From Ryuta Hamamoto at TIMEWELL
This is Ryuta Hamamoto from TIMEWELL Corporation.
In January 2026, NVIDIA announced the Vera Rubin platform at CES 2026 — marking a new phase in AI computing infrastructure. This article covers the architecture in detail, traces the history that led here, and examines what the roadmap through 2028 means for enterprise AI strategy.
NVIDIA GPU: January 2026 Specs at a Glance
| Specification | Detail |
|---|---|
| Rubin GPU transistors | 33.6 billion (two reticle-size dies) |
| Manufacturing process | TSMC 3nm |
| Inference performance | 50 PFLOPs (NVFP4) — 5x Blackwell |
| Training performance | 35 PFLOPs (NVFP4) — 3.5x Blackwell |
| Memory | HBM4, 288GB max |
| Memory bandwidth | 22 TB/s |
| Vera CPU | 22.7 billion transistors, 88 cores / 176 threads |
| NVL72 configuration | 72 GPUs + 36 CPUs, 3.6 EFLOPs |
| Inference token cost | 1/10th of Blackwell |
| Availability | H2 2026 (through partners) |
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The Vera Rubin Platform
Rubin GPU
Named after astronomer Vera Rubin, the Rubin GPU is NVIDIA's next-generation AI accelerator.
Specifications:
- 33.6 billion transistors across two reticle-size dies
- TSMC 3nm process
- 50 PFLOPs NVFP4 inference performance
- 35 PFLOPs NVFP4 training performance
- HBM4 memory up to 288GB
- 22 TB/s memory bandwidth
Vera CPU
The Vera CPU is the companion processor designed to work in tandem with the Rubin GPU.
Specifications:
- 22.7 billion transistors
- Custom Arm "Olympus" cores
- 88 cores, 176 threads (Spatial Multi-Threading)
- LPDDR5x memory up to 1.5TB
- 1.2 TB/s memory bandwidth
- 1.8 TB/s NVLink-C2C bandwidth
NVL72: The Rack-Scale System
NVL72 is the flagship configuration of the Vera Rubin platform.
NVL72 configuration:
- 72 Rubin GPUs + 36 Vera CPUs
- NVLink 6 interconnect
- 3.6 EFLOPs NVFP4 inference performance
- 2.5 EFLOPs training performance
- 20.7TB HBM4 capacity
- 54TB LPDDR5x capacity
- 1.6 PB/s HBM bandwidth
- 260 TB/s scale-up bandwidth
Efficiency Gains vs. Blackwell
| Metric | Improvement |
|---|---|
| Inference token cost | 10x lower |
| GPUs required for MoE training | 4x fewer |
| Inference performance | 5x |
| Training performance | 3.5x |
NVIDIA GPU History: From 1993 to AI Factories
1993 — Founding and the GPU
NVIDIA was founded in Silicon Valley with a strategic insight: rather than compete with general-purpose CPUs, focus on a specific computational problem. That led to the GPU — a new category of processor optimized for graphics. The company secured investment from Sequoia Capital and others and created the 3D graphics market.
2006 — CUDA
CUDA made GPUs useful for general-purpose computation. This was the architectural decision that positioned NVIDIA for the AI era: researchers and developers gained access to GPU compute through a programmable interface, establishing the "CUDA Everywhere" ecosystem that became the foundation of deep learning.
2012 — AlexNet and the Deep Learning Breakthrough
AlexNet's performance on computer vision benchmarks — enabled by GPU-accelerated training — launched the deep learning era. The combination of GPU compute and neural network architectures produced results that CPU-based systems couldn't match. NVIDIA was positioned as the infrastructure provider for a new computing paradigm.
2016 — DGX-1 and the AI Factory Concept
The DGX-1 was the world's first purpose-built AI supercomputer. Adopted by OpenAI and research institutions globally, it established the concept of AI infrastructure as a dedicated computing environment — the precursor to what NVIDIA now calls the AI factory.
AI Factories and Enterprise ROI
An AI factory is not just a GPU cluster. It's an integrated computing environment — GPU + network + software — designed to support AI development from training through production inference, with the same software stack across hardware generations.
Meta's advertising optimization case:
Meta deployed NVIDIA GPUs for advertising optimization, applying machine learning to ad targeting. The results included measurable business performance improvements and strong market valuation recovery. ROI on AI infrastructure investment exceeded 300% in some reported cases.
The shift from retrieval to generation:
Traditional compute workloads were largely retrieval-based. Generative AI changes the model: real-time content generation, advanced recommendation systems, algorithmic financial modeling, and medical diagnostic support all require sustained inference compute at scale. This is the workload the Vera Rubin platform is designed for.
Roadmap Through 2028
Rubin Ultra (2027)
| Specification | Detail |
|---|---|
| Inference performance | 100 PFLOPs — 2x Rubin |
| Memory | HBM4e |
| Flagship configuration | NVL576 (576 GPUs) |
Feynman (2028+)
NVIDIA has added the Feynman architecture to its published roadmap beyond Rubin Ultra.
Competitive Landscape
NVIDIA vs. AMD
| Item | NVIDIA Rubin | AMD Instinct MI450 |
|---|---|---|
| Inference performance | 50 PFLOPs | Not disclosed |
| Memory | HBM4, 288GB | HBM3e, 192GB |
| Ecosystem | CUDA (dominant market share) | ROCm |
| Market share | 80%+ | ~15% |
NVIDIA vs. Google TPU
| Item | NVIDIA Rubin | Google TPU v7 Ironwood |
|---|---|---|
| Performance | 50 PFLOPs | 4.6 PFLOPs/chip |
| Scale | NVL72 (72 GPUs) | 9,216-chip Pod |
| Delivery model | Hardware sales | Cloud service |
| Use case | General-purpose AI | Google-internal optimization |
Then vs. Now
| Item | 2024 (Blackwell launch) | January 2026 |
|---|---|---|
| Current GPU | Blackwell B200 | Rubin |
| Inference performance | 10 PFLOPs | 50 PFLOPs |
| Memory | HBM3e, 192GB | HBM4, 288GB |
| Memory bandwidth | 8 TB/s | 22 TB/s |
| Rack configuration | NVL72 (Blackwell) | NVL72 (Rubin) |
| Manufacturing node | TSMC 4nm | TSMC 3nm |
| Next generation preview | Rubin (2026) | Rubin Ultra (2027) |
| Primary workload focus | Training-centric | Inference optimization |
Enterprise Adoption Considerations
Advantages:
- Industry-leading inference and training performance
- 5x performance improvement over Blackwell
- CUDA ecosystem with mature tooling and developer support
- 10x inference token cost reduction — critical for production AI deployment
Considerations:
- High capital cost; datacenter-scale investment required
- Supply constraints may require long-term procurement planning
- Export regulations affect availability in certain regions
Summary
The Vera Rubin platform defines NVIDIA's position in 2026 AI computing:
- Rubin GPU: 33.6 billion transistors, TSMC 3nm, 50 PFLOPs inference
- HBM4 memory: 288GB, 22 TB/s — 2.75x Blackwell bandwidth
- Vera CPU: 88 cores / 176 threads, custom Arm "Olympus" cores
- NVL72: 72 GPUs + 36 CPUs, 3.6 EFLOPs — among the highest AI compute density available
- Inference token cost: 1/10th of Blackwell — fundamentally changes production AI economics
- 2027: Rubin Ultra (100 PFLOPs) and NVL576
From the 1993 invention of the GPU through thirty years of development, NVIDIA has consistently pursued the vision of accelerated computing. Vera Rubin is the latest chapter — infrastructure for the era in which AI permeates every domain of industry and enterprise.
For organizations evaluating AI infrastructure investment, the 10x reduction in inference token cost is the most significant number. At that cost point, AI applications that were previously economically marginal become viable at scale.