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LLM output divergence on different GPUs traced to floating-point arithmetic

By PulseAugur Editorial · [1 sources] ·

LLM outputs can vary even with identical settings and models due to floating-point arithmetic differences across GPUs. These differences arise from the non-associative nature of floating-point addition, where the order of operations can lead to slightly different intermediate results. While most tokens are generated with large confidence margins, a minor difference in the final digit can cause a divergence when the model is at a near-tie, leading to different token predictions and subsequent outputs. A provided script helps identify the exact token where such divergence occurs between two model runs. AI

IMPACT Highlights potential reproducibility issues in LLM outputs, impacting research and deployment.

RANK_REASON Technical explanation and script for reproducibility issues in LLMs due to hardware differences. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM output divergence on different GPUs traced to floating-point arithmetic

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44 / 100
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Technical explanation and script for reproducibility issues in LLMs due to hardware differences. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Dekita ·

    Temperature 0, same model, two different GPUs: find the word that splits them, then pin it

    <p>You set the temperature to 0, you fix the seed, you run the same GGUF file, and the two GPUs disagree. One says 20. The other says 2000. Neither of them is wrong about its own arithmetic, and each one repeats itself exactly when you run it a second time. The difference is not …

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