Nutanix extends AI platform to public cloud

Nutanix (NTNX)extended the company’s AI infrastructure platform with a new cloud native offering, Nutanix Enterprise AI, that can be deployed on any Kubernetes platform, at the edge, in core data centers, and on public cloud services like AWS EKS, Azure AKS, and Google GKE. The NAI offering delivers a consistent hybrid multicloud operating model for accelerated AI workloads, enabling organizations to leverage their models and data in a secure location of their choice while improving return on investment. Leveraging NVIDIA NIM for optimized performance of foundation models, Nutanix Enterprise AI helps organizations securely deploy, run, and scale inference endpoints for large language models to support the deployment of generative AI applications in minutes, not days or weeks. Generative AI is an inherently hybrid workload, with new applications often built in the public cloud, fine-tuning of models using private data occurring on-premises, and inferencing deployed closest to the business logic, which could be at the edge, on-premises or in the public cloud. This distributed hybrid GenAI workflow can present challenges for organizations concerned about complexity, data privacy, security, and cost. Nutanix Enterprise AI provides a consistent multicloud operating model and a simple way to securely deploy, scale, and run LLMs with NVIDIA NIM optimized inference microservices as well as open foundation models from Hugging Face. This enables customers to stand up enterprise GenAI infrastructure with the resiliency, day 2 operations, and security they require for business-critical applications, on-premises or on AWS Elastic Kubernetes Service, Azure Managed Kubernetes Service, and Google Kubernetes Engine. Additionally, Nutanix Enterprise AI delivers a transparent and predictable pricing model based on infrastructure resources, which is important for customers looking to maximize ROI from their GenAI investments. This is in contrast to hard-to-predict usage or token-based pricing.