Deep learning (DL) has emerged as a promising alternative for signal detection in multiple-input multiple-output (MIMO) wireless communication systems, achieving a performance close to the optimal maximum likelihood (ML) criterion. However, implementing DL algorithms for MIMO signal detection still demands a high computational cost. Recently, a DL-based architecture with reduced complexity was proposed for MIMO signal detection. More specifically, a preprocessing stage was added to label the input signals, resulting in a more efficient detection scheme. However, even with this reduction in complexity, the proposed detection schemes remain computationally demanding, and this cost grows sharply as the system scales to higher-order modulations and/or large antenna arrays. To further reduce the complexity of these schemes, this paper proposes a graphics processing unit (GPU)-accelerated procedure for DL-based MIMO signal detection. The algorithm is accelerated by using two strategies in a CUDA-enabled PyTorch implementation. First, we optimize the training, which tightens the bit error rate (BER) gap from 1.00 dB to approximately 0.20 dB for the One-Hot (OH) labeling and from 2.00 dB to approximately 0.90 dB for the One-Hot-per-Antenna (OHA) labeling. Additionally, we optimize the Monte Carlo simulation using seven strategies, reducing the end-to-end evaluation time by 34.7% on an NVIDIA RTX 4090 GPU. These results aim to support future projects in evaluating DL-based MIMO systems using shorter computing windows.