How do you handle scaling challenges in high-throughput LLM APIs?

Scaling LLM APIs requires load balancing, batching, caching, model routing, and horizontal GPU scaling.

High-throughput LLM systems must manage GPU saturation, request queuing, and token generation bottlenecks. Techniques include dynamic batching, autoscaling inference servers, request prioritization, and caching frequent responses. Load balancers distribute traffic based on model capacity.