Why does AI inference require different infrastructure than training?

Unlike model training, which is a localized, high-intensity batch process, inference is a continuous, real-time workload. It requires infrastructure that prioritizes resiliency and power-per-watt to avoid unmanageable energy costs during 24/7 production

How does Nutanix help with AI sustainability?

Nutanix provides a unified platform that can reduce the physical footprint in a data center, which in turn can lead to savings in power and cooling.

Can I run AI inference on CPUs?

Yes, depending on the use case. For many Small Language Models (SLMs), modern CPUs with built-in accelerators can offer high energy efficiency and lower TCO compared to dedicated GPUs . Rule of thumb: Use CPUs when appropriate and GPUs when you must.

How does Software-Defined Networking (SDN) factor into energy efficiency?

Traditional networking often relies on a sprawl of dedicated hardware appliances that stay powered on 24/7. By virtualizing these functions—using solutions like Nutanix Flow Virtual Networking—there is opportunity to eliminate redundant hardware switches and routers. This could lower data center energy consumption by reducing the "vampire power" draw from idle, standalone gear.

The Perfect Assist for Your AI Factory: Why Your Star GPUs Need Nutanix Unified Storage

By Alexander Almeida, Sr. Product Marketing Manager, Nutanix

June 1, 2026 8:26 pm

Let’s be honest: you wouldn’t spend millions signing the world’s best star striker only to leave them stranded on the pitch without a midfield to pass them the ball. Yet, as IT leaders race to transition from early AI experiments into massive, 24x7 production environments, this is exactly what happens when deploying top-tier GPUs with legacy storage systems.

We have entered the era of the “AI factory,” where data is the ball, and your storage system is your midfield playmaker. If the passes are slow or late, your heavily invested GPUs are left standing offside, and performance completely stalls. Fragmented infrastructure and siloed data can dramatically slow deployments and limit GPU efficiency, making your AI harder to scale reliably.

That’s why we are thrilled to announce that The Nutanix Unified Storage (NUS) solution is officially NVIDIA-Certified at the Enterprise level.

As highlighted in our press release, this certification provides IT decision-makers with a proven, validated path to deploy AI infrastructure confidently, maximizing GPU utilization and reducing integration risks.

What does this mean for your game plan for deploying data-hungry AI and Agentic AI applications?

The Perfect Through-Ball

No More starving your strikers to achieve a quicker route to revenue, your AI infrastructure needs to work at maximum efficiency. NUS leverages a highly parallel filesystem (pNFS) that, when coupled with NFS over RDMA, this enables NVIDIA GPUDirect Storage, which serves as a massive technical differentiator.

In a traditional setup, data has to travel through the compute server's CPU and memory before finally reaching the GPU. It’s like trying to pass the ball through three clumsy defenders. GPUDirect Storage completely skips these costly "middlemen." Instead, it creates a direct, low-latency, high-bandwidth through-ball straight from the storage nodes into the GPU memory. Your GPUs stay constantly fed, and your AI pipelines stay incredibly fast.

From Sunday League to the World Cup Final

How do you transition from a small training pitch to a massive AI production fleet without having to rip and replace your infrastructure? Nutanix makes scaling your AI factory predictable and seamless.

You can kick off your deployment with just a single SU (32 GPUs) backed by 5 Nutanix system nodes. You then simply add blocks to your “squad on the pitch” as your AI needs grow.

As mentioned in our press release, the NVIDIAs Design Guide for this solution quotes linear scalability from 10 GB/s read and 5 GB/s write for 32 GPUs to 160 GB/s read and 80 GB/s write for 1,024 GPUs. With this level of NVIDIA performance, prepare to be amazed by the “on field performance” that a Nutanix system including those GPUs can deliver!

Home Field Advantage

Bring the AI to your Data. Traditional, isolated storage arrays force you to inefficiently move massive chunks of data across your network. Nutanix’s compute-adjacent architecture eliminates this headache by allowing you to run GPU-accelerated compute nodes right alongside high-density storage within the same unified cluster. This ensures that your AI data pipelines and vector processing for Retrieval-Augmented Generation and Agentic Workflows operate directly where your enterprise data already lives, giving you a massive home-field advantage by minimizing latency and reducing vendor sprawl.

A Manager's Dream

Managing massive AI deployments shouldn't require a coaching staff of rocket scientists. Unlike incredibly complex HPC file systems that require specialized storage engineers to constantly tune and maintain them, Nutanix brings consumer-grade simplicity to your AI factory. With features like one-click, non-disruptive upgrades and automated load balancing, your existing virtualization teams can manage this high-performance infrastructure effortlessly.

The Winning Playbook for Immediate Success

You don't have to guess what tactics to use. Nutanix integrates directly into NVIDIA Enterprise Reference Architectures, providing a reliable, enterprise-grade blueprint that helps your deployment time and operational risk. It is designed to promote proven interoperability across Nutanix storage, NVIDIA-Certified servers, and NVIDIA Spectrum-X Ethernet networking.

Ready for kickoff?

By reducing infrastructure bottlenecks, Nutanix and NVIDIA are paving a fast and easy route to capturing opportunity and driving revenue with your new Agentic AI solutions.

For a deeper dive and looking to jump into the video room to view game footage and review the specific tactical configurations and scaling guidance, stay tuned to our website and social channels for the release of the Reference Architecture document coming soon.

©2026 Nutanix, Inc. All rights reserved. Nutanix, the Nutanix logo and all Nutanix product and service names mentioned are registered trademarks or trademarks of Nutanix, Inc. in the United States and other countries. All other brand names mentioned are for identification purposes only and may be the trademarks of their respective holder(s).This content reflects an experiment in a test environment. Results, benefits, savings, or other outcomes described depend on a variety of factors including use case, individual requirements, and operating environments, and this publication should not be construed as a promise or obligation to deliver specific outcomes.