Aolani and Rafay deploy NVIDIA DSX OS on GB200 NVL72 infrastructure

Aolani and Rafay Systems are deploying NVIDIA DSX OS on NVIDIA GB200 NVL72 infrastructure in order to build a fully managed platform tailored for enterprise and cloud-based artificial intelligence services. By pairing Aolani’s specialised AI cloud computing infrastructure with Rafay’s operational software, the two organisations aim to streamline the management and governance of complex computing environments.

The initiative represents one of the earliest industry deployments of NVIDIA DSX OS on the GB200 NVL72 framework. As part of its broader technical strategy, Rafay Systems participates in NVIDIA Inception, a global programme designed to assist technology companies developing solutions on NVIDIA platforms.

Preparing AI infrastructure for customer use

Simply installing GPU systems on a data centre floor does not render hardware immediately usable for software developers or enterprise clients. Infrastructure providers are still required to configure complex computing environments, establish access controls, isolate distinct workloads, and handle continuous maintenance long after physical setup is complete.

Designed to address these exact operational challenges, the combined platform allows customers to deploy Kubernetes clusters, virtual machines, custom AI workspaces, and dedicated execution spaces for trained models through a unified self-service portal. Meanwhile, system administrators retain centralised control over access rights, governance policies, and operational rules. This architecture grants operators end-to-end visibility across the entire compute stack, enabling them to balance resources and enforce uniform guidelines across varied internal teams or external client environments. Furthermore, multi-tenant functionality ensures that distinct users can share underlying hardware safely without exposing or blending their underlying workloads. Rafay’s software takes on the operational weight by automating both initial configurations and subsequent environment management.

Both companies anticipate that this platform will streamline model development, training, inference, and next-generation AI applications without forcing cloud providers to piece together custom operational software independently.

“Aolani has always been committed to delivering faster time-to-value for our customers,” said Nicholas Chia, CEO of Aolani. “Our customers are at the bleeding edge of AI development, and they need to provision, govern, and scale from day one in an industry that moves at lightning speed. Building the next generation of AI cloud means solving for more than just compute capacity, but also production-grade platforms that enable operational readiness from the get go. That’s why we are so excited about this partnership with Rafay.”

Reducing the time between installation and service delivery

This strategic collaboration primarily focuses on minimising the delay between installing raw NVIDIA infrastructure and delivering operational AI services to commercial end users.

Through this approach, developers can provision computing environments on demand without waiting for IT teams to configure each setup manually. Simultaneously, system operators can oversee these environments through a single management layer, enforcing policies while tracking compute utilisation in real time.

As enterprise organisations, cloud providers, and operators of sovereign AI infrastructure invest heavily in cutting-edge GPU arrays, commercial success increasingly depends on speed to market. Building compute capacity is only half the battle, as the true commercial value rests on how rapidly providers can onboard new users, manage multi-tenant environments, and maintain strict administrative oversight.

“AI infrastructure has entered a new phase,” said Haseeb Budhani, CEO and co-founder of Rafay Systems. “The question is no longer how quickly organisations can deploy GPUs. It’s how quickly they can transform that infrastructure into a governed, self-service platform that developers can use and operators can manage at scale. We’re excited to collaborate with Aolani to help demonstrate what’s possible with NVIDIA AI infrastructure and accelerate the path from hardware deployment to production AI services.”

In closing, Aolani and Rafay emphasised that the deployment will optimise infrastructure utilisation, speed up customer onboarding, and serve as a reliable foundation for upcoming AI offerings.