Yes, NVIDIA's Vera Rubin platform will offer cloud-based development environments. The Vera Rubin-based products are slated to be available through major cloud providers, including Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure, along with other NVIDIA Cloud Partners, starting in the second half of 2026. This widespread availability through cloud partners means that developers will be able to access and utilize the Vera Rubin platform's supercomputing capabilities within cloud-based infrastructure. The platform is designed to support a wide range of AI workloads, from large-scale pretraining to real-time inference, making it suitable for complex, multi-step autonomous AI workflows in a cloud setting.
The integration of Vera Rubin with these cloud platforms signifies that users will leverage the underlying hardware and software stack within a managed cloud environment. For instance, Google Cloud plans to be among the first to offer NVIDIA Vera Rubin NVL72 rack-scale systems, integrating them into their AI Hypercomputer architecture to support the next generation of reasoning and agentic AI. Similarly, Microsoft has already powered on NVIDIA's Vera Rubin NVL72 systems, deploying them across Azure datacenters, with an emphasis on supporting diverse workload types through unified platforms. These cloud offerings will provide the necessary infrastructure, including the Vera CPU and Rubin GPU racks, to facilitate the development, training, and deployment of agentic AI models.
This approach allows for a "cloud-first" strategy, enabling organizations to access the powerful Vera Rubin architecture without the need for significant on-premises hardware investment. Developers can utilize these cloud-based environments for tasks such as testing and validating AI models, especially those relying on reinforcement learning and agentic AI. The availability through cloud providers like Zilliz Cloud for vector database needs, alongside the computational power of Vera Rubin, streamlines the entire AI development lifecycle, from initial experimentation to large-scale deployment. The platform's design, which emphasizes efficiency and scalability for AI factories, further ensures that cloud-based development environments built on Vera Rubin can handle demanding AI workloads effectively.