大模型尾延迟的简单优化方案
A simple fix for LLM tail latency

原始链接: https://engineering.myhoai.com/posts/a-simple-fix-for-llm-tail-latency/

对于语音智能体等实时应用而言,尾部延迟(即偶尔出现的超过10秒的请求响应)是导致用户挂断电话的关键故障点。与其支付双倍费用购买大模型厂商的“优先级通道”来缓解这一问题,通常更好的做法是发送两次相同的请求,并取较快的那一次响应。 在针对 OpenAI 优先级通道的基准测试中,标准层级下的“双发”策略显著改善了延迟。测试显示,最差情况下的“首字延迟”从 4.2 秒降至 1.2 秒,最差情况下的“完成时间”从 9.8 秒缩短至 3.5 秒。此方法之所以有效,是因为只要假设缓慢响应是偶然且独立的事件,那么两次冗余请求同时发生延迟的概率极低。 通过利用这一策略,开发者可以在不增加单位 Token 成本的情况下,大幅减少尴尬的沉默时间并提升用户体验。在考虑昂贵的优先级服务之前,建议先测试这种冗余请求方法,看看能否以极低的成本实现更低的延迟。

抱歉。
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原文

When LLM responses are too slow for your realtime use case, you may be tempted to pay double the cost for a faster service tier. Anthropic’s Priority tier, OpenAI’s priority processing, Gemini’s priority inference, whatever your LLM provider calls it. There’s a simpler solution: send every request twice and take the faster response.

Why tail latency matters for voice agents

Our voice agent at HOAi answers phone calls. Every turn in a conversation makes an LLM request. Most responses come back within 1.5 seconds, but occasionally one takes 10 to 20 seconds. On a phone call, that’s 10 seconds of awkward silence, and after enough silence, the caller hangs up on our agent.

This happens more often than you’d think. A typical phone call has 20 to 30 turns. If 1% of LLM requests are catastrophically slow, a 25-turn call has roughly a 22% chance of hitting a long silence.

Priority tier vs. sending each request twice

We had two options.

  1. Upgrade to OpenAI’s priority tier and pay 2x cost per token for faster, more consistent responses.
  2. Stay on standard tier, but send every request twice and take the faster response.

We replayed 50 real production requests against both setups and tracked two metrics: time to first token (when the agent starts speaking) and time to complete response (when it can act on tool calls).

Time to first token:

Priority tierStandard tier, sent twice
median0.61s0.58s
p951.04s0.68s
p994.2s1.2s

Time to complete response:

Priority tierStandard tier, sent twice
median1.35s1.35s
p953.4s2.0s
p999.8s3.5s
worst9.8s3.5s

Sending the request twice clearly outperformed the priority tier. Worst-case time to complete response dropped from 9.8s to 3.5s. Worst-case time to first token dropped from 4.2s to 1.2s. Even the median matched the priority tier exactly, despite the standard tier being slower per individual request.

This works when slow responses are rare and independent. Sending the request twice makes it unlikely both copies are slow on the same turn. This significantly reduced the 10-second silences our callers were experiencing.

The takeaway

If you are building a realtime interactive LLM product, before you pay for the faster service tier, benchmark it against sending the request twice. You may get better latency at the same cost.

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