Edge Inference at 10ms Latency: Redefining Real-Time AI

Why Edge Deployment Matters for Real-Time AI

Latency is the silent killer of AI applications. A 100ms delay in fraud detection means fraudsters get away. A 200ms delay in autonomous vehicle perception is a safety hazard. For real-time AI, latency isn't a feature—it's a requirement.

AIGB's edge-near deployment model delivers inference at 10ms latency across Europe. Here's why this matters and how we're achieving it.

The Latency Problem with Centralized Cloud

Traditional cloud AI relies on centralized data centres:

• US-based inference: 150-200ms latency from Europe
• European hyperscaler: 50-100ms latency
• AIGB edge-near: 10-20ms latency

For latency-sensitive applications, this difference is critical:

Fraud Detection: 100ms delay = fraud approved before detection
Autonomous Vehicles: 200ms delay = safety hazard
Real-time Trading: 50ms delay = missed opportunity
Medical Imaging: 500ms delay = poor user experience

The Edge-Near Architecture

AIGB deploys inference clusters at 16+ European locations:

• Frankfurt: 5 MW (central Europe hub)
• Paris: 450 kW (France)
• Amsterdam: 2 MW (Benelux)
• Stockholm: 3 MW (Nordic hub)
• Vienna: 8 MW (Eastern Europe hub)
• Plus 11 additional locations

This distributed architecture ensures:

• Maximum 10ms latency from any European location
• Local data residency — data never leaves the region
• Redundancy — if one location fails, traffic routes to nearest alternative
• Compliance — meets EU AI Act jurisdictional requirements

Real-World Latency Performance

We tested inference latency across our European network:

Average latency across Europe: 11ms (p50), 16ms (p99)

Use Cases Enabled by Edge Inference

1. Real-Time Fraud Detection

• Approve/reject transactions in <20ms
• Prevent fraud before settlement
• 99.9% accuracy at 10ms latency

2. Autonomous Vehicle Perception

• Process sensor data at 10ms latency
• Enable real-time decision making
• Support autonomous driving at highway speeds

3. Real-Time Recommendation

• Personalized recommendations in <50ms
• Improve conversion rates by 15-20%
• Reduce latency-induced abandonment

4. Medical Imaging Analysis

• Real-time CT/MRI analysis during procedures
• Support live surgical guidance
• Enable intraoperative decision making

The Economics

Edge inference is more expensive than centralized cloud, but the business value justifies it:

The Future of AI Infrastructure

Centralized cloud is optimized for batch processing and non-latency-sensitive workloads. Edge-near infrastructure is optimized for real-time, latency-sensitive AI.

The future of AI infrastructure is distributed. AIGB is building that future.

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Jóhann Jónsson
Head of Infrastructure, AI Green Bytes
December 2025