Unverified50% confidenceFactExact time
大模型推理的自回归生成需要逐Token串行输出，生成N个Token需要N次前向传播
1
Sources
50%
Confidence
Long-term
Relevance
8/22/2026
First Seen
Sources
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Related Claims
UnverifiedLLMs generate text in an autoregressive manner, producing only one token at a time, requiring N forward inference passes to generate N tokens, with latency scaling linearly with output length.78% similarVerifiedThe token generation process of large language models is serial, based on an autoregressive mechanism, where each token's generation depends on the previous token.75% similarUnverifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.75% similarVerifiedThe autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.74% similarUnverifiedThe inference process of large language models is autoregressive, and the number of output Tokens is almost linearly correlated with response latency.71% similar
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