Load Balancing and Scaling LLM Serving

Prompt caching cuts LLM input costs by 50-90% and TTFT latency by up to 80%, but naive round-robin load balancing across N replicas reduces cache hit probability to 1/N. Cache-aware routing preserves efficiency at scale. Inference engines like vLLM, SGLang, and TensorRT manage GPU resources and concurrency for diverse workloads.