Unverified50% confidenceFactExact time
In autoregressive generation, each token requires a full forward computation, and inference latency is proportional to generation length, making it the main throughput bottleneck.
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10/21/2026
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VerifiedOutput tokens are more expensive because the generation process requires autoregressive inference token by token, with each token generation requiring a full forward computation.74% similarUnverifiedContext length determines the maximum number of tokens a model can process in a single pass and directly affects VRAM usage.73% similarUnverifiedIn the inference pipeline, the Prefill stage is compute-intensive and time-consuming, while the Decode stage performs token-by-token autoregressive generation and is sensitive to latency.73% similarUnverified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Token72% similarUnverifiedThe computational load of constraint validation is proportional to vocabulary size and must be repeated for every generated token, making performance optimization critical.71% similar
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