AI workloads such as large language models and generative AI applications place significant demands on infrastructure. As these workloads move from experimentation into production, organizations must scale compute and accelerator resources while maintaining operational control, security, and flexibility across environments.

Nutanix Enterprise AI provides a platform for deploying and operating AI workloads across private cloud, hybrid cloud, and public cloud environments. It enables organizations to run AI inference workloads close to their data while maintaining a consistent infrastructure and operational model.

Infrastructure Requirements for Scalable AI

By integrating GPU-enabled infrastructure into the Nutanix Cloud Platform, organizations can scale AI workloads as demand grows without introducing separate management frameworks. This approach simplifies infrastructure planning and helps align AI deployments with existing operational processes.

Nutanix Enterprise AI supports a range of accelerator options, including NVIDIA, AMD, and Intel GPUs. This flexibility allows organizations to select hardware that aligns with performance requirements, availability, and cost objectives.

The ability to support multiple accelerator types helps organizations avoid dependency on a single hardware vendor and adapt their AI infrastructure as model requirements evolve. AI workloads can be matched to appropriate GPU resources based on throughput needs and deployment scale.

Operational Consistency for AI Deployments

Scaling AI workloads effectively requires more than hardware, it requires consistent deployment, monitoring, and management across environments. Nutanix Enterprise AI provides a unified operational experience for deploying and managing AI inference workloads on Kubernetes platforms.

This centralized approach helps reduce the complexity of managing multiple AI environments and supports consistent governance across teams. AI workloads can be deployed, monitored, and updated using familiar operational workflows, improving efficiency and control.

Supporting Hybrid and Multicloud AI Workloads

AI workloads often span multiple environments due to data locality, regulatory requirements, and cost considerations. Nutanix Enterprise AI supports deployment across on premises infrastructure, hybrid cloud environments, and public cloud Kubernetes services.

This flexibility allows organizations to place AI inference workloads where they make the most sense while maintaining consistent management and operational practices. Teams can scale AI services without redesigning infrastructure or adopting new operating models for each environment.

GPU Virtualization Considerations

While GPU acceleration is a core requirement for AI workloads, infrastructure planning must account for how GPU resources are allocated and managed. Nutanix Enterprise AI supports GPU-enabled environments that allow organizations to scale AI workloads based on available accelerator capacity.

Organizations should plan GPU configurations carefully to align with expected inference demand and operational requirements. Infrastructure sizing and accelerator selection play a key role in ensuring efficient and reliable AI performance.

Partnering with Choice Solutions

As a Premier Partner with Nutanix, Choice Solutions helps organizations plan and deploy AI infrastructure at scale using Nutanix Enterprise AI. Our experts work with customers to assess AI requirements, recommend appropriate accelerator configurations, and implement environments that are scalable, efficient, and aligned with operational goals.

We provide guidance on infrastructure selection, deployment workflows, and ongoing optimization to ensure AI deployments run effectively and deliver value. Contact us today to learn how Nutanix Enterprise AI can help you scale AI workloads efficiently and match infrastructure to your performance needs.