Temperature=0 Doesn't Mean Deterministic. Your Batch Size Does.
📰 Dev.to AI
Learn how batch size affects model output even at temperature=0 and how to identify non-determinism in your model
Action Steps
- Run your model with temperature=0 and varying batch sizes to observe output differences
- Configure your eval suite to account for batch size-induced non-determinism
- Test your model with identical inputs and different batch sizes to identify potential issues
- Apply techniques like memoization or caching to mitigate non-determinism
- Compare model outputs with different batch sizes to detect regressions
Who Needs to Know This
Machine learning engineers and data scientists can benefit from understanding the impact of batch size on model output to improve model reliability and debugging
Key Insight
💡 Batch size can introduce non-determinism in model output even at temperature=0
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🚨 Temperature=0 doesn't guarantee deterministic output! Batch size matters too 🚨
Key Takeaways
Learn how batch size affects model output even at temperature=0 and how to identify non-determinism in your model
Full Article
The problem A team I worked with had an eval suite that called their model at temperature=0 and diffed the output against a golden completion, on the theory that greedy decoding meant identical input in, identical output out — perfect for catching regressions. Most of the time it worked. A few times a week, a completion that had passed for months would suddenly diff as "failed," with no code change, no prompt change, no model version bump. They spent real engineering h
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