Test‑time scaling improves LLM reasoning by generating multiple candidate responses, but the inference budget measured solely by the number of candidates N ignores how those candidates are executed. The same N can be realized in a single batched call or split into several sequential calls with smaller batch sizes, which influences energy consumption and performance. The authors investigate how increasing N and different candidate‑generation strategies affect reasoning quality and resource usage.