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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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Asking Meta about the private clipsWhere do the images come from? Can private videos from Sweden end up on screens in Kenya? Those who appear in the images, have they consented to appearing in this way?

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