Unverified50% confidenceFactExact time
vLLM's continuous batching causes the same request, run at different times, to join batches with different compositions, as the lengths, counts, and generation stages of other sequences in the batch are all changing, thereby affecting the computation path.
1
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50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedvLLM's continuous batching mechanism causes the same request to potentially produce different outputs when run at different times, even with temperature=0 and unchanged model weights and code.76% similarVerifiedvLLM employs Continuous Batching, refining the scheduling granularity to each generation step, filling slots as soon as they become available.74% similarUnverifiedWhen a node simultaneously performs LLM calls and routing decisions, it leads to repeated evaluation of the same data or multiple LLM calls per step, dragging down token consumption.70% similarUnverifiedVLM (Vision-Language Model)-based parsing may produce drift across different runs (inter-run drift), causing the same file to yield inconsistent outputs on two separate occasions.70% similarUnverifiedProduction-grade LLM applications often require multiple calls to the LLM, parsing of outputs, and passing context between different steps.70% similar
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