Model Serving Architecture: From Single GPU to Global Fleet

Most teams begin with a single model server and grow into multi-region fleets.

The key is to design for observability early so scaling does not break reliability.

Not all requests require the largest model. Route simple queries to smaller models and reserve large models for complex tasks.

This tiered routing lowers cost while preserving user experience.

Cache frequent responses and embeddings. This reduces GPU load and stabilizes latency spikes.

Cache policies must respect user privacy and data freshness.

Serve users from the nearest region to minimize latency.

Design automatic failover so a region outage does not take the product offline.

Define SLOs for latency, error rates, and cost per request.

Automate rollbacks and canary deployments to reduce deployment risk.

Forecast traffic with realistic peaks, not just averages. Load tests should simulate bursty usage, long responses, and mixed model tiers. This is the only way to understand tail latency before customers experience it.

Use autoscaling policies tied to queue depth and GPU utilization. Pre-warm capacity for launches so you avoid cold starts during critical traffic spikes.

Run chaos drills that simulate regional failures. Validate that routing, caches, and fallbacks keep the product responsive during partial outages.

Set explicit cost budgets per day and per feature. Budget pressure often reveals which workloads need smarter routing or caching.

Test canary releases under load so you understand how new versions behave before full rollout.

Separate control plane and data plane scaling. This keeps routing logic responsive even when inference clusters are saturated.

Use staged rollouts to watch for regressions in latency before shifting full traffic.

Do I need multi-region day one? No. Start simple and add regions when latency or reliability demands it.

What is the fastest win? Tiered routing plus caching usually provides immediate benefits.

How do I control cost? Track per-request GPU time and enforce budgets by routing or limits.