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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A voice from the control room broke in to make sure we’d snapped on our five-point harnesses. Then the demonstration began: “Three, two, one, now.” The flight deck lurched, and we began to plunge up and down. It was simulating a moderately turbulent wind recorded over Idaho and Montana. But the control room had segregated the wind’s vertical and lateral forces, so I could feel them in isolation. We were being bounced around by the vertical ones now, and it was kind of fun—like riding a hobbyhorse. The lateral motions came next. They were only half as strong, Pettit told me later, but they felt twice as discomfiting—slow, seasick waves like ocean swells. But the worst, by far, were the motions that were both vertical and lateral. When the control room programmed the flight deck to re-create full turbulence, the steady waves suddenly turned chaotic, off-kilter, completely unpredictable. A jolt or two, an odd pause; a little jerk to the side, and then the bottom fell out.，这一点在51吃瓜中也有详细论述