LLM developers and users face challenges with achieving deterministic outputs, even when setting the temperature parameter to 0. Research from Thinking Machines Lab indicates that variations in LLM responses, even at temperature 0, are not due to floating-point inaccuracies but rather to batching strategies on the server side. For API users, controlling batch size is not an option, making true determinism difficult to achieve. Strategies for handling this randomness include accepting variance, using fixed seeds with caution, or employing record-and-replay methods for guaranteed consistency. AI
IMPACT Understanding LLM output variability is crucial for reliable application development and testing.
RANK_REASON Research paper explaining a technical nuance of LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
- Claude Opus-4.6
- Defeating Nondeterminism in LLM Inference
- GPT-5
- GPT-5.1
- GPT-5.2
- GPT-5 mini
- GPT-5 nano
- Horace He
- Richard Feynman
- Thinking Machines Lab
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