The Challenge of Reproducible LLM Inference: Why Even Greedy Sampling Isn't Deterministic
By
jxmorris12
Toasted golden, schmeared with insight. Top of the rack.
Summary
The article discusses the challenge of achieving reproducible results in Large Language Model (LLM) inference. Even when using greedy sampling (temperature=0), which should theoretically be deterministic, LLM APIs and inference libraries still produce non-deterministic outputs due to factors like hardware differences, software implementations, and parallel processing. The article explores the technical reasons behind this nondeterminism and potential solutions for making LLM inference truly reproducible.
Key quotes
· 3 pulledReproducibility is a bedrock of scientific progress. However, it's remarkably difficult to get reproducible results out of large language models.
Even when we adjust the temperature down to 0 (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice.
Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn't deterministic.
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