The answer shouldn't depend on which GPU you rented (regularjoeceo.substack.com)

Recent tests reveal that the AI model DeepSeek-R1-Distill-Qwen-7B exhibited up to 9% variability in response due to GPU differences, illustrating a critical issue in AI/ML reproducibility. Researchers from Rice University, the University of Minnesota, and Adobe found that running the same model with identical prompts and random seeds on different GPUs could yield divergent outputs. The inconsistencies arise from the nature of floating-point arithmetic, where the order of operations can lead to significant variation, particularly in reasoning tasks that process extensive token sequences. This finding underscores the importance of having portable AI solutions that produce consistent results regardless of hardware. A breakthrough came when the Luxi engine achieved bit-exact computations across nine different GPU types, showcasing the potential for cost-effective hardware choices based on rental rates rather than proprietary results tied to specific models. With this approach, AI developers can ensure reproducibility and reliability, paving the way for more trustworthy AI systems. The project’s public documentation allows others to verify results, fostering transparency in scientific research and enabling auditors to assess AI outputs accurately across varying hardware configurations.