Step vs. StepAIC

Question

I'm trying to understand why my code has taken several days to process and how I can improve the next iteration. I'm on my third day and continue to have outputs with marginal improvements in AIC. The last couple of AIC's have been 18135.38, 18187.43, and 18243.13. I currently have 33 covariates in the model. The "none" option is 12th from the bottom, so there are still many covariates to run.

The data is ~610K observations with ~1600 variables. Outcome variables and covariates are mostly binary. My covariates were chosen after doing univariate logistical regression and P-value adjustment using Holm procedure (alpha=0.05). No interaction terms are included.

The code I've written is here:

intercept_only <- glm(outcome ~ 1, data=data, family="binomial")
full.model <- glm(outcome ~ 157 covariates, data=data, family = "binomial")
forward_step_model <- step(intercept_only, direction = "forward", scope = formula(full.model))

I'm hoping to run the same code on a different outcome variable with double the number of covariates identified in the same way as above but am worried it will take even longer to process. I see there are both the step and stepAIC functions to perform stepwise regression. Is there an appreciable difference between these functions? Are any other ways of doing this? Is there any way to speed up the processing?