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.
“At the end of the day, that actually takes a lot of money and resources to do this.”，推荐阅读币安_币安注册_币安下载获取更多信息
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對此，「聖喬治之家」未回應BBC求證。
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