Nvidia's Vera Rubin and Reuben Ultra AI Chips: A New Era in Computing

The AI hardware race just took a monumental leap forward. With Nvidia’s newly unveiled Vera Rubin and Reuben Ultra GPUs, industries are about to see computing power reach unprecedented heights.

Introduction to Nvidia’s New AI Chips

At GTC 2025 in San Jose, Nvidia CEO Jensen Huang outlined the company’s roadmap for the next generation of AI accelerators. Building on the success of the Blackwell series, Nvidia is pushing boundaries with two flagship GPU architectures: the Vera Rubin, slated for late 2026, and the even more powerful Reuben Ultra, expected in 2027. Alongside these, the Feynman architecture serves as a visionary glimpse into 2028 and beyond. Together, these chips underscore Nvidia’s commitment to delivering the compute muscle needed for ever-growing AI workloads in research, enterprise, and edge applications.

Overview of the Vera Rubin GPU

Named after the pioneering astronomer Vera Rubin, the Vera Rubin GPU is designed to accelerate both training and inference tasks. Key specifications include:

• 288 GB of high-bandwidth memory (HBM3e), matching transcript figures.
• Pairing with Nvidia’s custom “Vera” CPU, built on 88 ARM-based cores and supporting 176 processing threads over an ultra-fast NVLink connection at 1.8 TB/s.
• An impressive 50 petaflops of FP4 inference performance per chip.
  When deployed in an NVL 144 rack configuration, a cluster of these GPUs achieves a total of 3.6 exaflops of FP4 inference throughput—3.3 times the power of the prior Blackwell Ultra system [verify]. This combination of massive memory, high-speed interconnects, and threaded CPU support aims to slash data-loading bottlenecks and deliver smooth scaling for large AI models.

Details on the Reuben Ultra GPU

Following Vera Rubin, Nvidia will introduce the Reuben Ultra series in 2027 to tackle even more demanding AI tasks. Unlike its predecessor, each Reuben Ultra GPU comprises four discrete processing dies, each delivering 100 petaflops of FP4 precision. Connected via the NVL 576 rack, a full system promises:

• 15 exaflops of FP4 inference compute power.
• 5 exaflops of FP8 training performance.
  This represents a fourfold gain over the NVL 144 Vera Rubin setup. Additionally, each Reuben Ultra GPU houses 1 TB of HBM4E memory, with a rack-level aggregate of 365 TB of ultra-fast on-package storage. Such staggering specifications are engineered to sustain the next wave of massive generative models, physics simulations, and real-time analytics.

The Feynman Architecture Teaser

Nvidia also teased a long-term architecture codenamed “Feynman,” honoring Nobel laureate Richard Feynman and his vision for computing at quantum scales. Although details remain sparse, Huang confirmed that Feynman will leverage the proven Vera CPU and Nvidia’s advanced interconnects. Slated for a 2028 debut, Feynman is expected to introduce novel die-stacking techniques or next-gen tensor engines to further boost efficiency. By evoking Feynman’s emphasis on reducing complexity and increasing parallelism, Nvidia hints at an architecture focused on delivering even higher compute density per watt.

Performance Metrics of the Blackwell Ultra B3 Upgrade

While eyes remain on Vera Rubin and Reuben Ultra, Nvidia also announced an interim upgrade: the Blackwell Ultra B3 GPU, due in late 2025. It will feature:

• Two high-performance processors delivering 15 petaflops of dense FP4 compute per chip.
• 288 GB of HBM3e memory, up from 192 GB in the prior Blackwell models.
  In a complete NVL 72 rack, the B3 system will deliver 1.1 exaflops of FP4 inference compute, a 1.5× uplift versus the existing Blackwell B200 configuration. These incremental gains ensure Nvidia continues to meet rising AI demands even as its next-gen lines roll out.

Future Implications for AI Technology

Such leaps in GPU horsepower are poised to reshape AI research and deployment in several ways:

• Training gargantuan language and vision models will require fewer synchronized nodes, reducing overhead.
• Real-time inference for interactive agents and multimodal applications becomes feasible at data-center and edge scales.
• Energy-efficiency improvements per flop can lower operational carbon footprints for large AI infrastructures.
  By extending the horizons of model size and complexity, these chips encourage innovators to explore new AI frontiers—from real-world robotics to high-fidelity scientific simulations.

Real-World Applications of Nvidia’s New GPUs

Nvidia’s upcoming GPUs are set to deliver transformative gains across industries:

1. Healthcare

   • Accelerated analysis of high-resolution medical scans for earlier, more accurate diagnostics.
   • Faster molecular dynamics simulations to speed up drug discovery and personalized medicine.

2. Autonomous Driving

   • Real-time sensor fusion and object recognition improvements to enhance safety in complex driving scenarios.
   • On-board inference gains that reduce reliance on remote servers and minimize latency.

3. Finance

   • Real-time risk modeling and fraud detection powered by sub-millisecond inferencing on large trading datasets.
   • Enhanced accuracy for algorithmic trading strategies through deeper neural-network ensembles.

4. Space Exploration

   • High-throughput processing of telescope imagery and satellite telemetry to unlock new astrophysical insights.
   • Large-scale simulations of planetary atmospheres and orbital mechanics for mission planning.

These examples highlight how Nvidia’s Vera Rubin, Reuben Ultra, and future Feynman GPUs are tailored to meet diverse computational challenges.

Conclusion

Nvidia’s new AI chips are catalysts for the next wave of technological breakthroughs. As industries embrace the unparalleled performance of Vera Rubin, Reuben Ultra, Blackwell Ultra B3, and the forthcoming Feynman architecture, the boundaries of what’s possible continue to expand.

Bold Takeaway:

• Start evaluating your current AI pipelines today to identify where these upcoming GPUs can deliver the greatest performance uplift and cost efficiency.

What future applications of AI will benefit most from Nvidia’s advancements? Share your thoughts below and join the conversation!