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Reduce LLM Latency Before Changing Models
Cut sequential calls, shorten outputs, stream useful progress, cache stable prefixes, and parallelize independent work.
Cut sequential calls, shorten outputs, stream useful progress, cache stable prefixes, and parallelize independent work.
简要回答: Start with architecture. The largest wins often come from fewer serial model calls and shorter generated outputs. Then optimize prompt size, cache reusable prefixes, parallelize independent retrieval, and choose a faster model only after measuring the quality trade-off.
实施步骤
- Break total latency into queue, input, first-token, generation, and tool time.
- Remove unnecessary model turns and combine deterministic processing.
- Set concise output contracts and realistic token limits.
- Stream user-visible progress without pretending unfinished work is complete.
验证清单
- Track p50 and p95 by task class.
- Measure task success after every optimization.
- Load-test with real concurrency and output lengths.
常见错误
- Optimizing average latency only.
- Streaming verbose text that users do not need.
- Shrinking context without checking retrieval failures.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
