Unverified50% confidenceFactExact time
Autoregressive models can only determine one token per forward pass, so inference cannot be parallelized along the time dimension, and latency grows linearly with output length.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Mercury 3: A Diffusion LLM That Scales 40x Without Slowing Down
producthuntProductHunt Daily10/22/2026
Related Entities
Related Claims
UnverifiedThe autoregressive generation mode means the model must generate tokens one by one and cannot output an entire response in parallel.73% similarUnverifiedThe KV Cache mechanism in inference services may introduce intra-batch state dependencies, causing multiple outputs from the same model within a short period to not be entirely independent.72% similarUnverifiedFor each token generated or processed, one forward pass must be executed, and its computational cost is roughly proportional to the number of model parameters.72% 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.71% similarUnverifiedIn generative large models, a single forward pass usually produces only one token, and to obtain a complete output the result must be fed back into the input and called repeatedly, i.e., autoregressive decoding.71% similar
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