Unverified50% confidenceCautionExact time
The speedup ratios and acceptance lengths in the paper all come from specific benchmarks and hardware configurations, and the gains in actual deployment will vary with task type, batch size, and target model scale.
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10/15/2026
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UnverifiedFor performance range figures with extremely wide spans, reaching the upper end of the range often depends on favorable conditions such as specific tasks, specific baseline comparisons, and specific batch sizes.75% similarUnverifiedThe speedup magnitude is usually strongly correlated with the baseline configuration — whether the comparison is against an unoptimized default configuration or one that has already undergone routine optimization, the conclusion may differ greatly; in addition, factors such as model size, sequence length, cluster size and network bandwidth all affect the actual gains.73% similarUnverifiedIn scenarios with extremely high response speed requirements or very high call volumes, choosing a smaller model is an inevitable choice under engineering constraints.72% similarUnverifiedIn the model loader, a higher inference strength value yields better generation quality but lower speed, representing a trade-off between quality and speed.71% similarUnverified显存节省通常以牺牲一定生成速度为代价，实际效果因硬件和任务类型而存在差异70% similar
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