Find out the energy cost of launching speculative executions when handling data dependencies to enhance parallelism on multi-GPU platforms. We present CUDAlign 4.0 as case study, a multi-GPU execution for an optimal alignment of huge DNA sequences using the exact Smith-Waterman algorithm. Our speculative approach easily attains 10-20x speed-up versus the baseline pipelined version where GPUs are idle waiting for dependencies to be solved. But working on mispredictions, GPUs waste energy. In the green computing era where GFLOPS/w is the trending metric, we need to know which is worse: wasting time or power. Our experimental study analyzes speculation hit ratios to evaluate extra performance and measures energy spent on mispredictions, to conclude to what extent the speculative approach jeopardizes the GFLOPS/w ratio.