Take a real, current piece of edge-AI hardware: NVIDIA's Jetson AGX Orin (64GB module).

NVIDIA's own published figure: up to 275 TOPS (trillion operations per second) running INT8, with structured sparsity.

Switch the same chip to FP16 / BF16 and achievable throughput drops — roughly by half, since each floating-point operation occupies more of the tensor core's datapath than an 8-bit integer one.

Same transistors. Same chip. The number format you choose to compute in changes your real-world throughput by a factor of two or more.