The wall clock time for predict.randomForest() is dependent on the number of trees and predictors, as you've specified. However, it's also going to depend on the hardware you're using, as well as any other processing going on at the same time. I'm not sure whether anyone can determine how long it should take in your particular environment. Do you have a threshhold <57ms that you're trying to hit?
One brute force way to speed things up would be to run your predictions in parallel. That is, instead of running 1 prediction 1000 times, run 1000 predictions in parallel just once. Of course that's extreme, so work out how many cores/processors you can spare at once, and chunk up your 1000 predictions across those cores. Be aware that if your dataset is small, the overhead in coordination of the cores/data might offset any savings from the parallelization.
For example, on my 4 core machine, I want one core unused. So I'd use the doParallel parallel backend (I'm running Windows) and the foreach package to register 3 cores and run 333 loops on 2 cores and 334 on the third core. The same process is analagous to if you want to score 1000 observations from your test dataset just once.
A good reference for the outlined suggestion can be found at https://cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf
And similarly, using the doSNOW parallel backend, the author provides runtime benchmarks at http://www.vikparuchuri.com/blog/parallel-r-model-prediction-building/
A somewhat related parallel prediction post from SO has a few other suggestions on dataset splitting that I don't have personal experience with https://stackoverflow.com/questions/28695076/parallel-predict