Unverified50% confidenceFactExact time
LLM inference is autoregressive: every token generated requires a full forward pass.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA typical LLM uses an autoregressive generation approach, predicting token by token with each step's output feeding into the next step as input, forming a loop75% similarUnverifiedLLM inference is autoregressive token-by-token generation with bottlenecks in memory bandwidth and KV Cache management, which is fundamentally different from diffusion model inference.74% similarUnverified现代主流LLM采用自回归生成方式，逐个token按顺序生成74% similarUnverifiedThe autoregressive generation characteristic of LLMs determines that their output is streamed token by token, stemming from the decoding strategy of the Transformer architecture.72% similarUnverifiedLLMs 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.72% similar
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