it directly participates in transforming physical industries, from automotive to manufacturing. This evolution from component supplier to ecosystem orchestrator marks a fundamental transition in how the tech industry positions itself for the next decade.