AI Model Scaling

Horizontal scaling adds model replicas behind a load balancer — effective for models that fit on a single GPU. Vertical scaling (multi-GPU tensor parallelism) handles models too large for one device. We configure both dimensions: replica count scales with request rate while individual replicas use the minimum GPU resources needed. No overprovisioning, no bottlenecks.

KEDA or custom HPA metrics scale on GPU utilization, request queue depth, or inference latency — not just CPU. Scale-from-zero saves costs during quiet periods with a configurable cold-start budget. Scale-up triggers are aggressive (don't drop requests), scale-down is conservative (don't thrash). We tune the scaling parameters against your traffic patterns, not generic defaults.

For latency-sensitive inference, we deploy model replicas across regions with GeoDNS or global load balancing. Smaller distilled models run at the edge for ultra-low latency. Model updates propagate to all regions via a central registry with automated canary rollout per region. Each region scales independently based on local traffic.

GPU fleet dashboards show utilization, cost allocation, and capacity across all clusters. Bin-packing algorithms place inference workloads to maximize GPU utilization. We implement GPU sharing for small models and exclusive allocation for large ones. Capacity planning models project when you'll need more hardware based on traffic growth trends.