Unverified50% confidenceFactExact time
"Temperature 0" is not equivalent to "a unique result": batch scheduling, floating-point nondeterminism, mixture-of-experts routing, quantization error, and provider backend redeployments can all cause the same input to produce different outputs.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
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Related Claims
VerifiedEven when the temperature is set to 0, some inference frameworks still have non-determinism in underlying floating-point computations, causing output differences across hardware and across batches.79% similarUnverifiedEven with temperature set to zero, verdicts still flip between identical replicates; on difficult tasks, swapping the positions of two candidate answers can reverse most verdicts.77% similarVerifiedEven when temperature is set to 0, some models still exhibit slight output fluctuations due to floating-point errors in parallel computation.77% similarVerifiedThe non-determinism of large language models stems from their autoregressive sampling process; even with temperature set to 0, differences in inference frameworks, batch sizes, and hardware floating-point operations can all cause output drift.77% similarUnverifiedAutoregressive generation outputs are affected by sampling strategies (hyperparameters such as temperature and top-p), making it difficult to guarantee completely consistent outputs76% similar
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