Why do TensorFlow models degrade when deployed across different hardware?

Differences in precision, kernels, and numerical computation cause output variation.

Different hardware (CPU vs GPU vs TPU) may use different floating-point precision (FP32 vs BF16) and optimized kernels. These small differences accumulate, causing prediction drift. TensorFlow abstracts hardware, but numerical equivalence is not guaranteed.