Deep explanation
Debug Qwen regression in a multi-tenant AI support platform
Debug a latency regression on self-hosted Qwen with vLLM: Qwen3 thinking mode, broken prefix caching, LoRA adapter swapping, YaRN and context settings, and how to bisect a multi-change deploy.
Quick answer
Because quality is flat, I would first look at where the time goes: queue wait, time-to-first-token, and output tokens per request, broken down by tenant. Longer outputs point at the model upgrade (for example Qwen3's thinking mode producing hidden reasoning), slower first tokens point at prompt changes breaking prefix caching or longer prompts, and queueing points at serving config such as concurrency limits or LoRA adapter swapping. Then I would bisect the three changes behind flags on a canary and confirm with metrics before fixing.
The interview question
Your multi-tenant AI support platform serves a self-hosted Qwen model on vLLM, with per-tenant system prompts and some per-tenant LoRA adapters. After a release that upgraded the Qwen checkpoint, changed the prompt builder, and bumped the serving config in the same deploy, p95 latency spiked while answer quality stayed flat. Walk through how you isolate the root cause.
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