Can TensorFlow distribute workload to two non-SLI GPUs to gain acceleration?

We know SLI can combine two identical GPU cards into one and get better performance due to parallel computation. But for two GPUs that are not SLI-capable and not identical, can TensorFlow distribute training computation workload among these two GPUs, and then gain some acceleration from training in parallel?