Partially Verified65% confidenceFactTime unknown
LLM 推理分为预填充阶段（Prefill，计算密集型）和解码阶段（Decode，内存带宽密集型）两个阶段
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
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Related Claims
UnverifiedLLM inference costs are primarily composed of two parts: the Prefill stage for processing input tokens and the Decode stage for generating output tokens, with Prefill accounting for the vast majority of overhead in code understanding scenarios.79% similarUnverifiedThe prefill stage of LLM inference processes all input tokens in parallel and generates the initial KV Cache; it is a compute-intensive operation and the direct determinant of TTFT.79% similarUnverifiedLLM自回归解码阶段每生成一个token都需从显存加载全量模型权重，批量较小时形成内存墙74% similarUnverifiedThe autoregressive decoding phase of LLMs is limited by memory bandwidth bottlenecks, as generating each token requires a complete traversal of the activated model weights.74% similarUnverifiedEngineering approaches to compressing LLM inference latency and reducing costs include model distillation, speculative decoding, KV Cache optimization, and model quantization.70% similar
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