基于决策树的LLM推理优化技术选型指南发布
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2026年11月，相关技术指南提出了一套通过六步决策树匹配大语言模型（LLM）推理优化技术的方法，旨在帮助工程师根据硬件、模型大小和流量特征精准定位瓶颈。指南详细对比了模型压缩（量化与蒸馏）、Prefix Caching、连续批处理、PagedAttention、推测解码以及 Prefill 与 Decode 解耦等技术的适用假设与权衡，并针对缓存命中率归零、推测解码反常变慢等常见落地方案失效问题，提供了具体的排查与解决建议。
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- Hacker News · AISelectedHow to choose LLM inference optimization techniques using a decision tree
This guide presents a six-step decision tree for selecting LLM inference optimization techniques, helping engineers pinpoint bottlenecks based on hardware, model size, and traffic patterns. It provides an in-depth comparison of the assumptions and trade-offs behind model compression (quantization and distillation), prefix caching, continuous batching, PagedAttention, speculative decoding, and prefill-decode disaggregation, along with troubleshooting steps for common failure modes such as zero cache hit rates and unexpected speculative decoding slowdowns.
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