Edge AI Inference Isn’t All at the Edge

AI has gone mobile. On-device intelligence is being integrated into built-in smartphone applications for voice assistance, image enhancement, predictive typing and translation. It’s in social media, music streaming, finance, healthcare and retail apps. AI-powered conversational assistants like ChatGPT, Google Gemini and Microsoft Copilot have exploded in popularity. And it doesn’t stop with what’s on your phone: AI is also in industrial IoT sensors, smart home devices, autonomous vehicles and remote healthcare devices.

All these AI-powered technologies require AI inference at the edge. So, it’s not surprising that edge AI is trending. The global edge AI market is expected to grow at a compound annual growth rate of 21.7% over the next five years.[1] Meanwhile, AI is being integrated into mobile networks, and mobile networks are being used for AI data transport. NVIDIA just invested US$1 billion in Nokia to support AI-native mobile networks for large-scale distributed AI and low-latency mobile edge inference.[2]

Most users won’t give a second thought to how mobile AI works, where the data lives, how it travels, or what kinds of hardware and software are required. It might even seem like AI magic is happening inside the mobile device. In truth, there’s invisible infrastructure outside the device that makes edge inference possible. Inference involves a complex process and many ecosystem participants, from data and model providers to clouds, AI infrastructure providers and networks. And they all need to be interconnected for fast, secure data exchange.

If you’re looking to deploy an AI application that can be consumed on smartphones and other devices at the edge, it’s important to understand the architectural implications of edge AI. To deliver the best user experiences and the best business outcomes, you need a resilient, agile, high-performance multicloud network interconnecting all the elements of your edge inference architecture.

The network defines edge AI performance

AI inference, the process whereby a trained AI model is applied to new data to generate decisions or outputs, is increasingly happening at the edge, close to end users and devices. This proximity is necessary to minimize latency and enable AI inference nodes to react to fresh data quickly to support real-time actions.

The edge inference process includes a complex set of steps, such as:

• Input: Capturing data input from the edge (text, audio, images or sensor data)
• Tokenization/Embedding: Breaking down data into small, meaningful units for the AI model (tokens) and converting those tokens into numerical vectors
• Retrieval (the first step in retrieval-augmented generation, or RAG): Querying a database to find relevant data (on the device, in an edge node or in an enterprise database located somewhere else)
• Augmentation: Combining the original prompt and vector data to give the model more context
• Generation: Delivering the model’s output to the end user or device in a human-readable/actionable form or using it as input for the next reasoning step

The process requires access to data coming to and from the user/device over the internet, a mobile network or a private network. It also requires access to data that may live in multiple clouds or a database in a corporate data center or colocation facility. To further complicate matters, AI applications are usually distributed to many locations around the world, wherever users already are. That inference infrastructure needs to be close enough to reach them with the lowest possible latency while still being connected to core, centralized data centers.

With data and users in so many places, the network that connects all these parts of an AI inference solution is of fundamental importance. The network determines the speed, security and reliability of the whole process. It transports data, distributes trained models to the edge, carries model context protocol (MCP) requests, collects monitoring data and handles traffic for multi-node distributed inferencing environments. This need for interconnection everywhere is even more pronounced with agentic AI since AI agents access backend data, frontend applications and external sources simultaneously.

Edge inference in action: Voice query in a corporate AI app

Imagine a remote worker who makes a voice query in a corporate AI application on their tablet or smartphone. The application listens and collects input data from the device microphone as well as other data such as the user’s location and device profile. It then transmits this information over a 5G network to a server at the metro edge that runs an embedding model converting the user input into tokens and vector data.

Using this data, the embedding model submits a query to a pre-established vector database (DB) to retrieve relevant corporate data. The vector DB may run on the same server at the metro edge or in a private, on-premises data center reachable over private interconnect (to ensure security and governance). The original prompt is augmented with the matching vector DB data. The augmented prompt is then sent over private interconnect to an LLM running either on private hosted infrastructure, a neocloud or a public cloud.

The LLM may be configured to use external tools (agents) via MCP servers to provide further context for the response or take actions on behalf of the user. The MCP servers could be used over public or private connectivity, depending on security and sovereignty requirements. Finally, the LLM generates a response to the employee’s original voice query and sends it back to the metro edge server, which then creates a voice response using a local text-to-speech model.

This inference scenario requires data transfer between users, clouds, private storage, inference nodes and more, delivered through network points of presence (PoPs). The network needs to be very fast to deliver real-time responses to user queries, and it must protect proprietary enterprise data in the process.

Dynamic data flows are the backbone of edge intelligence

Let’s take a closer look at the movement of data across various endpoints in this edge inference scenario. Each of these numbered steps corresponds to a data transfer illustrated in the edge architecture diagram below.

1. Voice input at edge device

The user or IoT device sends voice input to the system from an edge device.

2. Speech-to-text processing

At the metro edge, an AI app converts speech to text. Aggregating heavier processing at the metro edge reduces the technical footprint required on end devices, enabling simpler application lifecycles and lowering overall costs.

3. Access private data at metro edge

The AI-enabled app retrieves private data at the metro edge to enrich the prompt.

4. Send augmented prompt via Equinix Fabric

The app transmits the enriched prompt securely from the metro edge to a core data center over a private interconnection solution like Equinix Fabric®.

5. AI factory processing with GPU inference

At the data center, the prompt enters the AI factory for orchestration and GPU-based inference.

6. Access private data in core for RAG

The LLM retrieves private data stored in the core location to support RAG and enhance the AI response.

7. LLM orchestrates agentic AI across MCP servers

The LLM reaches out to multiple MCP servers (distributed across cloud and SaaS environments) to perform agentic AI tasks, leveraging specialized tools and services. MCP traffic flows can leverage the low-latency multicloud networking capabilities of Equinix Fabric, such as Equinix Fabric Cloud Router. For MCP servers in SaaS applications only available over the public internet, access is provided via an Equinix Network Edge security device that connects to Equinix Internet Access, ensuring resilient and robust performance.

8. LLM sends completed response to metro edge

The LLM in the core data center returns the fully processed response to the metro edge server for final delivery.

9. Metro edge application converts text to speech

The application running at the metro edge uses a text-to-speech module to convert the AI-generated text response into audio for playback.

10. Response delivered back to edge device

The audio output is sent back to the edge mobile device or IoT endpoint to deliver to the end user.

Data flow patterns for an edge AI inference scenario

You can see that one simple mobile AI query can involve data and infrastructure in many locations. Edge inference requires a dynamic hybrid multicloud network backbone to transport data quickly and reliably across the inference architecture. Private interconnection can play an important role here, offering deterministic performance and high security for sensitive inference data.

Connect edge infrastructure in the right places

With a robust global data center footprint, Equinix can host edge infrastructure for distributed AI deployments anywhere organizations need it. As a vendor-neutral colocation provider, we’re home to many of the technologies and services that are part of the AI ecosystem. And we offer robust private interconnectivity solutions to support hybrid multicloud networking for edge AI.

Learn more about planning the right infrastructure for AI at the edge by downloading our five-step roadmap to implementing edge computing.

[1] Edge AI Market (2025 – 2030), Grand View Research.

[2] NVIDIA and Nokia to Pioneer the AI Platform for 6G — Powering America’s Return to Telecommunications Leadership, NVIDIA, October 28, 2025.