Solver Steps or Solver-Free Retraining? A Same-Board Latency–Energy–Quality Characterization of Flow-Matching Speech Enhancement on a Power-Constrained Embedded GPU

Speech enhancement on edge and wearable devices operates under strict power budgets; generative enhancers buy quality through repeated on-device evaluations. On a Jetson Orin NX, we characterize solver truncation and solver-free retraining for a 65.59 M-parameter flow-matching enhancer. Same-board latency, energy, and quality measurements yield a decision rule comparing two quantities: N*, the smallest step count PESQ-equivalent to the retrained model (±0.05), and Nmax, the largest a mode affords. N* = 4 for the reference on this two-speaker corpus; Nmax (p95) is 5 at the unconstrained mode and 2 at every constrained mode. We keep the solver where affordable; otherwise, under the PESQ criterion, only retraining reaches that quality among the compared recipes. Power modes are not a performance axis; incremental energy varies across budgets (2218 vs. 2022 mJ/s); a 0.048 M reference at MAXN shows 16.3× lower sustained end-to-end RTF at approximately 1% of the energy, 0.208 PESQ lower, with no detectable SI-SDR difference. At one pass, retraining gains 0.173 PESQ but loses 0.75 dB SI-SDR. The procedure is specified, not validated beyond this configuration: re-measure Nmax on the target board and N* after a model, criterion, distribution, or arithmetic change.