AI · 1h ago
Inference Backends Can Skew LLM Outputs, Study Finds
A new paper shows that the choice of inference engine significantly affects LLM reproducibility. Default settings, prefix caching, and numerical precision differences can alter outputs. Fixing the random seed is not enough; the full inference stack must be documented.
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This research underscores that LLM behavior is not just about the model weights, but the entire runtime environment, complicating comparisons and audits.
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Impact of Inference Backends on LLM Reproducibility: Notes from a Research Paper →
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