Model Serving

AI Inference Routing

Which serving mix absorbs the traffic growth without exceeding GPU, latency, budget, or carbon limits?

Result

Expand local GPU pool

First constraint to relieve

Model map

Policy-constrained routing

Active limits

Local GPU pools full

GPU hours fully used

Latency policy near limit

External API quota tight

Situation

Traffic is growing across local models, external APIs, and CPU fallback. Each route has a different capacity, latency, cost, and carbon profile.

Model logic

Allocates requests across serving routes while enforcing GPU, latency, quota, budget, and carbon limits.

Readiness conditions

CPU fallback is cheaper, but more traffic there breaks the latency policy.

External API burst helps until vendor quota becomes binding.

Local GPU capacity is the available path to higher traffic without a latency breach.

Takeaway

Expand the local GPU pool to grow served traffic without exceeding the latency limit.