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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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3 │ let x = 1 + "hello"

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3 月 4 日,蚂蚁集团联合清华大学发布开源强化学习训练框架 AReaL v1.0 稳定版。该版本主打「Agent 一键接入 RL 训练」:不用改代码,兼容各类 Agent 框架,让智能体强化学习训练开箱即用。

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