Why do distributed TensorFlow training jobs sometimes produce different results with identical code?

Non-determinism arises due to asynchronous updates, floating-point reduction order, and hardware-level execution differences.

Even with identical code, distributed training may vary due to non-deterministic GPU kernels, different gradient aggregation order, and race conditions in asynchronous updates. Floating-point arithmetic is not associative, so different summation orders yield slightly different results that accumulate over time.