Inside Equinix, Cisco, NVIDIA and Presidio's AI Alliance

Choosing an AI model is a major strategic decision for many enterprises, but there is a related challenge of choosing where and how to run it.

The latter dynamic is at the heart of an expanded collaboration between Equinix, Cisco and NVIDIA, as Equinix looks to simplify enterprise AI deployments across its global footprint.

Equinix plans to support deployment of the Cisco Secure AI Factory with NVIDIA across its network of data centres, giving customers access to standardised AI infrastructure blueprints and automation tools designed to reduce the complexity of rolling out AI environments.

At the same time, Equinix is partnering with technology services provider Presidio to launch a new testing environment inside its facilities, allowing enterprises to validate AI infrastructure before committing to larger deployments.

Building the foundations for AI

AI workloads often require specialised power, advanced cooling systems and high-performance interconnection capabilities to support demanding hardware.

Gordon Mackintosh, Senior Vice President, Global Partner Sales and Ecosystems at Equinix, says: "The success of enterprise AI starts with its physical foundation.

"Our collaboration with Cisco, NVIDIA and Presidio delivers the infrastructure AI workloads demand while giving customers a place to prove it out before they scale.

"This is how AI shifts from pilot to production with the speed, simplicity and certainty businesses need."

Equinix says the Cisco Secure AI Factory with NVIDIA will be available across its global data centre platform, helping customers access infrastructure designed around NVIDIA reference architectures.

These designs are intended to provide a repeatable approach for enterprises deploying AI technologies through existing technology partners and infrastructure providers.

A production-grade AI testing environment

A key part of the announcement is the launch of Presidio's Programmable AI Technology Hub, or P.A.T.H. Lab, within Equinix facilities.

Built on Cisco's Secure AI Factory with NVIDIA, the lab is designed as a production-grade environment where organisations can test and refine AI infrastructure strategies before expanding them across the wider business.

The facility aims to give enterprises hands-on access to infrastructure that supports hybrid deployment models spanning public cloud, on-premises environments, colocation facilities and emerging neocloud platforms.

Tim McHugh, VP Partnerships & Alliances at Presidio, says: "One of the most important shifts we've seen in the last 18 months is that AI success is no longer about finding the most powerful model.

"It's about building the infrastructure that can run AI everywhere it matters, without sacrificing data sovereignty or control."

"Equinix Distributed AI is the foundation that makes that possible at global scale, and P.A.T.H. is how Presidio brings that capability directly to our clients.

"We're not asking them to take our word for it – we're putting them inside a production-grade environment and showing them what distributed AI infrastructure actually looks like in practice."

Partner ecosystem takes centre stage

The announcement also highlights how enterprise AI projects rely on multiple technology providers working together.

Cisco sees that collaborative approach as becoming more important as organisations move AI deployments into production.

Cassie Roach, Global Vice President of Cloud and AI Infrastructure Partner Sales at Cisco, says: "As agentic AI reshapes the industry, long-term success belongs to partner ecosystems that can adapt and innovate as rapidly as the technology itself.

"Our collaboration with Equinix, Presidio and NVIDIA to deliver the Cisco Secure AI Factory with NVIDIA illustrates how a trusted agile partner ecosystem can deliver secure, flexible AI infrastructure quickly to meet customers' needs."

By combining data centre capacity, AI infrastructure blueprints and a real-world testing environment, the partners are aiming to remove some of the uncertainty enterprises face as they scale AI workloads from experimentation into production environments.