Трамп рассказал о «колоссальной угрозе» США со стороны Ирана

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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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SAT solvers usually expect boolean formulas in this form, because they are specialized to solve problems in this form efficiently. I decided to use this form to validate results of the LLM output with a SAT solver.

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