Verified75% confidenceFactExact time
Even when temperature is set to 0, some models still exhibit slight output fluctuations due to floating-point errors in parallel computation.
3
Sources
75%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
如何科学测试提示词对LLM引用行为的影响
redditr/learnmachinelearning7/9/2026
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.81% similarVerifiedEven when Temperature is set to 0 (greedy decoding), the order of parallel floating-point operations on modern GPUs can still lead to minor differences in results.80% similarUnverified加载MTP层后额外浮点运算会导致数值产生微小变化，可能导致模型走不同推理路径产生不同结果，即使温度设为0也会出现79% similarUnverifiedTemperature=0（贪心采样）也不能保证模型输出的确定性，至少有三个原因：浮点运算非结合性、MoE路由不确定性、服务端配置漂移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.76% similar
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