Question

A data science team benchmarks a large language model on two GPUs. On the newer GPU, matrix-multiply-heavy training steps run dramatically faster when the framework enables FP8/mixed-precision math, but element-wise data-preprocessing kernels show almost no speedup. An engineer asks why the same GPU benefits enormously on one workload but not the other. What is the most accurate explanation?

Accepted Answer

Matrix-multiply operations map onto specialized Tensor Cores that perform fused mixed-precision matrix math, while general element-wise kernels run on standard CUDA cores that do not benefit from that dedicated hardware.

Answer

The preprocessing kernels are being executed on the CPU because element-wise operations cannot run on a GPU at all.

Answer

The newer GPU has less memory bandwidth, so only compute-light workloads like matrix multiply can be accelerated.

Answer

Mixed precision only affects data stored in system RAM, so element-wise kernels reading from GPU memory are unaffected by it.