What is edge inference?

Edge inference runs AI models on distributed nodes physically close to where data is generated — users, devices, sensors — instead of sending every request to a centralized cloud region. Aurora runs optimized models on edge nodes for low-latency local processing, then feeds results back to the Aurora core for training and aggregation.

When should you run AI inference at the edge instead of a central cloud?

Run inference at the edge when workloads are real-time, latency-critical, or bandwidth-heavy and cannot afford the round trip to a centralized region. Edge inference cuts latency, reduces backhaul, and stays responsive even when connectivity is intermittent.

What workloads is Aurora edge inference built for?

Vision and video, robotics and IoT, telco and 5G, retail, and industrial workloads — anywhere decisions must be made in milliseconds next to the data. Aurora also supports hub-and-spoke deployments where edge nodes infer locally and the Aurora core trains and aggregates.

How does Aurora deploy edge infrastructure?

Aurora deploys edge inference through Aurora micro-DC pods, at partner sites, or on customer premises, starting from 5 kW edge pods and scaling across many locations. Aurora designs, deploys, and operates the nodes end to end.

How does Aurora manage edge deployments across many sites?

Aurora operates the entire edge fleet from a single control plane — continuous monitoring, incident response, driver, firmware and software updates, and capacity changes — backed by a 99.9% uptime SLA. Aurora operates what it deploys.

Does edge inference keep working without a connection to the cloud?

Yes. Edge nodes process locally, so latency-critical workloads stay responsive even when the link to the core is intermittent or temporarily lost. When connectivity returns, nodes sync back to the Aurora core for aggregation and model updates.

How does Aurora edge work with Aurora's GPU and cloud infrastructure?

Aurora edge uses a hub-and-spoke model: distributed edge nodes run inference close to the data, while the Aurora core — GPU clusters and AI Cloud — handles training, aggregation, and model updates that are distributed back to the fleet. Edge and core are one managed stack.

Where can Aurora deploy edge infrastructure?

Aurora builds and operates infrastructure across North America, Western Europe, the Nordics, GCC, and APAC, deploying edge capacity in-region and close to where your data is generated.

Edge Inference — Low-Latency AI at the Edge

Some workloads can’t wait for the round trip.

Real-time, latency-critical, and bandwidth-heavy workloads can’t afford the trip to a centralized region. Edge inference puts the model next to the data — cutting latency, reducing backhaul, and staying responsive even when connectivity is intermittent.

Ultra-Low
Latency

Local
Processing

Resilient
by Design

Fleet
Management

Data Centers, GPUs, and Operations —
Built, Operated, and Scaled.

Some workloads can’t wait for the round trip.

Some Example Workloads:

Vision & Video

Robotics & IoT

Telco & 5G

Retail

Industrial

Hub-and-Spoke

Deployment Modalities

Aurora Micro-DC Pods

Partner Sites

Customer Premises

AI Data center infrastructure.
Deployed close to where you operate.

Bring inference to the edge.

Data Centers, GPUs, and Operations — Built, Operated, and Scaled.

Some workloads can’t wait for the round trip.

Some Example Workloads:

Vision & Video

Robotics & IoT

Telco & 5G

Retail

Industrial

Hub-and-Spoke

Deployment Modalities

Aurora Micro-DC Pods

Partner Sites

Customer Premises

AI Data center infrastructure.Deployed close to where you operate.

Bring inference to the edge.

Data Centers, GPUs, and Operations —
Built, Operated, and Scaled.

AI Data center infrastructure.
Deployed close to where you operate.