I am a technology journalist with three decades of press experience. Primarily I work as a news analysis writer dedicated to a software application development ‘beat’. I have spent much of the last twenty years focusing on open source, analytics & data science, cloud computing, mobile devices & data management.
2004 in New York City. The Friday after Thanksgiving, called"Black Friday," is one of the busiest shopping days of the year with stores opening early and a large number of shoppers looking for holiday gifts.
AI is dispersing. As we move through yet more hype cycles gravitatng around the use of Artificial Intelligence, we appear to be at a point where we’re being told about new predictive, generative - and now agentic - forms for AI being made available from every enterprise software platform vendor worth their salt. While some argue that adoption rates are slower than the so-called innovation cycles beng pushed by the vendors, most of us agree that for AI to stick, it needs to be available on every form factor of device and through every deployment channel. What that means is that while AI itself is becoming more multi-model , it is also becoming more multi-modal as it evolves.A perfect example of this multi-model multi-modal modality is generative AI with its use of retrieval augmented generation , which makes it an inherently hybrid workload. Where the foundation technologies and large language models for these services start in public cloud, they are then fine-tuned using private data occurring in on-premises cloud deployments so that they can perform inferencing closest to a company’s business logic. Because we now live in the Internet of Things era of smart machines, that business logic might be at the edge, on-premises or in the public cloud. But distributed hybrid generative AI workflows can be tough to pull off because organizations will naturally harbor concerns about complexity, data privacy, security and cost. Hybrid multicloud computing company Nutanix thinks it has a strategy to serve this new AI cloud topography. It has now extended its AI infrastructure platform with a new cloud-native offering. Nutanix Enterprise AI can be deployed on a Kubernetes platform, at the edge, in company datacenters and on public cloud services such as AWS EKS, Microsoft Azure AKS and Google GKE.According to Thomas Cornely in his role as SVP for product management at Nutanix, the NAI offering delivers a"consistent hybrid multi-cloud operating model" for accelerated AI workloads, enabling software engineers and AI-centric data scientists to run models and data in a secure location of their choice. Using Nvidia NIM for optimized performance of foundation models, Nutanix Enterprise AI helps run inference endpoints for large language models to support the deployment of generative AI applications in what the company says is less time than normal by current standards. It’s ‘Unsustainable’—Tesla CEO Elon Musk Issues Serious U.S. ‘Bankruptcy’ Warning As Bitcoin And Dogecoin Fuel $3 Trillion Price Boom“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 generative AI infrastructure with resiliency and also security they require for business-critical applications, on-premises or on AWS Elastic Kubernetes Service , Microsoft Azure Managed Kubernetes Service and Google Kubernetes Engine ,” notes Cornely and team. The company says that 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 AI investments. With the existence of token-based pricing being what Nutanix would label as less transparent, the option may appeal to some organizations.Nutanix Enterprise AI is a component of Nutanix GPT-in-a-Box 2.0. GPT-in-a-Box also includes Nutanix Cloud Infrastructure, Nutanix Kubernetes Platform and Nutanix Unified Storage along with services to support customer configuration and sizing needs for on-premises training and inferencing. Where a business wants to deploy in the public cloud, Nutanix Enterprise AI can be deployed in any Kubernetes environment but it remains “operationally consistent” with on-premises deployments, which is clearly the part that really matters. "Generative AI workloads are inherently hybrid, with training, customization and inference occurring across public clouds, on-premises systems and edge locations," said Justin Boitano, vice president of enterprise AI at Nvidia."Integrating Nvidia NIM into Nutanix Enterprise AI provides a consistent multi-cloud model with secure application programming interfaces, enabling customers to deploy AI across diverse environments with the high performance and security needed for business-critical applications."Typical use cases for organizations applying these technologies might include customer experience tools that make use of generative AI functions and accelerating software application development code and content creation through the use of co-pilots and intelligent document processing. These technologies could also be used to fine-tune models on domain-specific data to accelerate developer code and content generation. There’s also relevance here for firms looking to strengthen security, including using AI models for fraud detection, threat detection, alert enrichmen, and automatic policy creation. The wider evolution here is that Nutanix will have made buying and using AI infrastructure easier. If that sounds fairly uninspiring as a singular statement, what makes it more exciting is that system administrators might start to become not just admins, but AI admins, which is clearly a step up. When organizations can start to deploy and test large language model inference endpoints in minutes , then we might move more forcibly forward to real AI everywhere, dispersed, distributed, diffused and done better.Our community is about connecting people through open and thoughtful conversations. 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