The economics alone turn heads. But the technology behind this bold claim reveals why Nvidia remains untouchable in the AI chip arena. The Rubin CPX targets a specific pain point that’s been haunting AI developers: processing enormous amounts of data for video creation and software generation.
The Million-Token Problem
Modern AI faces a crushing bottleneck. Processing just one hour of video content demands up to 1 million tokens—units of data that AI models digest. Traditional graphics processors buckle under this load. Meanwhile, AI systems have evolved from simple chatbots into sophisticated agents that write entire software programs and generate high-definition videos.
These advanced models need to understand entire codebases, maintain cross-file dependencies, and grasp repository structures. They’re not just autocompleting your sentences anymore. They’re becoming intelligent collaborators that require unprecedented computing muscle.
Nvidia’s solution integrates previously separate processing steps directly into the chip. Video decoding, encoding, and inference—the moment when AI produces its output—now happen together instead of bouncing between different components.
The Architecture Revolution
The Rubin CPX doesn’t work alone. It forms part of Nvidia’s disaggregated inference strategy, which splits AI processing into two distinct phases. The context phase devours compute power, analyzing massive input data. The generation phase needs lightning-fast memory transfers to produce outputs token by token.
This separation allows each phase to run on hardware optimized for its specific demands. Think of it as having a sprinter handle the short bursts while a marathon runner tackles the long haul.
The technical specifications read like science fiction. The Rubin CPX delivers 30 petaFLOPs of NVFP4 compute power and packs 128 GB of GDDR7 memory. Hardware acceleration for video processing comes built-in. Attention mechanisms—crucial for understanding context in AI—run three times faster than Nvidia’s current GB300 NVL72 systems.
The Complete Package
Nvidia packages this technology into the Vera Rubin NVL144 CPX rack—a behemoth containing 144 Rubin CPX GPUs, 144 standard Rubin GPUs, and 36 Vera CPUs. This single rack delivers 8 exaFLOPs of compute power, representing a 7.5-fold increase over the GB300 NVL72.
The system offers 100 terabytes of high-speed memory with 1.7 petabytes per second of memory bandwidth. These numbers matter because they determine how quickly AI can process and generate complex outputs.
Supporting infrastructure includes Nvidia’s Quantum-X800 InfiniBand or Spectrum-X Ethernet networking, paired with ConnectX-9 SuperNICs. The Dynamo platform orchestrates everything, ensuring components work in harmony.
The Stakes Keep Rising
Wall Street watches closely as companies pour hundreds of billions into AI hardware. The pressure to demonstrate returns intensifies daily. Nvidia’s promise of 30x to 50x return on investment addresses this concern directly.
The company already dominates the AI chip market, holding the crown as the world’s most valuable company. But competition lurks. Every major tech player wants a piece of the AI acceleration market. By targeting specific high-value workloads—video generation and complex software development—Nvidia sharpens its competitive edge.
The Rubin architecture succeeds Nvidia’s current Blackwell technology, marking the company’s continued evolution from selling individual chips to providing complete processing systems. Each generation brings exponential improvements in capability while addressing specific bottlenecks that limit AI advancement.
As AI systems grow more sophisticated, they demand infrastructure that can keep pace. Tasks once considered impossible—like AI writing entire applications or generating feature-length videos—edge closer to reality. The Rubin CPX represents Nvidia’s bet that solving the long-context processing challenge unlocks the next wave of AI breakthroughs.
The countdown to late 2026 begins. If Nvidia delivers on its promises, the Rubin CPX could accelerate AI’s transition from impressive demos to transformative real-world applications. For companies investing billions in AI infrastructure, that transformation can’t come soon enough.
Written by Alius Noreika
