Multi-Tenant AI Serving Suffers Severe Latency from Cache Thrashing

A severe performance bottleneck called KV-cache thrashing is impacting shared vLLM clusters running multiple AI agents. The issue occurs when different, lengthy agent prompts are randomly distributed across GPU workers, causing them to repeatedly evict each other's cached data. This collapses cache hit rates from over 95% to near zero, dramatically increasing initial response latencies from milliseconds to several seconds. The problem is particularly acute for enterprise AI applications using large-context prompts, where slow initial responses break service level agreements. Standard web load balancing techniques worsen the issue, though coordinated routing solutions can mitigate it.