NVIDIA GTC Taipei 2026: Seven Launches Signal the Arrival of the Agentic AI Era
At 11:00 AM on June 1, Taipei Music Center.
NVIDIA CEO Jensen Huang stood on stage and delivered a two-hour keynote that the industry has called “the most important day for the PC industry in 40 years.”
There was only one core thesis: AI has moved from “generating content” to “getting work done,” and Agentic AI is the signature of this shift.
Seven flagship products were announced in a single event. This was not a product refresh—it was a systemic declaration of a new paradigm.
The Seven Launch List
| No. | Product | Core Positioning |
|---|---|---|
| 1 | Vera Rubin | Fully mass-produced rack-scale AI supercomputer |
| 2 | Vera CPU | NVIDIA’s first self-developed data center processor |
| 3 | Nemotron 3 Ultra | World’s first open model with SSM+MoE hybrid architecture |
| 4 | RTX Spark + N1X | Redefining the PC ecosystem in partnership with Microsoft |
| 5 | Cosmos 3 | Open-source foundation model for physical AI |
| 6 | Alphamayo 2 | Open reasoning model for autonomous driving |
| 7 | Isaac GR00T | Humanoid robot development platform |
Core Thesis: Token Economics
Throughout the keynote, Huang anchored one logic: compute is revenue, and tokens per watt is profit margin.
He used a set of data to refute the “AI will reduce employment” narrative:
- GitHub code commits in 2023: approximately 300 million
- Early 2026: approaching 900 million
- Total global engineer compensation: approximately $3 trillion
- Productivity value achievable under agent amplification: approximately $9 trillion
“That is complete nonsense. AI is not taking jobs—it is exponentially amplifying the output of every engineer.”
Vera Rubin: The Supercomputer Built for Agents
Vera Rubin has officially entered full-scale production. This is the most complex end-to-end rack-level system in NVIDIA history:
| Component | Specification |
|---|---|
| GPU | Vera Rubin NVL72 (NVLink 72 interconnect) |
| CPU | Vera CPU (self-developed Olympus architecture) |
| Network | ConnectX-9 + world’s first 200Gb CPO optical Spectrum-X switch |
| Security | BlueField-4 security processor (confidential computing standard) |
| Storage | New agent-native memory system architecture |
| Assembly Time | Compressed from 2 hours to 5 minutes |
The supply chain is twice the scale of Grace Blackwell, involving 150 partners in Taiwan.
Vera CPU: NVIDIA Enters the CPU Market
This was the most strategically significant announcement of the entire keynote.
Huang’s argument: “The number of agents in the future will far exceed the number of humans, and they are extremely impatient with latency. Traditional CPUs designed for ‘humans’ are inherently ill-suited to nanosecond-level heterogeneous computing. This is a massive new market that NVIDIA has created out of thin air.”
Benchmark data:
- SQL database processing: 3x speedup
- NYSE real-time stream processing: 6x speedup
Key architectural advantage: monolithic mesh interconnect eliminating chiplet overhead, with core bandwidth of 3.6 TB/s.
RTX Spark: Redefining the PC
Jointly developed by NVIDIA and Microsoft over three years, marking the first comprehensive PC revolution in 40 years.
| Specification | Data |
|---|---|
| GPU | Blackwell RTX, 6144 CUDA cores, 1 PFLOP AI compute |
| CPU | 20-core Grace (co-developed with MediaTek, codename N1X) |
| Memory | 128GB unified memory |
| Process | TSMC 3nm, 70 billion transistors |
| Interconnect | NVLink unified interconnect |
Demo scenario: running the Hermes framework locally, connecting to cloud-based Claude Sonnet, AI autonomously invokes Rhino modeling, Blender rendering, and Flux 2 image generation—transforming a sketch into a professional 3D render, with full self-correction throughout the process.
Huang’s prediction: in ten years, home AI supercomputers could be as common as home theater systems.
Cosmos 3: The Foundation Model for Physical AI
The biggest pain point for physical AI is data—the vast majority of video is third-person perspective, while robots need first-person.
Cosmos 3’s solution: fusing autoregressive and diffusion Transformer architectures, unifying pixels, actions, sounds, and language into a single processing pipeline, directly generating synthetic video that conforms to real-world physical laws.
As the “virtual mentor” for robots, Cosmos 3 is fully open-source—model, data, and training methods all publicly available.
Built on Cosmos 3, NVIDIA also announced:
- Alphamayo 2: an open reasoning model for autonomous driving, with approximately 80% of global automakers already onboard the Hyperion platform
- Isaac GR00T: a reference development platform for humanoid robots, 31 degrees of freedom (including 25-DOF dexterous hands), powered by Jetson Thor, ready out of the box in hours
The Nature of Competitive Moats Has Shifted
Huang invited 11 leaders from the open-model ecosystem—including Mistral, Perplexity, Cursor, and Thinking Machines Lab—to join him on stage.
He offered a new perspective: “Proprietary and open are not opposing concepts. You can be both proprietary and open.”
Cursor CEO Michael Truell’s assessment was even more direct: the industry is witnessing the rise of a third category of company—neither pure foundation-model labs nor pure application builders, but new AI companies that integrate models, systems, and products into a single stack.
NVIDIA’s competitive moat is no longer just about single-GPU performance. It is the full-stack integration and extreme co-design capability spanning from chip to rack, from software to operations, from cloud to edge PC.
The Core Signal
Looking back at the entire keynote, there is only one core signal:
NVIDIA is no longer a GPU company. It is the irreplaceable infrastructure monopoly of the agent era.
When the cost of a single AI factory approaches the $100 billion scale, whoever helps customers deploy faster, operate more efficiently, and generate profit over longer cycles will win this era.
Sources
- TMTPost, June 1, 2026
- Juejin, June 1, 2026
- TradingKey, June 1, 2026
- East Money, June 1, 2026
- NVIDIA Official GTC Taipei Page