At GTC Taipei @ COMPUTEX 2026 (early June), NVIDIA unveiled the Vera Rubin platform with a range of new chips going into production, aimed at scaling up "AI factories" — compute centers built for large-scale AI. According to NVIDIA, the platform covers the entire cycle from training to agentic inference (autonomous AI agents).
Quick summary
- When: GTC Taipei @ COMPUTEX 2026, early June 2026.
- What: the Vera Rubin platform with a range of new chips (Vera CPU, Rubin GPU, NVLink, ConnectX, BlueField, Spectrum).
- Aimed at: scaling up the 'AI factory', serving both training and agentic inference.
- Why it matters: the hardware for autonomous AI agents is maturing.
What happened?
According to NVIDIA, the Vera Rubin platform comprises multiple components working together — CPU, GPU, high-speed interconnect switches, SuperNIC and network DPU — to run large AI compute clusters. The goal is to boost performance and efficiency for both model training and running inference for AI agents.
Why this matters
More powerful and more efficient hardware is the precondition for autonomous AI agents to become practical. What matters for enterprises: as AI infrastructure becomes increasingly available and optimized, building your own AI infrastructure (instead of only renting cloud) becomes more feasible in terms of both performance and cost.
An enterprise perspective
Namtech deploys internal AI on on-premises hardware (Apple Silicon, energy-efficient) — proof that enterprises don't necessarily have to depend entirely on the cloud to have powerful AI. The trend of maturing AI hardware only reinforces this choice.
Frequently asked questions
What is Vera Rubin?
It's the name of NVIDIA's AI compute platform announced at COMPUTEX 2026, comprising multiple chips/components that power 'AI factory' centers for both training and inference.
How does this affect Vietnamese enterprises?
Increasingly powerful and efficient AI hardware makes building/deploying AI on-premises more feasible in terms of performance and cost.
Do you need such massive hardware to run internal AI?
Not necessarily. Many internal enterprise AI tasks run well on modest hardware (for example, an energy-efficient Apple Silicon cluster); the scale depends on your needs.
Own your AI infrastructure
Namtech deploys internal AI on on-premises hardware — without depending entirely on the cloud.
Book a free consultationNote: This article is compiled from public sources as of 23/06/2026; information is for reference and may change.