Thanks — you're asking how to keep EOU latency under 600 ms when it sometimes spikes above 1s. Below are focused, practical steps that typically fix those spikes. Key actions to reduce EOU latency • Use non-burstable compute (e.g., m5/c5/c6i). t3/t4 instances can get CPU-throttled and cause >1s spikes. • Prewarm the VAD model on process start so inference doesn't pay a load penalty per call. • Enable preemptive generation (

preemptive_generation=True

) so TTS starts before the LLM finishes. • Deploy agent servers near your STT/LLM/TTS providers to cut network RTT. • Monitor EOUMetrics (

end_of_utterance_delay

,

transcription_delay

) to find whether the bottleneck is VAD, STT, LLM, TTS, or network. • Consider LiveKit Inference (provisioned capacity / co-location with models) to reduce queuing on public endpoints. • Tune endpointing: adjust

min_endpointing_delay

and

max_endpointing_delay

to balance responsiveness vs false endpoints. One diagnostic question • Are you seeing spikes correlated with CPU usage, STT/LLM/TTS remote call times, or specific times of day? If you can share an example metric (EOU delay + CPU + network/third-party latency) I can point to the most likely root cause and the highest-impact fix. If you want, I can give specific commands/config snippets for prewarming VAD or for configuring

min_endpointing_delay

and

preemptive_generation

. Which would help most right now? Sources: Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | STT Metrics | LiveKit docs | examples/frontdesk | livekit/livekit_composite | DeepWiki | Agents Framework | livekit/livekit_composite | DeepWiki | Introducing LiveKit Inference: A unified model interface for voice AI