Unverified50% confidenceSolutionExact time
The decode phase of large language model inference generates only one token at a time and is a memory-bandwidth-bound operation, because generating each token requires loading the entire model weights from memory once.
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Long-term
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9/29/2026
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Related Claims
UnverifiedLLM inference during the decode phase is typically memory-bandwidth-bound rather than compute-bound, because token-by-token generation requires loading the entire model weights but has extremely low arithmetic intensity.79% similarUnverifiedStructured Outputs通过约束解码（Constrained Decoding）技术实现，在模型生成token时动态限制可选token的范围77% similarVerifiedThe inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.77% similarUnverified在自回归生成阶段每生成一个token都需要从内存读取整个模型全部权重，使得生成阶段严重受限于内存带宽而非计算能力（memory-bound）75% similarUnverifiedOutlines adopts a Constrained Decoding approach, constraining the model's sampling behavior at each step of generation.74% similar
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