What is the max number of variables once can use in an exhaustive all-subsets regression using glmulti()

What is the max number of variables once can use in an exhaustive
all-subsets regression using glmulti()

I am using the glmulti() package in R to try and run an all-subset
regression on some data. I have 51 predictors, all with a maximum of 276
observations. I realize that the exhaustive and genetic algorithm
approaches cannot compute with this many variables as I receive the
following:
Warning message: In glmulti(y = "Tons_N", data = MDatEB1_TonsN, level = 1,
method = "h", : !Too many predictors.
With these types of requirements (i.e. many variables with lots of
observations), how many will I be able to use in a single run of the
all-subsets regression? I am looking into variable elimination techniques
but I would like to use as many variables as possible in this stage of the
analysis. That is, I want to use the results of this analysis to make
variable elimination decisions. Is there another package that can process
more variables at a time?
Here is the code I am using. Unfortunately, because of the confidentiality
associated with the project, I cannot attach datasets.
TonsN_AllSubset <- glmulti(Tons_N ~ ., data = MDatEB1_TonsN, level = 1,
method = "h",crit = "aic", confsetsize = 20, plotty = T, report =
T,fitfunction = "glm")
I am relatively new to this package and modeling in general. Any direction
or advice will be greatly appreciated. Thank you!