Nutanix expands AI platform to public cloud to boost enterprise AI deployments

Nutanix, the hybrid multicloud computing provider, has announced the expansion of its AI infrastructure platform, Nutanix Enterprise AI (NAI), to support deployment across multiple environments, including public cloud services.  

The new offering aims to create a consistent multicloud operating model that supports AI workloads across various environments, allowing organizations to select secure locations for deploying their data and AI models. The solution integrates NVIDIA’s NIM for optimized performance, enabling faster deployment of large language models (LLMs) to support generative AI applications. 

Nutanix Enterprise AI is also designed to operate on Kubernetes platforms and can be implemented at the edge, in core data centers, and in public cloud environments such as AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKE), the company said. 

Furthermore, the platform is designed to support business-critical applications with necessary security, operational resilience, and ease of deployment. Additionally, Nutanix Enterprise AI includes open foundation models from providers like Hugging Face, further expanding its capabilities in AI model management. 

“With Nutanix Enterprise AI, we're helping our customers simply and securely run GenAI applications on-premises or in public clouds. Nutanix Enterprise AI can run on any Kubernetes platform and allows their AI applications to run in their secure location, with a predictable cost model,” said Thomas Cornely, SVP, Product Management, Nutanix. 

Nutanix Enterprise AI is a part of the Nutanix GPT-in-a-Box 2.0 suite, which also includes Nutanix Cloud Infrastructure, Nutanix Kubernetes Platform, and Nutanix Unified Storage. The suite supports customer requirements for configuration, on-premises training, and inferencing needs. 

Nutanix's integration with NVIDIA’s AI Enterprise software platform, including NIM microservices, ensures compatibility and reliability in high-performance AI applications.