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.
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The thing is, they called you to change that codebase! You need to meet new functional requirements. Or fix some bugs.