Aolani and Rafay Systems have announced one of the industry's first deployments of NVIDIA DSX OS on NVIDIA GB200 NVL72 infrastructure, marking another milestone in the evolution of enterprise AI infrastructure. Rather than focusing solely on GPU availability, the collaboration emphasizes production-ready AI platforms that enable organizations to rapidly deploy, govern, and scale AI workloads across enterprise and cloud environments.

Aolani and Rafay Systems have partnered to deploy NVIDIA DSX OS on NVIDIA GB200 NVL72 infrastructure, demonstrating how next-generation AI hardware can be transformed into production-ready enterprise AI platforms.

The deployment combines Aolani's AI cloud infrastructure with the Rafay Platform, enabling organizations to provision AI environments, automate infrastructure operations, and manage large-scale GPU resources through a centralized software layer. The companies say the collaboration addresses a growing challenge facing enterprise AI adoption: operationalizing advanced AI infrastructure rather than simply deploying high-performance hardware.

As organizations continue investing in accelerated computing, attention is increasingly shifting from GPU procurement to platform readiness. Enterprises now require infrastructure capable of supporting AI model development, training, inference, governance, and self-service provisioning without lengthy deployment cycles.

NVIDIA DSX OS is designed to simplify AI infrastructure operations by providing a software foundation for managing accelerated computing environments. Combined with NVIDIA GB200 NVL72, one of NVIDIA's high-performance AI computing platforms, the deployment supports enterprise AI workloads requiring significant computational performance and scalability.

According to the companies, the solution extends beyond GPU infrastructure by enabling organizations to provision Kubernetes clusters, virtual machines, AI workspaces, and inference environments through a self-service portal while maintaining centralized governance and policy enforcement.

This operational layer is becoming increasingly important as enterprises scale AI initiatives across multiple teams and business units. Instead of manually configuring infrastructure for every AI project, organizations can provide developers with standardized environments while maintaining security, compliance, and operational visibility.

The Rafay Platform contributes orchestration, lifecycle automation, multi-tenancy, and infrastructure management capabilities that allow enterprises and cloud providers to operate AI environments more efficiently. Multi-tenancy enables multiple users or organizations to securely share the same physical infrastructure while maintaining workload isolation and governance controls.

The announcement reflects a broader evolution in enterprise AI infrastructure. During the early stages of generative AI adoption, organizations primarily focused on securing access to high-performance GPUs. As deployments mature, software platforms that automate infrastructure provisioning, workload management, and operational governance are becoming equally important.

Production-ready AI platforms reduce the complexity of deploying AI environments by integrating infrastructure management, security policies, automation, and developer tools into a unified operational framework. This enables organizations to move from hardware installation to AI application development more quickly.

The deployment also highlights the growing role of Kubernetes in enterprise AI. Kubernetes has become the dominant orchestration platform for containerized applications and increasingly serves as the operational foundation for AI training clusters, inference services, and machine learning platforms.

According to IDC, enterprise spending on AI infrastructure continues to accelerate as organizations expand investments in generative AI, high-performance computing, and cloud-native platforms. Gartner similarly identifies AI engineering, platform operations, and infrastructure automation as essential capabilities for organizations scaling enterprise AI initiatives.

Competition within the AI infrastructure market continues to intensify. Technology providers including NVIDIA, Microsoft, Google Cloud, Amazon Web Services (AWS), Oracle, Dell Technologies, Hewlett Packard Enterprise (HPE), and Red Hat are investing heavily in AI infrastructure, GPU cloud services, Kubernetes management, and AI platform software.

For enterprises, the challenge is increasingly operational rather than computational. While advanced GPU infrastructure provides the processing power required for modern AI models, organizations also require governance, automation, developer access, and lifecycle management to achieve meaningful business outcomes.

The partnership between Aolani and Rafay reflects this shift toward integrated AI platforms that combine hardware, orchestration, automation, and security into a unified enterprise offering. Such platforms can help reduce deployment complexity while accelerating AI adoption across development teams.

As AI infrastructure continues evolving, industry focus is expected to move beyond compute capacity toward operational efficiency, infrastructure utilization, and developer productivity. The collaboration demonstrates how software-defined AI operations are becoming a critical layer in enabling enterprises to transform GPU investments into scalable AI services that support model training, inference, and future AI-driven applications.

Market Landscape

Enterprise AI infrastructure is rapidly evolving from hardware-centric deployments to software-defined AI platforms. Organizations are investing in GPU orchestration, Kubernetes management, AI platform engineering, and lifecycle automation to improve infrastructure utilization and accelerate AI application development. As next-generation GPU systems become more widely available, enterprise success increasingly depends on governance, automation, and operational readiness rather than compute capacity alone.

Top Insights

• Aolani and Rafay have deployed NVIDIA DSX OS on NVIDIA GB200 NVL72 infrastructure to support production-ready enterprise AI platforms.
• The collaboration combines GPU infrastructure with orchestration, lifecycle automation, governance, and self-service capabilities for AI workloads.
• Enterprise AI strategies are shifting from hardware deployment toward operational software that accelerates developer productivity and infrastructure utilization.
• Kubernetes, automation, and multi-tenancy are becoming foundational technologies for managing large-scale AI environments securely and efficiently.
• Production-ready AI platforms help organizations shorten deployment timelines while supporting AI model training, inference, and enterprise governance.

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