Nvidia Vera Rubin Used by Google Could Next and Thinking Machines Lab

Key Takeaways

• NVIDIA Vera Rubin promises 10× inference performance per watt
• Google Cloud A5X racks scale to 80,000 GPUs per site
• Multisite clusters can host up to 960,000 Rubin GPUs
• OpenAI uses Vera Rubin for large‑scale ChatGPT inference on Google Cloud
• NVIDIA projects $1 trillion in Blackwell/Rubin orders through 2027

Pulse Analysis

The Vera Rubin platform represents a paradigm shift in AI hardware architecture. By tightly integrating the Rubin GPU with the Vera CPU and leveraging Groq LPU for inference disaggregation, NVIDIA claims a tenfold improvement in performance per watt. This efficiency gain translates into lower operational expenses for data centers, making it feasible for enterprises to run ever‑larger language models without proportional cost spikes. The platform’s design also emphasizes deterministic low‑latency data movement, a critical factor for real‑time agentic applications.

Google Cloud’s A5X rollout showcases how cloud providers can capitalize on this hardware breakthrough. The A5X system, built on NVIDIA’s NVL72 rack‑scale modules and ConnectX‑9 SuperNICs, enables clusters of up to 80,000 Rubin GPUs at a single site and nearly a million across multiple sites. Such scale delivers up to ten times higher token throughput per megawatt and slashes inference cost per token, directly addressing the economic bottlenecks that have limited widespread generative AI deployment. Early adopters like Thinking Machines Lab and OpenAI are already testing the limits, running intensive workloads such as ChatGPT inference on Google Cloud’s infrastructure.

The broader market implications are profound. With NVIDIA forecasting $1 trillion in Blackwell and Rubin orders by 2027, the hardware ecosystem is poised for rapid expansion, prompting cloud rivals to accelerate their own AI‑optimized offerings. Enterprises can now consider moving mission‑critical AI services to the cloud with confidence that performance, cost, and scalability will meet next‑generation demands. This convergence of cutting‑edge silicon and cloud services is likely to accelerate AI integration across sectors ranging from finance to healthcare, reinforcing the strategic importance of AI‑first infrastructure investments.