From pilot to production – making AI real for enterprise IT
There is no shortage of interest in AI. From boardroom strategy sessions to early-stage experiments in IT and data teams, nearly every enterprise is exploring how to tap into the power of large language models. But interest alone does not drive value. To realise the real potential of AI, businesses need to move beyond pilots and into production.
That transition is where many initiatives stall. The challenges of operationalising AI are often underestimated. Security, cost, model governance, data locality and system integration all need to be addressed before a model can do useful work at scale. For enterprises serious about using AI to improve customer experience, streamline operations or uncover new insights, these concerns are now front and centre.
At Nutanix, we are focused on helping organisations overcome these obstacles with infrastructure and partnerships designed for real-world use, not just experimentation.
AI in the enterprise needs more than GPU power
It is easy to get caught up in the hardware conversation when discussing AI. Graphics processing units (GPUs) get the spotlight, and for good reason as they are essential to training and inference. But running AI at scale in a business setting requires more than compute.
Enterprise AI needs integration with existing environments. It needs consistent operations across cloud and on-prem settings. It needs to support diverse teams with different workflows and responsibilities. And it needs to balance innovation with control, so that security and compliance are not sacrificed for speed.
That is why we created Nutanix Enterprise AI, a platform designed to support AI workloads and simplify how they are deployed, managed and scaled in complex environments.
From experimentation to execution with the right partners
For many enterprises, AI begins with exploration. A team spins up an LLM in the public cloud, plays with a chatbot interface or tests a summarisation tool on internal data. These activities are valuable, but often disconnected from business systems and workflows.
Moving into production means operationalising those models, putting them into secure environments, making them accessible via APIs, monitoring performance, and ensuring outputs are accurate and aligned with enterprise policies.
To support this shift, Nutanix has partnered with NVIDIA and Hugging Face.
Our integration with NVIDIA NIM microservices allows teams to quickly deploy and serve models in a way that is scalable, cost-effective and consistent across environments.
Through Hugging Face, enterprises can access a vast library of pre-trained, validated models, reducing the time and risk involved in model selection and fine-tuning.
Together, these partnerships streamline the AI pipeline. Enterprises no longer need to stitch together disparate tools. They can focus on building applications and services that deliver business value, rather than managing infrastructure complexity.
Making AI manageable with GPT-in-a-Box
For those ready to go further, we offer GPT-in-a-Box – a pre-integrated solution that combines infrastructure, orchestration and AI tools into one deployable stack. It is designed for enterprises that want to move quickly, without cutting corners on control or compliance.
GPT-in-a-Box supports:
Deployment of Hugging Face models or your own custom LLMs
Inference at the edge, in the data centre, or across hybrid cloud environments
Integration with Kubernetes environments including EKS, AKS and GKE
A unified interface for managing endpoints, scaling models and applying policies
By simplifying the mechanics of AI deployment, GPT-in-a-Box helps teams focus on outcomes, not infrastructure.
AI is not a proof of concept, it is a capability
The organisations that will benefit most from AI are those that treat it as a core capability. That means embedding AI in the same operational, security and compliance frameworks that govern the rest of the business. It means choosing tools and platforms that support long-term evolution, not just short-term wins.