Unverified50% confidenceFactExact time
自回归生成具有串行依赖性，第N个Token必须等待第N-1个Token生成完毕，导致大模型输出速度通常只有每秒数十Token
1
Sources
50%
Confidence
Long-term
Relevance
7/9/2026
First Seen
Sources
DeepSeek DSpec Hands-On: How Speculative Decoding Accelerates LLM Inference by 6x
bilibili程序员-智能译站7/8/2026
Related Claims
Unverified如果流式响应计费实现有误，将每个 chunk 携带的累计 Token 数当作增量叠加，会产生等差数列求和 n(n+1)/2 的放大效应68% similarUnverifiedLSTM still performs poorly when processing sequences exceeding 500 tokens, and its sequential nature means that a sequence of length N requires N time steps to complete the forward pass.67% similarVerifiedOutput tokens are more expensive because the generation process requires autoregressive inference token by token, with each token generation requiring a full forward computation.66% similarUnverified大模型每个数字都是逐 token 生成的，而非逐位计算，导致其算术能力在小数、除法、多步混合运算上不稳定65% similarUnverifiedSetting a reasonable max_tokens limit and repetition_penalty (typically 1.1–1.2) can alleviate the infinite loop problem but cannot completely resolve it.65% similar
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