Equinix Unveils AI Inference Exchange with NVIDIA and Together AI Across 280 Global Data Centers

Equinix Launches Distributed AI Inference Program with NVIDIA and Together AI

Equinix announced Equinix Inference Exchange at its inaugural Horizon customer event, marking a significant expansion of its collaboration with NVIDIA Corporation and a new partnership with Together AI1

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. The distributed AI inference program targets global enterprises struggling with where to run inference as AI scales across models, providers and geographies. The strategic alliance combines NVIDIA Enterprise Reference Architectures with Together AI's inference platform, which supports more than 200 open-source models, delivered through Equinix's global data center infrastructure3

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"AI is transforming enterprise technology at extraordinary speed, and the infrastructure decisions enterprises make today will define their competitive position for years to come," said Adaire Fox-Martin, Chief Executive Officer and President at Equinix3

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. The solution will be available starting in the first quarter of 20274

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Why Where Inference Runs Has Become a Strategic Imperative

The architecture of enterprise AI is being reshaped by a question that's harder to answer than it appears: where should AI inference run1

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? While AI infrastructure conversations have centered on graphics processing units, training clusters and capital buildout, the operational challenge now is distribution. Data resides across multiple clouds and enterprise systems. Users, sensors and business processes are dispersed across regions. Models may be proprietary, open source, fine-tuned or delivered as services.

As enterprise AI moves from experimentation to production, inference increasingly needs to run closer to the users, data and applications it serves across clouds, models, providers and geographies3

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. This shift requires enterprises to determine not only how to deploy AI infrastructure, but where it should run and how it connects to the data, applications and workloads it depends on. Managing these distributed inference deployments introduces significant operational complexity at precisely the moment enterprises need greater control and visibility.

"Performance, cost and governance have become strategic considerations as AI workloads grow more distributed across providers, data sources and environments," said Nick Patience, Vice President & Practice Lead, AI Platforms at The Futurum Group2

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Equinix Fabric One Transforms Network into Adaptive Control Plane

Alongside Equinix Inference Exchange, Equinix unveiled Fabric One, an intent-driven, managed any-to-any connectivity service1

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. The service represents a shift from the project-oriented networking model still familiar to many large enterprises. Rather than requiring customers to design, provision and manage discrete network services, Fabric One lets them state the business or application outcome they need and have the platform compose the underlying connectivity.

"For decades, enterprise networks have been built one connection at a time for each partner and provider they depend on. That approach doesn't scale in a world of distributed AI that demands dynamic, flexible and real-time connectivity," said Chris Audie, Equinix's chief product officer1

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. The service will use open connectivity specifications developed with Amazon Web Services and Google Cloud, entering beta later this year with general availability planned for 2027, initially in North America.

This model is particularly relevant for agentic AI. Future enterprise applications won't make fixed calls to a single large language model in a single cloud. They may dynamically coordinate several models and services, retrieve information from multiple data domains, apply policy checks and invoke specialized agents in different locations. In that scenario, the network cannot remain an opaque, static utility—it must become programmable, policy-aware and responsive to application needs as a control plane1

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Three-Layer Architecture Simplifies Distributed AI Environments

Equinix Inference Exchange combines three complementary layers designed to simplify distributed AI inference4

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. Equinix provides the infrastructure foundation, including power, advanced cooling and day-two operations, connected through Equinix Fabric to the clouds, networks and AI providers that inference depends on. NVIDIA Corporation anchors the build with its Enterprise Reference Architectures and AI infrastructure purpose-built to maximize AI factory throughput and minimize token cost. Together AI runs the platform on top, supporting both multitenant deployments for shared efficiency and dedicated single-tenant environments for workloads requiring dedicated capacity.

"Equinix Inference Exchange turns the world's leading digital interconnection platform into a global fabric for AI inference," said Raj Mirpuri, vice president of global AI clouds and infrastructure ecosystem at NVIDIA2

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. The solution connects to inference providers across major metros worldwide, cutting time-to-first-token, and connects to an expansive ecosystem of clouds, networks and AI providers, reducing deployment complexity.

Equinix brings more than 280 data centers across 77 metros, 230 cloud on-ramps and over 10,500 businesses interconnected on its neutral exchange2

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. Eight of the top 10 AI model providers and nine of the top 10 AI clouds are deployed with Equinix, underscoring the company's position at the center of the AI ecosystem.

Open Model Flexibility Addresses Enterprise Trade-offs

Together AI brings open-model flexibility to enterprises deploying AI at scale. "Together AI was built on the conviction that open, accessible AI is what will define the industry moving forward, because enterprises shouldn't have to choose between model performance and operational flexibility," said Vipul Ved Prakash, co-founder and CEO at Together AI2

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. The platform's support for more than 200 open-source models means enterprises can avoid vendor lock-in while maintaining production-grade inference capacity.

The solution aims to support a broad range of enterprise AI inference scenarios, including metro edge inference, open model migration, and sovereign AI4

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. Built on Equinix Fabric, it will provide secure, low-latency connectivity to the data, users and ecosystem enterprises depend on, giving them a faster path from AI experimentation to production. Watch for how enterprises balance the operational simplicity of this managed approach against the control of building their own distributed AI environments as the first quarter 2027 launch approaches.