Load Balancing and Scaling LLM Serving
Load balancing for large language models requires specialized cache-aware routing strategies because traditional round-robin approaches can degrade prompt cache hit rates from 50-90% efficieny down to just 1/N across N replicas, eliminating the cost and latency benefits. A new technique called precise prefix cache-aware routing--which uses radix trees and real-time KV cache events from inference engines--can improve throughput by up to 108% compared to standard Kubernetes load balancing by ensuring requests hit servers that already have relevant conversation history cached. Read More
