Why We Chose the Edge for AI Inference

When designing Arcten's infrastructure, we faced a critical decision: where to run AI inference. The conventional wisdom suggested centralized GPU clusters in major cloud regions. We went a different direction.

The Edge Architecture

We deploy lightweight inference workers to edge locations worldwide, using a combination of Cloudflare Workers, Fastly Compute, and similar platforms. This puts compute physically closer to users.

Why Edge?

The primary benefit is latency. For a user in Singapore accessing a copilot, routing requests to a data center in us-east-1 adds hundreds of milliseconds of network latency before any computation even starts. Edge deployment cuts that dramatically.

Second, edge platforms handle scaling automatically. We don't manage Kubernetes clusters or worry about provisioning GPU capacity. The platform scales from zero to millions of requests seamlessly.

The Challenges

Edge inference isn't free. The biggest constraint is memory and CPU limits. We can't run massive 70B parameter models at the edge. Instead, we use a tiered approach:

• Simple queries run on optimized small models at the edge (<7B parameters)
• Complex reasoning tasks route to larger models in centralized locations
• We use sophisticated routing logic to decide which tier handles each request

Results

For 80% of queries, we're able to serve responses entirely from the edge with p95 latency under 300ms. The remaining 20% use our centralized tier, but even those benefit from edge-based preprocessing and postprocessing.