functions, making inlining decisions and computing call-graph
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End-to-end latency averaged around 1.6 seconds, measured from my server. According to Twilio, their media edge adds roughly ~100ms on top of that, bringing total perceived latency to about 1.7s.
In voice systems, receiving the first LLM token is the moment the entire pipeline can begin moving. The TTFT accounts for more than half of the total latency, so choosing a latency-optimised inference setup like Groq made the biggest difference. Model size also seems to matter: larger models may be required for some complex use cases, but they also impose a latency cost that's very noticeable in conversational settings. The right model depends on the job, but TTFT is the metric that actually matters.