Training large Transformers is slow, but recent innovations on GPU architecture gives us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of accelerating feed-forward networks (FFNs) of Transformers in pre-training. First, we define a "flip rate" to monitor the stability of a 2:4 training process. Utilizing this metric, we suggest two techniques to preserve accuracy: to modify the sparse-refined straight-through estimator by applying the mask decay term on gradients, and to enhance the model's quality by a simple yet effective dense fine-tuning procedure near the end of pre-training. Besides, we devise two effective techniques to practically accelerate training: to calculate transposable 2:4 mask by convolution, and to accelerate gated activation functions by reducing GPU L1 cache miss. Experiments show that a combination of our methods reaches the best performance on multiple Transformers among different 2:4 training methods, while actual acceleration can be observed on different shapes of Transformer block.